From 449524f2b5a263818fc372c7207b25e116c300d4 Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Thu, 21 May 2026 20:57:45 +0200 Subject: [PATCH 01/44] Adaptive mesh refinement based on target number of nodes --- Manifold_Generation/LUT/LUTGenerators.py | 838 ++++++++++-------- .../NICFD/LUT_Twophase/generate_table.py | 16 +- 2 files changed, 493 insertions(+), 361 deletions(-) diff --git a/Manifold_Generation/LUT/LUTGenerators.py b/Manifold_Generation/LUT/LUTGenerators.py index 33c9095..00e0535 100644 --- a/Manifold_Generation/LUT/LUTGenerators.py +++ b/Manifold_Generation/LUT/LUTGenerators.py @@ -28,9 +28,10 @@ from scipy.spatial import Delaunay from sklearn.preprocessing import MinMaxScaler from tqdm import tqdm +from Common.Interpolators import Invdisttree from Common.DataDrivenConfig import Config_NICFD import gmsh -from concave_hull import concave_hull, concave_hull_indexes +from concave_hull import concave_hull import meshio def shoelace(XY:np.ndarray[float]): @@ -108,20 +109,29 @@ class SU2TableGenerator_NICFD: _base_cell_size:float = 2e-2 # Table level base cell size. - _refined_cell_size:float = 5e-3#2.5e-3#1.5e-3 # Table level refined cell size. - _refinement_radius:float = 1e-2#5e-2 # Table level radius within which refinement is applied. - __refinement_vars:list[str] = [] - __refinement_norm_min:list[float] = [] - __refinement_norm_max:list[float] = [] + __target_node_count:bool=False # Refine grid based on target number of nodes. + __Np_target:int=3000 # Target number of nodes per table level. + _table_vars:list[str] = [s.name for s in EntropicVars][:-1] - _table_nodes = [] # Progress variable, total enthalpy, and mixture fraction node values for each table level. - _table_nodes_norm = [] # Normalized table nodes for each level. + _data_in_table = [] # Progress variable, total enthalpy, and mixture fraction node values for each table level. + _data_in_table_norm = [] # Normalized table nodes for each level. _table_connectivity = [] # Table node connectivity per table level. _table_hullnodes = [] # Hull node indices per table level. - _controlling_variables:list[str]=["Density",\ - "Energy"] # FGM controlling variables + __saturation_curve_table_points:np.ndarray[float] = [] + + _conditional_refinement_vars:list[str] = [] # Thermophysical quantities for which refinement is applied within specified bounds + _conditional_refinement_indices:list[int] = [] # Column indices of refinement quantities + _conditional_lower_bound:list[float] = [] # Lower bounds of conditional refinement quantities. + _conditional_upper_bound:list[float] = [] # Upper bounds of conditional refinement quantities. + _conditional_refinement_factor:list[float] = [] # Refinement factors to apply within the bounds + _fluid_data_scaler:MinMaxScaler= MinMaxScaler() # Scaler for flamelet data controlling variables. + + __initiate_from_pointcloud:bool = False # Fit table bounds to reference data set. + __initial_solution_filename:str = None # File name from which reference data are read. + + _lookup_tree:Invdisttree = None # Inverse-distance KD tree used for evaluating conditional refinement criteria. def __init__(self, Config:Config_NICFD): """ @@ -131,9 +141,14 @@ def __init__(self, Config:Config_NICFD): :type Config: Config_FGM """ self._Config = Config - self._controlling_variables= [c for c in self._Config.GetControllingVariables()] self._DataGenerator = DataGenerator_CoolProp(self._Config) + + self.__setTableOutputVariables() + + return + + def __setTableOutputVariables(self): entropic_vars = [a.name for a in EntropicVars][:-1] self._table_vars = entropic_vars.copy() if not self._Config.TwoPhase(): @@ -141,9 +156,27 @@ def __init__(self, Config:Config_NICFD): if not self._Config.CalcTransportProperties(): self._table_vars.remove(EntropicVars.ViscosityDyn.name) self._table_vars.remove(EntropicVars.Conductivity.name) + return + + def tableBoundsFromPointCloud(self, pointcloud_filename:str): + """Determine the table limits from a point cloud of density-static energy pairs stored in a comma-separated file. + + :param pointcloud_filename: file name from which density-static energy data are read. + :type pointcloud_filename: str + :raises Exception: if the labels for density and static energy cannot be found in the file header. + """ + with open(pointcloud_filename,'r') as fid: + variables_in_header = fid.readline().strip().split(',') + variables_in_header = [v.strip("\"") for v in variables_in_header] + + if EntropicVars.Density.name not in variables_in_header or EntropicVars.Energy.name not in variables_in_header: + raise Exception("Density and static energy not found in point cloud data") + + self.__initial_solution_filename = pointcloud_filename + self.__initiate_from_pointcloud = True return - - def SetFDStepSize(self, val_step_size:float=3e-7): + + def setFDStepSize(self, val_step_size:float=3e-7): """Set the relative step size for density and static energy for evaluating fluid properties in the two-phase region. :param val_step_size: relative finite-difference step size, defaults to 3e-7 @@ -154,8 +187,22 @@ def SetFDStepSize(self, val_step_size:float=3e-7): raise Exception("Relative step size for finite-differences should be positive.") self._DataGenerator.SetFDStepSizes(val_step_size,val_step_size) return + + def setTargetNumberOfNodes(self, N_nodes_target:int=4000): + """Define the total number of points in the thermodynamic table. + + :param N_nodes_target: desired number of nodes per table level, defaults to 4000 + :type N_nodes_target: int, optional + :raises Exception: if non-positive values are provided. + """ + if N_nodes_target <= 0: + raise Exception("Target number of table nodes should be positive") + self.__Np_target = N_nodes_target + self.__target_node_count = True - def SetNpDensity(self, Np_x:int=DefaultSettings_NICFD.Np_p): + return + + def setNpDensity(self, Np_x:int=DefaultSettings_NICFD.Np_p): """Specify the number of table nodes in the x-direction of the Cartesian table. :param Np_x: number of nodes, defaults to DefaultSettings_NICFD.Np_p @@ -164,7 +211,7 @@ def SetNpDensity(self, Np_x:int=DefaultSettings_NICFD.Np_p): self._Config.SetNpDensity(Np_x) return - def SetNpEnergy(self, Np_y:int=DefaultSettings_NICFD.Np_temp): + def setNpEnergy(self, Np_y:int=DefaultSettings_NICFD.Np_temp): """Specify the number of table nodes in the y-direction of the Cartesian table. :param Np_y: number of nodes, defaults to DefaultSettings_NICFD.Np_temp @@ -173,7 +220,7 @@ def SetNpEnergy(self, Np_y:int=DefaultSettings_NICFD.Np_temp): self._Config.SetNpEnergy(Np_y) return - def SetDensityBounds(self, Rho_lower:float=DefaultSettings_NICFD.Rho_min, Rho_upper:float=DefaultSettings_NICFD.Rho_max): + def setTableLimitsDensity(self, Rho_lower:float=DefaultSettings_NICFD.Rho_min, Rho_upper:float=DefaultSettings_NICFD.Rho_max): """Define the density bounds of the density-energy based fluid data grid. :param Rho_lower: lower limit density value, defaults to DefaultSettings_NICFD.Rho_min @@ -186,7 +233,7 @@ def SetDensityBounds(self, Rho_lower:float=DefaultSettings_NICFD.Rho_min, Rho_up self._DataGenerator.SetDensityBounds(Rho_lower, Rho_upper) return - def SetEnergyBounds(self, E_lower:float=DefaultSettings_NICFD.Energy_min, E_upper:float=DefaultSettings_NICFD.Energy_max): + def setTableLimitsEnergy(self, E_lower:float=DefaultSettings_NICFD.Energy_min, E_upper:float=DefaultSettings_NICFD.Energy_max): """Define the internal energy bounds of the density-energy based fluid data grid. :param E_lower: lower limit internal energy value, defaults to DefaultSettings_NICFD.Energy_min @@ -198,7 +245,8 @@ def SetEnergyBounds(self, E_lower:float=DefaultSettings_NICFD.Energy_min, E_uppe self._DataGenerator.UseAutoRange(False) self._DataGenerator.SetEnergyBounds(E_lower, E_upper) return - def SetCellSize_Coarse(self, cell_size_coarse:float=1e-2): + + def setMaximumCellSize(self, cell_size_coarse:float=1e-2): """Specify the coarse level cell size of the table :param cell_size_coarse: coarse cell size, defaults to 1e-2 @@ -209,32 +257,37 @@ def SetCellSize_Coarse(self, cell_size_coarse:float=1e-2): raise Exception("Cell size value should be positive") self._base_cell_size = cell_size_coarse return - - def SetCellSize_Refined(self, cell_size_ref:float=5e-3): - """Specify the refined level cell size of the table - - :param cell_size_ref: refined cell size, defaults to 1e-2 - :type cell_size_ref: float, optional - :raises Exception: if specified cell size is negative or zero - """ - if cell_size_ref <= 0: - raise Exception("Cell size value should be positive") - self._refined_cell_size = cell_size_ref - return - - def SetRefinement_Radius(self, refinement_radius:float=1e-2): - """Specify the radius around each refinement point within which the refined cell size is applied - - :param refinement_radius: refinement radius, defaults to 1e-2 - :type refinement_radius: float, optional - :raises Exception: if specified value is negative or zero + + def addRefinementCriterion(self, varname:str, lowerbound:float=-np.inf, upperbound:float=np.inf, coef:float=0.5): + """Specify conditional refinement based on interpolated thermochemical state data. Cell sizes are reduced by a factor "coef" where the table data lies between the specified lower bound and upper bound (inclusive) + + :param varname: thermochemical state variable name. + :type varname: str + :param lowerbound: lower bound above which refinement is applied, defaults to -np.inf + :type lowerbound: float, optional + :param upperbound: upper bound below which refinement is applied, defaults to np.inf + :type upperbound: float, optional + :param coef: mesh refinement factor, defaults to 0.5 + :type coef: float, optional + :raises Exception: if variable is not in the set of flamelet thermochemical state variables. + :raises Exception: if lower bound value exceeds upper bound value. + :raises Exception: if the coefficient value is negative. """ - if refinement_radius <= 0: - raise Exception("Refinement radius should be positive") - self._refinement_radius = refinement_radius + fluid_vars = [a.name for a in EntropicVars][:-1] + if varname not in fluid_vars: + raise Exception("%s is not in the list of available thermophysical state variables" % varname) + if lowerbound > upperbound: + raise Exception("Upper bound value should exceed lower bound value") + if coef <= 0: + raise Exception("Refinement coeffcient should be positive") + self._conditional_refinement_vars.append(varname) + self._conditional_refinement_indices.append(fluid_vars.index(varname)) + self._conditional_lower_bound.append(lowerbound) + self._conditional_upper_bound.append(upperbound) + self._conditional_refinement_factor.append(coef) return - - def SetTableDiscretization(self, method:str=DefaultSettings_NICFD.tabulation_method): + + def setDiscretizationMethod(self, method:str=DefaultSettings_NICFD.tabulation_method): """Overwrite the thermodynamic state space discretization method from the configuration. :param method: discratization method, defaults to 'cartesian' @@ -243,7 +296,7 @@ def SetTableDiscretization(self, method:str=DefaultSettings_NICFD.tabulation_met self._Config.SetTableDiscretization(method) return - def SetTableVars(self, table_vars_in:list[str]): + def setTableVars(self, table_vars_in:list[str]): """Specify the thermophysical variables to be included in the table file. All quantities are included by default. The list shoud at least contain "Density" and "Energy". :param table_vars_in: list with thermophysical variables to be included in the table. @@ -283,25 +336,167 @@ def SetTableVars(self, table_vars_in:list[str]): raise Exception("Some specified thermophysical variables are not supported.") return - def __Compute2DMesh(self, points:np.ndarray[float], ref_pts:np.ndarray[float]=[],show:bool=False,sat_curve_pts:np.ndarray[float]=[]): - """Populate two-dimensional thermodynamic state space with table nodes according to refinement settings. - - :param points: initial point cloud. - :type points: np.ndarray[float] - :param ref_pts: locations to apply refinement to, defaults to [] - :type ref_pts: np.ndarray[float], optional - :param show: show the discretization generated by Gmesh, defaults to False - :type show: bool, optional - :param sat_curve_pts: saturation curve points, defaults to [] - :type sat_curve_pts: np.ndarray[float], optional - :return: table nodes, table connectivity. - :rtype: tuple + + def generateTable(self): + """Initiate table generation process + """ + + self.__tablePreProcessing() + + # Load initial fluid data and scale it + if self._Config.GetTableDiscretization()=="cartesian": + self.__CartesianTriangulation() + else: + self.__unstructuredTriangulation() + return + + def __tablePreProcessing(self): + self.__generateCoarseTable() + + self._fluid_data_scaler = MinMaxScaler() + self._fluid_data_scaler.fit(self._data_in_table) + + self.__prepareInterpolator() + return + + + def __CartesianTriangulation(self): + """ + Create Delaunay triangulation of valid grid points. """ - # Create concave hull of normalized table coordinates. - add_sat_curve = (len(sat_curve_pts) > 0) - XY_hull = concave_hull(np.unique(points,axis=0), length_threshold=self._base_cell_size) - nans = np.isnan(XY_hull).all(1) - XY_hull = XY_hull[np.invert(nans), :] + print("Creating Delaunay triangulation...") + + # Extract valid points + rho_table = self.state_data[:,:,EntropicVars.Density.value] + e_table = self.state_data[:,:,EntropicVars.Energy.value] + rho_valid = rho_table[self.valid_mask].flatten() + e_valid = e_table[self.valid_mask].flatten() + + # Stack as (N, 2) array + cv_table = np.column_stack([rho_valid, e_valid]) + + #self._data_in_table = np.column_stack(tuple(self.state_data[:,:,EntropicVars[v].value][self.valid_mask].flatten() for v in self._table_vars)) + self._data_in_table = np.column_stack(tuple(self.state_data[:,:,i][self.valid_mask].flatten() for i in range(EntropicVars.N_STATE_VARS.value))) + + # Create Delaunay triangulation + tri = Delaunay(cv_table) + self._table_connectivity = tri.simplices + + # Identify hull nodes + edges = np.vstack([tri.simplices[:, [0, 1]], + tri.simplices[:, [1, 2]], + tri.simplices[:, [2, 0]]]) + edges = np.sort(edges, axis=1) + unique_edges, counts = np.unique(edges, axis=0, return_counts=True) + boundary_edges = unique_edges[counts == 1] + self._table_hullnodes= np.unique(boundary_edges.flatten()) + + print(f" Triangulation nodes: {len(self._data_in_table):,}") + print(f" Triangles: {len(self._table_connectivity):,}") + print(f" Hull nodes: {len(self._table_hullnodes):,}") + print() + return + + def __unstructuredTriangulation(self): + print("Generating unstructured table with adaptive refinement...") + + if self._Config.TwoPhase(): + self.__saturation_curve_table_points = self.__CreateSaturationCurve() + + table_fluid_data, tria, hullTags = self.__computeMeshAndTableData() + + self._data_in_table = table_fluid_data + self._table_connectivity = tria + self._table_hullnodes = hullTags + return + + def __computeMeshAndTableData(self): + + scaled_fluid_data = self._fluid_data_scaler.transform(self._data_in_table) + rhoe_norm = scaled_fluid_data[:, [EntropicVars.Density.value, EntropicVars.Energy.value]] + + rhoe_norm_mesh_nodes,tria, hullTags = self.__discretize2DTable(rhoe_norm) + + # Calculate thermodynamic state variables of initial table nodes + fluid_data_norm = np.zeros([len(rhoe_norm_mesh_nodes), EntropicVars.N_STATE_VARS.value]) + fluid_data_norm[:, EntropicVars.Density.value] = rhoe_norm_mesh_nodes[:,0] + fluid_data_norm[:, EntropicVars.Energy.value] = rhoe_norm_mesh_nodes[:,1] + fluid_data_mesh = self._fluid_data_scaler.inverse_transform(fluid_data_norm) + fluid_data_mesh = self.__evaluateFluidPropertiesOnMesh(fluid_data_mesh) + return fluid_data_mesh, tria, hullTags + + + def __discretize2DTable(self, rhoe_init:np.ndarray[float]): + + # Discretize thermochemical state space with target node count or user-defined cell sizes. + if self.__target_node_count: + nodes, tris, hulltags = self.__optimizeTableNodes(rhoe_init) + else: + nodes, tris, hulltags = self.__create2DMesh(rhoe_init) + + return nodes, tris, hulltags + + def __optimizeTableNodes(self, rhoe_init:np.ndarray[float]): + print("Refining table based on target node count") + print("Target node count: %i" % self.__Np_target) + + # Iterate base cell size to reach within 1% of target number of table nodes. + sufficient_refinement = False + niter_max = 20 + relaxation = 0.35 + + # Initial guess + self._base_cell_size = 2 * np.sqrt(1.0 / self.__Np_target) + print("| Iteration | Node count | Base cell size | Diff from target(%) |") + iter = 0 + while not sufficient_refinement and iter < niter_max: + nodes, tris, hulltags = self.__create2DMesh(rhoe_init) + n_nodes = len(nodes) + rel_diff = abs(float(n_nodes - self.__Np_target)/self.__Np_target) + print("| %i | %i | %.3e | %.1f |" % (iter, n_nodes, self._base_cell_size, 100*rel_diff)) + # Terminate if relative difference is less than 1% + if rel_diff > 0.01: + self._base_cell_size *= (1 + relaxation * (float(n_nodes / self.__Np_target) - 1)) + else: + sufficient_refinement = True + iter += 1 + + if iter == niter_max: + print("Target node count was not reached within %i iterations" % niter_max) + return nodes, tris, hulltags + + def __create2DMesh(self, rhoe_init:np.ndarray[float]): + XY_hull = self.__findHullNodes(rhoe_init) + + gmsh.initialize() + gmsh.model.add("table_level") + gmsh.option.setNumber("General.Verbosity", 0) + gmsh.option.setNumber("Mesh.MeshSizeFromPoints",0) + gmsh.option.setNumber("Mesh.MeshSizeExtendFromBoundary",0) + + factory = gmsh.model.geo + mesher = gmsh.model.mesh + + hull_curvloop, hull_lines = self.__createHullCurvLoop(XY_hull, factory) + + self.__createFluidPlane(XY_hull, hull_curvloop, factory) + + self.__prepareMesher(mesher) + + mesher.generate(2) + + nodeTags, triaTags = self.__extractNodesTriangles() + hullTags = self.__extractHulltags(hull_lines) + + gmsh.finalize() + + return nodeTags, triaTags, hullTags + + def __findHullNodes(self, initial_point_cloud:np.ndarray[float]): + unique_pts_init = np.unique(initial_point_cloud, axis=0) + nans = np.isnan(unique_pts_init).all(1) + pts_wo_nans = unique_pts_init[np.invert(nans)] + XY_hull = concave_hull(pts_wo_nans, length_threshold=self._base_cell_size) i = 0 hull_indices = [i] @@ -317,17 +512,13 @@ def __Compute2DMesh(self, points:np.ndarray[float], ref_pts:np.ndarray[float]=[] i = i_next hull_indices.append(i_next) XY_hull = XY_hull[hull_indices, :] - - # Initiate gmsh - gmsh.initialize() - gmsh.model.add("table_level") - gmsh.option.setNumber("General.Verbosity", 0) - factory = gmsh.model.geo - + return XY_hull + + def __createHullCurvLoop(self, XY_hull:np.ndarray[float], factory:gmsh.model.geo): # Create hull points hull_pts = [] for i in range(int(len(XY_hull))): - hull_pts.append(factory.addPoint(XY_hull[i, 0], XY_hull[i, 1], 0, self._base_cell_size)) + hull_pts.append(factory.addPoint(XY_hull[i, 0], XY_hull[i, 1], 0)) # Connect hull points to a closed multi-component curve hull_lines = [] @@ -339,150 +530,162 @@ def __Compute2DMesh(self, points:np.ndarray[float], ref_pts:np.ndarray[float]=[] curvloop = factory.addCurveLoop(hull_lines) # Apply refinement points - area_ref = shoelace(concave_hull(XY_hull, length_threshold=self._base_cell_size)) - ref_pt_ids = [] - if len(ref_pts)>0: - for i in range(len(ref_pts)): - XY_with_pt = np.vstack((XY_hull, ref_pts[i,:])) - hull_n = concave_hull(XY_with_pt, length_threshold=self._base_cell_size) - area_n = shoelace(hull_n) - within_hull = (area_n <= area_ref) - if within_hull: - ref_pt_ids.append(factory.addPoint(ref_pts[i,0], ref_pts[i, 1], 0.0)) + self.__table_hull_area = shoelace(concave_hull(XY_hull, length_threshold=self._base_cell_size)) factory.synchronize() - if add_sat_curve: - - # Create normal vector to saturation curve. - dedrho_sat_norm = FiniteDifferenceDerivative(sat_curve_pts[:,0], sat_curve_pts[:,1]) - norm_vector = np.column_stack((-1.0 / dedrho_sat_norm, np.ones(len(dedrho_sat_norm)))) - norm_vector = norm_vector / np.sqrt(np.sum(np.power(norm_vector, 2), axis=1))[:,np.newaxis] - - # Create offset curves to ensure that no nodes are generated on the saturation curve itself. - sat_curve_upper_pts = [] - sat_curve_lower_pts = [] - dx = 1e-3*self._refined_cell_size - - sat_curve_rhoe_upper = sat_curve_pts + dx * norm_vector - sat_curve_rhoe_lower = sat_curve_pts - dx * norm_vector - inside_upper = np.logical_and(sat_curve_rhoe_upper > 0, sat_curve_rhoe_upper < 1).all(1) - inside_lower = np.logical_and(sat_curve_rhoe_lower > 0, sat_curve_rhoe_lower< 1).all(1) - valid_sat_curve_pts = np.logical_and(inside_upper, inside_lower) - nans_lower = np.isnan(sat_curve_rhoe_lower).all(1) - nans_upper = np.isnan(sat_curve_rhoe_upper).all(1) - valid_nans = np.logical_and(np.invert(nans_lower), np.invert(nans_upper)) - valid_pts = np.logical_and(valid_sat_curve_pts, valid_nans) - - # Clip the saturation curve points to the bounds of the thermodynamic mesh - - within_hull = np.zeros(len(sat_curve_pts),dtype=np.bool) - for i in range(len(sat_curve_pts)): - XY_with_pt = np.vstack((XY_hull, sat_curve_rhoe_upper[i,:])) - hull_n = concave_hull(XY_with_pt, length_threshold=self._base_cell_size) - area_n = shoelace(hull_n) - within_hull_upper = (area_n <= area_ref) - XY_with_pt = np.vstack((XY_hull, sat_curve_rhoe_lower[i,:])) - hull_n = concave_hull(XY_with_pt, length_threshold=self._base_cell_size) - area_n = shoelace(hull_n) - within_hull_lower = (area_n <= area_ref) - within_hull[i] = (within_hull_upper and within_hull_lower) - valid_pts = np.logical_and(valid_pts, within_hull) - - sat_curve_pts = sat_curve_pts[valid_pts, :] - norm_vector = norm_vector[valid_pts, :] - - i = 0 - j = 1 - sat_curve_upper_pts.append(factory.addPoint(sat_curve_pts[i,0] + dx*norm_vector[i, 0],\ - sat_curve_pts[i,1] + dx*norm_vector[i, 1],0)) - sat_curve_lower_pts.append(factory.addPoint(sat_curve_pts[i,0] - dx*norm_vector[i, 0],\ - sat_curve_pts[i,1] - dx*norm_vector[i, 1],0)) - dists = [] - while j < len(sat_curve_pts): - dist = np.sqrt(np.sum(np.power(sat_curve_pts[j,:] - sat_curve_pts[i,:],2))) - if dist < 0.6*self._refined_cell_size: - j += 1 - else: - i = j - j += 1 - dists.append(dist) - sat_curve_upper_pts.append(factory.addPoint(sat_curve_pts[i,0] + dx*norm_vector[i, 0],\ - sat_curve_pts[i,1] + dx*norm_vector[i, 1],0)) - sat_curve_lower_pts.append(factory.addPoint(sat_curve_pts[i,0] - dx*norm_vector[i, 0],\ - sat_curve_pts[i,1] - dx*norm_vector[i, 1],0)) - sat_curve_connecting_lines = [] - for i in range(len(sat_curve_upper_pts)): - sat_curve_connecting_lines.append(factory.addLine(sat_curve_lower_pts[i], sat_curve_upper_pts[i])) - sat_curve_upper_lines = [] - sat_curve_lower_lines = [] - - sat_curve_crvloops = [] - sat_curve_cornertags = [] - for i in range(len(sat_curve_lower_pts)-1): - if dists[i] < self._base_cell_size: - c_upper=factory.addLine(sat_curve_upper_pts[i],sat_curve_upper_pts[i+1]) - c_lower=factory.addLine(sat_curve_lower_pts[i],sat_curve_lower_pts[i+1]) - sat_curve_upper_lines.append(c_upper) - sat_curve_lower_lines.append(c_lower) - sat_curve_crvloops.append(factory.addCurveLoop([c_upper, -sat_curve_connecting_lines[i+1], -c_lower, sat_curve_connecting_lines[i]])) - sat_curve_cornertags.append([sat_curve_lower_pts[i], sat_curve_lower_pts[i+1], sat_curve_upper_pts[i+1], sat_curve_upper_pts[i]]) - factory.synchronize() - fluid_plane_crvloops = [curvloop] + [-c for c in sat_curve_crvloops] - fluid_surf = factory.addPlaneSurface(fluid_plane_crvloops) - sat_surfs = [factory.addPlaneSurface([c]) for c in sat_curve_crvloops] - factory.addPhysicalGroup(2, [fluid_surf] + sat_surfs) - else: - factory.synchronize() - fluid_surf = factory.addPlaneSurface([curvloop]) - factory.addPhysicalGroup(2, [fluid_surf]) - # Apply conditional refinement, where the refined cell size is applied in proximity to the refinement points - factory.synchronize() - threshold_fields = [] - dist_field_ref_pt = gmsh.model.mesh.field.add("Attractor") - gmsh.model.mesh.field.setNumbers(dist_field_ref_pt, "NodesList", ref_pt_ids) - t_field_ref_pt = gmsh.model.mesh.field.add("Threshold") - gmsh.model.mesh.field.setNumber(t_field_ref_pt, "InField", dist_field_ref_pt) - gmsh.model.mesh.field.setNumber(t_field_ref_pt, "SizeMin", self._refined_cell_size) - gmsh.model.mesh.field.setNumber(t_field_ref_pt, "SizeMax", self._base_cell_size) - gmsh.model.mesh.field.setNumber(t_field_ref_pt, "DistMin", 0.5*self._refinement_radius) - gmsh.model.mesh.field.setNumber(t_field_ref_pt, "DistMax", 1.5*self._refinement_radius) - threshold_fields.append(t_field_ref_pt) + return curvloop, hull_lines + + def __createFluidPlane(self, rhoe_hull:np.ndarray[float], hull_crvloop:int, factory:gmsh.model.geo): + add_sat_curve = len(self.__saturation_curve_table_points) > 0 if add_sat_curve: - dist_field_sat_crv = gmsh.model.mesh.field.add("Distance") - gmsh.model.mesh.field.setNumbers(dist_field_sat_crv, \ - "PointsList", sat_curve_upper_pts + sat_curve_lower_pts) - gmsh.model.mesh.field.setNumber(dist_field_sat_crv, "Sampling", 10) - t_field_sat_crv = gmsh.model.mesh.field.add("Threshold") - gmsh.model.mesh.field.setNumber(t_field_sat_crv, "InField", dist_field_sat_crv) - gmsh.model.mesh.field.setNumber(t_field_sat_crv, "SizeMin", self._refined_cell_size) - gmsh.model.mesh.field.setNumber(t_field_sat_crv, "SizeMax", self._base_cell_size) - gmsh.model.mesh.field.setNumber(t_field_sat_crv, "DistMin", 0.5*self._refinement_radius) - gmsh.model.mesh.field.setNumber(t_field_sat_crv, "DistMax", 1.5*self._refinement_radius) - threshold_fields.append(t_field_sat_crv) - - back_field = gmsh.model.mesh.field.add("Min") - gmsh.model.mesh.field.setNumbers(back_field, "FieldsList", threshold_fields) - gmsh.model.mesh.field.setAsBackgroundMesh(back_field) + sat_curve_crvloops = self.__createSaturationCurveGeom(rhoe_hull, factory) + else: + sat_curve_crvloops = [] + fluid_plane_crvloops = [hull_crvloop] + [-c for c in sat_curve_crvloops] + fluid_surf = factory.addPlaneSurface(fluid_plane_crvloops) + sat_surfs = [factory.addPlaneSurface([c]) for c in sat_curve_crvloops] + factory.addPhysicalGroup(2, [fluid_surf] + sat_surfs) factory.synchronize() - if add_sat_curve: - for i in range(len(sat_curve_connecting_lines)): - gmsh.model.mesh.setTransfiniteCurve(sat_curve_connecting_lines[i], 2) - for i in range(len(sat_curve_lower_lines)): - gmsh.model.mesh.setTransfiniteCurve(sat_curve_lower_lines[i], 2) - gmsh.model.mesh.setTransfiniteCurve(sat_curve_upper_lines[i], 2) - - for s, c in zip(sat_surfs, sat_curve_cornertags): - gmsh.model.mesh.setTransfiniteSurface(s, cornerTags=c) - gmsh.model.mesh.setRecombine(2, s) - # Generate 2D mesh and extract table nodes - gmsh.model.mesh.generate(2) - - if show: - gmsh.fltk.run() - # Global nodes + return fluid_surf + + + def __createSaturationCurveGeom(self, rhoe_hull:np.ndarray[float], factory:gmsh.model.geo): + + sat_curve_pts, sat_curve_lower, sat_curve_upper = self.__saturationOffsetCurves(rhoe_hull) + + sat_curve_lower_pts, sat_curve_upper_pts = self.__samplePointsAlongSaturationCurve(sat_curve_pts, sat_curve_lower, sat_curve_upper, factory) + + return self.__encloseSaturationCurve(sat_curve_lower_pts, sat_curve_upper_pts, factory) + + def __encloseSaturationCurve(self, lower_offset_pts:np.ndarray[int], upper_offset_pts:np.ndarray[int], factory:gmsh.model.geo): + connecting_lines = [] + Npoints = len(lower_offset_pts) + for i in range(Npoints): + connecting_lines.append(factory.addLine(lower_offset_pts[i], upper_offset_pts[i])) + + sat_curve_crvloops = [] + for i in range(Npoints-1): + tangent_line_upper=factory.addLine(upper_offset_pts[i],upper_offset_pts[i+1]) + tangent_line_lower=factory.addLine(lower_offset_pts[i],lower_offset_pts[i+1]) + + sat_curve_crvloops.append(factory.addCurveLoop([tangent_line_upper, connecting_lines[i+1], tangent_line_lower, connecting_lines[i]], reorient=True)) + factory.synchronize() + return sat_curve_crvloops + + def __samplePointsAlongSaturationCurve(self, saturation_curve_points:np.ndarray[float], offset_lower:np.ndarray[float], offset_upper:np.ndarray[float], factory:gmsh.model.geo): + i = 0 + j = 1 + sat_curve_upper_pts = [] + sat_curve_lower_pts = [] + sat_curve_upper_pts.append(factory.addPoint(offset_upper[0,0], offset_upper[0,1],0)) + sat_curve_lower_pts.append(factory.addPoint(offset_lower[0,0], offset_lower[0,1],0)) + dists = [] + while j < len(saturation_curve_points): + dist = np.sqrt(np.sum(np.power(saturation_curve_points[j] - saturation_curve_points[i],2))) + local_refinement_factor = self.__refinelocation(saturation_curve_points[j, 0], saturation_curve_points[j, 1], 0) + if dist < 0.5*local_refinement_factor*self._base_cell_size: + j += 1 + else: + i = j + j += 1 + dists.append(dist) + sat_curve_upper_pts.append(factory.addPoint(offset_upper[i, 0], offset_upper[i,1],0)) + sat_curve_lower_pts.append(factory.addPoint(offset_lower[i, 0], offset_lower[i,1],0)) + return sat_curve_lower_pts, sat_curve_upper_pts + + def __saturationOffsetCurves(self, rhoe_hull:np.ndarray[float]): + + norm_vector = self.__getSaturationCurveNormal() + + # Create offset curves to ensure that no nodes are generated on the saturation curve itself. + sat_curve_rhoe_lower, sat_curve_rhoe_upper = self.__createSaturationCurveOffsets(norm_vector) + + valid_pts = self.__clipCurveToTableLimits(rhoe_hull, sat_curve_rhoe_lower, sat_curve_rhoe_upper) + + sat_curve_pts = self.__saturation_curve_table_points[valid_pts, :] + norm_vector = norm_vector[valid_pts, :] + sat_curve_rhoe_lower = sat_curve_rhoe_lower[valid_pts] + sat_curve_rhoe_upper = sat_curve_rhoe_upper[valid_pts] + + return sat_curve_pts, sat_curve_rhoe_lower, sat_curve_rhoe_upper + + def __getSaturationCurveNormal(self): + dedrho_sat_norm = FiniteDifferenceDerivative(self.__saturation_curve_table_points[:,0], self.__saturation_curve_table_points[:,1]) + norm_vector = np.column_stack((-1.0 / dedrho_sat_norm, np.ones(len(dedrho_sat_norm)))) + norm_vector = norm_vector / np.sqrt(np.sum(np.power(norm_vector, 2), axis=1))[:,np.newaxis] + return norm_vector + + def __createSaturationCurveOffsets(self, normal_vector:np.ndarray[float]): + offset = 1e-4*self._base_cell_size + + sat_curve_rhoe_upper = self.__saturation_curve_table_points + offset * normal_vector + sat_curve_rhoe_lower = self.__saturation_curve_table_points - offset * normal_vector + + return sat_curve_rhoe_lower, sat_curve_rhoe_upper + + + def __clipCurveToTableLimits(self, rhoe_hull:np.ndarray[float], pts_offset_lower:np.ndarray[float], pts_offset_upper:np.ndarray[float]): + + within_bounds_lower = np.logical_and(pts_offset_lower > 0, pts_offset_lower < 1).all(1) + within_bounds_upper = np.logical_and(pts_offset_upper > 0, pts_offset_upper< 1).all(1) + valid_sat_curve_pts = np.logical_and(within_bounds_lower, within_bounds_upper) + + nans_lower = np.isnan(pts_offset_lower).all(1) + nans_upper = np.isnan(pts_offset_upper).all(1) + + valid_nans = np.logical_and(np.invert(nans_lower), np.invert(nans_upper)) + valid_pts = np.logical_and(valid_sat_curve_pts, valid_nans) + + within_hull = np.zeros(len(self.__saturation_curve_table_points),dtype=np.bool) + for i in range(len(self.__saturation_curve_table_points)): + XY_with_pt = np.vstack((rhoe_hull, pts_offset_upper[i,:])) + hull_n = concave_hull(XY_with_pt, length_threshold=self._base_cell_size) + area_n = shoelace(hull_n) + within_hull_upper = (area_n <= self.__table_hull_area ) + XY_with_pt = np.vstack((rhoe_hull, pts_offset_lower[i,:])) + hull_n = concave_hull(XY_with_pt, length_threshold=self._base_cell_size) + area_n = shoelace(hull_n) + within_hull_lower = (area_n <= self.__table_hull_area ) + within_hull[i] = (within_hull_upper and within_hull_lower) + valid_pts = np.logical_and(valid_pts, within_hull) + + return valid_pts + + def __prepareMesher(self, mesher:gmsh.model.mesh): + mesher.clear() + gmsh.option.setNumber("Mesh.MeshSizeMax", self._base_cell_size) + def meshSizeCallback(dim,tag,x,y,z,lc): + fac = self.__refinelocation(x, y, z) + return self._base_cell_size * fac + + mesher.setSizeCallback(meshSizeCallback) + return + + def __refinelocation(self, x:float,y:float,z:float): + state_fluid_norm = np.zeros([1, EntropicVars.N_STATE_VARS.value]) + state_fluid_norm[0, EntropicVars.Density.value] = x + state_fluid_norm[0, EntropicVars.Energy.value] = y + state_fluid_dimensional = self._fluid_data_scaler.inverse_transform(state_fluid_norm) + val_density = state_fluid_dimensional[0, EntropicVars.Density.value] + val_static_energy = state_fluid_dimensional[0, EntropicVars.Energy.value] + + refinement_factor = 1.0 + + if len(self._conditional_refinement_indices) > 0: + fluid_state_data = self.__interpolateFluidData(val_density, val_static_energy) + valid_pt = len(fluid_state_data) > 0 + + if valid_pt: + for ivar, lower, upper, c in zip(self._conditional_refinement_indices, self._conditional_lower_bound, self._conditional_upper_bound, self._conditional_refinement_factor): + test_val = fluid_state_data[ivar] + if (test_val >= lower) and (test_val <= upper): + refinement_factor = min(refinement_factor, c) + return refinement_factor + + def __extractNodesTriangles(self): nodeTags, coords, _ = gmsh.model.mesh.getNodes() nodeTags = np.asarray(nodeTags, dtype=np.int64) MeshPoints = np.asarray(coords, dtype=float).reshape(-1, 3)[:, :2] @@ -491,11 +694,8 @@ def __Compute2DMesh(self, points:np.ndarray[float], ref_pts:np.ndarray[float]=[] nodeTags_sorted = nodeTags[order] # 2) 2D elements - if fluid_surf is None: - elemTypes, _, elemNodeTags = gmsh.model.mesh.getElements(2) - else: - elemTypes, _, elemNodeTags = gmsh.model.mesh.getElements(2, fluid_surf) - + elemTypes, _, elemNodeTags = gmsh.model.mesh.getElements(2) + tris = [] quads = [] @@ -517,9 +717,20 @@ def __Compute2DMesh(self, points:np.ndarray[float], ref_pts:np.ndarray[float]=[] quads[:, [0, 1, 2]], quads[:, [0, 2, 3]], ]) - gmsh.finalize() return MeshPoints, tris + + def __extractHulltags(self, hull_curves:list[int]): + allHullTags = [] + for line in hull_curves: + nodeTags, nodes, _ = gmsh.model.mesh.getNodes(includeBoundary=True,tag=line,dim=1) + nodes = np.asarray(nodes, dtype=float).reshape(-1, 3) + nodeTags = np.asarray(nodeTags, dtype=np.int64) + + allHullTags = np.append(allHullTags, nodeTags) + hullTags = np.unique(np.int64(allHullTags)) + return hullTags + def __map_tags(self,tags,nodeTags_sorted,order): tags = np.asarray(tags, dtype=np.int64).ravel() @@ -530,7 +741,7 @@ def __map_tags(self,tags,nodeTags_sorted,order): raise RuntimeError(f"Node tags non trovati in getNodes(): {missing[:20]} (tot missing={len(missing)})") return order[pos] - def __CalcMeshData(self, fluid_data_mesh:np.ndarray[float]): + def __evaluateFluidPropertiesOnMesh(self, fluid_data_mesh:np.ndarray[float]): """Calculate the fluid thermodynamic state variables for the table nodes :param fluid_data_mesh: table mesh nodes of density and static energy @@ -610,47 +821,11 @@ def __CartesianTableData(self): self.state_data[idx_2d[0], idx_2d[1], :] = None return - def __CartesianTriangulation(self): - """ - Create Delaunay triangulation of valid grid points. - """ - print("Creating Delaunay triangulation...") - - # Extract valid points - rho_table = self.state_data[:,:,EntropicVars.Density.value] - e_table = self.state_data[:,:,EntropicVars.Energy.value] - rho_valid = rho_table[self.valid_mask].flatten() - e_valid = e_table[self.valid_mask].flatten() - - # Stack as (N, 2) array - cv_table = np.column_stack([rho_valid, e_valid]) - - #self._table_nodes = np.column_stack(tuple(self.state_data[:,:,EntropicVars[v].value][self.valid_mask].flatten() for v in self._table_vars)) - self._table_nodes = np.column_stack(tuple(self.state_data[:,:,i][self.valid_mask].flatten() for i in range(EntropicVars.N_STATE_VARS.value))) - - # Create Delaunay triangulation - tri = Delaunay(cv_table) - self._table_connectivity = tri.simplices - - # Identify hull nodes - edges = np.vstack([tri.simplices[:, [0, 1]], - tri.simplices[:, [1, 2]], - tri.simplices[:, [2, 0]]]) - edges = np.sort(edges, axis=1) - unique_edges, counts = np.unique(edges, axis=0, return_counts=True) - boundary_edges = unique_edges[counts == 1] - self._table_hullnodes= np.unique(boundary_edges.flatten()) - - print(f" Triangulation nodes: {len(self._table_nodes):,}") - print(f" Triangles: {len(self._table_connectivity):,}") - print(f" Hull nodes: {len(self._table_hullnodes):,}") - print() - return def __CreateSaturationCurve(self): rhoe_sat_curve = self._DataGenerator.ComputeSaturationCurve() - rho_min, rho_max = self._DataGenerator.GetDensityBounds() - e_min, e_max = self._DataGenerator.GetEnergyBounds() + [rho_min, rho_max] = self._Config.GetDensityBounds() + [e_min, e_max] = self._Config.GetEnergyBounds() within_bounds_density = np.logical_and(rhoe_sat_curve[:,0] > rho_min, rhoe_sat_curve[:,0] < rho_max) within_bounds_energy = np.logical_and(rhoe_sat_curve[:,1] > e_min, rhoe_sat_curve[:,1] < e_max) within_bounds = np.logical_and(within_bounds_density, within_bounds_energy) @@ -664,86 +839,43 @@ def __CreateSaturationCurve(self): sat_curve_pts_norm = state_sat_curve_norm[:, [EntropicVars.Density.value,EntropicVars.Energy.value]] return sat_curve_pts_norm - def __GenerateMeshAndData(self, rhoe_norm:np.ndarray[float], sat_curve_pts:np.ndarray[float],ix_ref=[]): - - rhoe_norm_mesh_nodes,tria = self.__Compute2DMesh(rhoe_norm, ref_pts=rhoe_norm[ix_ref,:], show=False, sat_curve_pts=sat_curve_pts) - - # Calculate thermodynamic state variables of initial table nodes - fluid_data_norm = np.zeros([len(rhoe_norm_mesh_nodes), EntropicVars.N_STATE_VARS.value]) - fluid_data_norm[:, EntropicVars.Density.value] = rhoe_norm_mesh_nodes[:,0] - fluid_data_norm[:, EntropicVars.Energy.value] = rhoe_norm_mesh_nodes[:,1] - fluid_data_mesh = self._fluid_data_scaler.inverse_transform(fluid_data_norm) - fluid_data_mesh = self.__CalcMeshData(fluid_data_mesh) - return fluid_data_mesh, tria - - def GenerateTable(self): - """Initiate table generation process - """ - + + + def __generateCoarseTable(self): + if self.__initiate_from_pointcloud: + with open(self.__initial_solution_filename,'r') as fid: + variables_in_header = fid.readline().strip().split(',') + variables_in_header = [v.strip("\"") for v in variables_in_header] + pointCloudData = np.loadtxt(self.__initial_solution_filename,delimiter=',',skiprows=1)[::10, :] + rhoe_pointCloud = pointCloudData[:, [variables_in_header.index(EntropicVars.Density.name),\ + variables_in_header.index(EntropicVars.Energy.name)]] + rhoe_min, rhoe_max = np.min(rhoe_pointCloud, axis=0), np.max(rhoe_pointCloud,axis=0) + rho_min = rhoe_min[0]*0.9 + rho_max = 1.1*rhoe_max[0] + e_min = rhoe_min[1]*0.99 + e_max = 1.01*rhoe_max[1] + + self._Config.SetDensityBounds(rho_min, rho_max) + self._Config.SetEnergyBounds(e_min, e_max) + self.__CartesianTableData() - self._table_nodes = np.column_stack(tuple(self.state_data[:,:,i][self.valid_mask].flatten() for i in range(EntropicVars.N_STATE_VARS.value))) - self._fluid_data_scaler = MinMaxScaler() - self._fluid_data_scaler.fit(self._table_nodes) - - # Load initial fluid data and scale it - if self._Config.GetTableDiscretization()=="cartesian": - self.__CartesianTriangulation() - else: - print("Generating table with adaptive refinement") - - self._table_nodes = np.column_stack(tuple(self.state_data[:,:,i][self.valid_mask].flatten() for i in range(EntropicVars.N_STATE_VARS.value))) - - fluid_data_norm = self._fluid_data_scaler.fit_transform(self._table_nodes) - - sat_curve_pts_norm = [] - if self._Config.TwoPhase(): - sat_curve_pts_norm = self.__CreateSaturationCurve() - - # Generate initial coarse table of fluid data - rhoe_norm = fluid_data_norm[:, [EntropicVars.Density.value, EntropicVars.Energy.value]] - print("Generating coarse thermodynamic mesh...") - fluid_data_coarse, _ = self.__GenerateMeshAndData(rhoe_norm, sat_curve_pts_norm) - print("Done!") - # Identify refinement locations - fluid_data_norm = self._fluid_data_scaler.transform(fluid_data_coarse) - ix_ref = self.__ApplyRefinement(fluid_data_norm) - - # # Regenerate table including refinement locations - rhoe_norm_mesh = fluid_data_norm[:, [EntropicVars.Density.value, EntropicVars.Energy.value]] - - # Create triangulation of filtered thermodynamic state data - print("Generating refined thermodynamic mesh...") - fluid_data_ref, _ = self.__GenerateMeshAndData(rhoe_norm_mesh, sat_curve_pts_norm, ix_ref=ix_ref) - print("Done!") - fluid_data_norm_ref = self._fluid_data_scaler.transform(fluid_data_ref) - - DT = Delaunay(fluid_data_norm_ref[:, [EntropicVars.Density.value,EntropicVars.Energy.value]]) - - # Extract triangulation, hull nodes, and table data - Tria = DT.simplices - HullNodes = concave_hull_indexes(fluid_data_norm_ref[:, [EntropicVars.Density.value,EntropicVars.Energy.value]]) - #plt.triplot(DT.points[:,0],DT.points[:,1], Tria) - #plt.show() - self._table_nodes = fluid_data_ref - self._table_connectivity = Tria - self._table_hullnodes = HullNodes - + self._data_in_table = np.column_stack(tuple(self.state_data[:,:,i][self.valid_mask].flatten() for i in range(EntropicVars.N_STATE_VARS.value))) return - - def __remove_invalid_nodes_from_mesh(self, connectivity, valid_mask, rhoe_mesh_norm): - - conn = np.asarray(connectivity, dtype=np.int64) - - tri_keep = np.all(valid_mask[conn], axis=1) - conn2 = conn[tri_keep] - - keep_nodes = np.flatnonzero(valid_mask) - old_to_new = -np.ones(len(rhoe_mesh_norm), dtype=np.int64) - old_to_new[keep_nodes] = np.arange(len(keep_nodes), dtype=np.int64) - - conn2 = old_to_new[conn2] - - return conn2 + + def __prepareInterpolator(self): + fluid_data_normalized = self._fluid_data_scaler.transform(self._data_in_table) + controlling_variable_data = fluid_data_normalized[:, [EntropicVars.Density.value, EntropicVars.Energy.value]] + self._lookup_tree = Invdisttree(controlling_variable_data, self._data_in_table) + return + + def __interpolateFluidData(self, density:float, staticEnergy:float): + cv_data = np.zeros([1, EntropicVars.N_STATE_VARS.value]) + cv_data[0, EntropicVars.Density.value] = density + cv_data[0, EntropicVars.Energy.value] = staticEnergy + cv_data_norm = self._fluid_data_scaler.transform(cv_data) + rhoe_norm = cv_data_norm[0, [EntropicVars.Density.value, EntropicVars.Energy.value]] + return self._lookup_tree(rhoe_norm) + def WriteOutParaview(self,file_name_out:str="vtk_table"): """ write a file containing all the LuT data that can be opened with Paraview @@ -751,8 +883,8 @@ def WriteOutParaview(self,file_name_out:str="vtk_table"): :param file_name_out: string indicating the name and extension of the saved file """ - #x, y = self._table_nodes[:, EntropicVars.Density.value], self._table_nodes[:, EntropicVars.Energy.value] - table_data_norm = self._fluid_data_scaler.transform(self._table_nodes) + #x, y = self._data_in_table[:, EntropicVars.Density.value], self._data_in_table[:, EntropicVars.Energy.value] + table_data_norm = self._fluid_data_scaler.transform(self._data_in_table) x, y = table_data_norm[:, EntropicVars.Density.value], table_data_norm[:, EntropicVars.Energy.value] # scale_x= self._fluid_data_scaler.data_max_[EntropicVars.Density.value] - self._fluid_data_scaler.data_min_[EntropicVars.Density.value] # scale_y= self._fluid_data_scaler.data_max_[EntropicVars.Energy.value] - self._fluid_data_scaler.data_min_[EntropicVars.Energy.value] @@ -766,7 +898,7 @@ def WriteOutParaview(self,file_name_out:str="vtk_table"): point_data = {} for var in self._table_vars: ivar = EntropicVars[var].value - point_data[var] = np.asarray(self._table_nodes[:, ivar]) + point_data[var] = np.asarray(self._data_in_table[:, ivar]) mesh = meshio.Mesh( points=pts, @@ -777,24 +909,24 @@ def WriteOutParaview(self,file_name_out:str="vtk_table"): return - def AddRefinementCriterion(self, TD_variable:str, norm_val_min:float=np.inf, norm_val_max:float=-np.inf): - """Apply refinement in the table where the normalized value of the thermodynamic variable lies between the specified bounds. - - :param TD_variable: name of the thermodynamic variable for which to apply refinement - :type TD_variable: str - :param norm_val_min: lower bound of the normalized thermodynamic variable, defaults to np.inf - :type norm_val_min: float, optional - :param norm_val_max: upper bound of the normalized thermodynamic variable, defaults to -np.inf - :type norm_val_max: float, optional - :raises Exception: if thermodynamic state variable is unknown to SU2 DataMiner - """ - if TD_variable not in self._table_vars: - raise Exception("%s is not present in fluid data" % TD_variable) - - self.__refinement_vars.append(TD_variable) - self.__refinement_norm_min.append(norm_val_min) - self.__refinement_norm_max.append(norm_val_max) - return + # def AddRefinementCriterion(self, TD_variable:str, norm_val_min:float=np.inf, norm_val_max:float=-np.inf): + # """Apply refinement in the table where the normalized value of the thermodynamic variable lies between the specified bounds. + + # :param TD_variable: name of the thermodynamic variable for which to apply refinement + # :type TD_variable: str + # :param norm_val_min: lower bound of the normalized thermodynamic variable, defaults to np.inf + # :type norm_val_min: float, optional + # :param norm_val_max: upper bound of the normalized thermodynamic variable, defaults to -np.inf + # :type norm_val_max: float, optional + # :raises Exception: if thermodynamic state variable is unknown to SU2 DataMiner + # """ + # if TD_variable not in self._table_vars: + # raise Exception("%s is not present in fluid data" % TD_variable) + + # self.__refinement_vars.append(TD_variable) + # self.__refinement_norm_min.append(norm_val_min) + # self.__refinement_norm_max.append(norm_val_max) + # return def __ApplyRefinement(self, fluid_data_norm_ref:np.ndarray[float]): ix_ref = np.array([],dtype=np.int64) @@ -831,7 +963,7 @@ def WriteTableFile(self, output_filepath:str=None): fid.write("[Version]\n1.0.1\n\n") fid.write("[Number of points]\n") - fid.write("%i\n" % np.shape(self._table_nodes)[0]) + fid.write("%i\n" % np.shape(self._data_in_table)[0]) fid.write("\n") fid.write("[Number of triangles]\n") @@ -852,10 +984,10 @@ def WriteTableFile(self, output_filepath:str=None): print("Writing table data...") fid.write("\n") - for iNode in range(len(self._table_nodes)): + for iNode in range(len(self._data_in_table)): for var in self._table_vars: ivar = EntropicVars[var].value - fid.write("\t%+.14e" % self._table_nodes[iNode, ivar]) + fid.write("\t%+.14e" % self._data_in_table[iNode, ivar]) fid.write("\n") fid.write("\n\n") print("Done!") diff --git a/TestCases/NICFD/LUT_Twophase/generate_table.py b/TestCases/NICFD/LUT_Twophase/generate_table.py index a53b377..81935e4 100644 --- a/TestCases/NICFD/LUT_Twophase/generate_table.py +++ b/TestCases/NICFD/LUT_Twophase/generate_table.py @@ -15,31 +15,31 @@ # Include visosity, conductivity, and vapor quality. config.IncludeTransportProperties(True) config.UseAutoRange(False) -config.SetDensityBounds(0.1, 460) +config.SetDensityBounds(0.1, 500) config.SetEnergyBounds(200e3, 360e3) config.SetNpDensity(50) config.SetNpEnergy(50) # Initiate table generator with adaptive triangulation. tablegen = SU2TableGenerator_NICFD(config) -tablegen.SetTableDiscretization("adaptive") +tablegen.setDiscretizationMethod("adaptive") # Specify table resolution for coarse and refined sections. -tablegen.SetCellSize_Coarse(2e-2) -tablegen.SetCellSize_Refined(2e-3) +tablegen.setTargetNumberOfNodes(10000) # Relative step size for finite-differences. -tablegen.SetFDStepSize(7e-3) +tablegen.setFDStepSize(7e-3) +tablegen.tableBoundsFromPointCloud("SU2_solution_data.csv") # Optionally, specify thermophysical variables to be included in the table. By default, all variables are included. # tablegen.SetTableVars(["Density","Energy","s","p","T", "dsdrho_e","dsde_rho", "d2sdrho2","d2sde2","d2sdedrho","VaporQuality","ViscosityDyn","Conductivity"]) # Specify custom refinement regions (low density, around Trova isentrope) -tablegen.AddRefinementCriterion("Density", 0.0, 10.0) -tablegen.AddRefinementCriterion("s", 729.13-2, 729.13+40) +tablegen.addRefinementCriterion("Density", 0.0, 50.0, 0.2) +tablegen.addRefinementCriterion("s", 729.13-2, 729.13+10, 0.2) # Generate table. -tablegen.GenerateTable() +tablegen.generateTable() # Write SU2 DRG and vtk table. tablegen.WriteTableFile("LUT_adaptive.drg") From cebd06a345ca73b3ca0cd762f05d8c600187615e Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Thu, 21 May 2026 20:58:10 +0200 Subject: [PATCH 02/44] fix whitespaces --- Manifold_Generation/LUT/LUTGenerators.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/Manifold_Generation/LUT/LUTGenerators.py b/Manifold_Generation/LUT/LUTGenerators.py index 00e0535..60ee796 100644 --- a/Manifold_Generation/LUT/LUTGenerators.py +++ b/Manifold_Generation/LUT/LUTGenerators.py @@ -118,7 +118,7 @@ class SU2TableGenerator_NICFD: _table_connectivity = [] # Table node connectivity per table level. _table_hullnodes = [] # Hull node indices per table level. - __saturation_curve_table_points:np.ndarray[float] = [] + __saturation_curve_table_points:np.ndarray[float] = [] _conditional_refinement_vars:list[str] = [] # Thermophysical quantities for which refinement is applied within specified bounds _conditional_refinement_indices:list[int] = [] # Column indices of refinement quantities @@ -156,7 +156,7 @@ def __setTableOutputVariables(self): if not self._Config.CalcTransportProperties(): self._table_vars.remove(EntropicVars.ViscosityDyn.name) self._table_vars.remove(EntropicVars.Conductivity.name) - return + return def tableBoundsFromPointCloud(self, pointcloud_filename:str): """Determine the table limits from a point cloud of density-static energy pairs stored in a comma-separated file. @@ -357,7 +357,7 @@ def __tablePreProcessing(self): self._fluid_data_scaler.fit(self._data_in_table) self.__prepareInterpolator() - return + return def __CartesianTriangulation(self): @@ -408,7 +408,7 @@ def __unstructuredTriangulation(self): self._data_in_table = table_fluid_data self._table_connectivity = tria self._table_hullnodes = hullTags - return + return def __computeMeshAndTableData(self): From 2ac468a6a044e0f47d1572a6e36c38bf51e93bcf Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Fri, 29 May 2026 10:47:40 +0200 Subject: [PATCH 03/44] Base classes used for table generation --- Manifold_Generation/LUT/LUTGenerator_Base.py | 511 +++++++++++++++++++ Manifold_Generation/LUT/MeshTools.py | 410 +++++++++++++++ 2 files changed, 921 insertions(+) create mode 100644 Manifold_Generation/LUT/LUTGenerator_Base.py create mode 100644 Manifold_Generation/LUT/MeshTools.py diff --git a/Manifold_Generation/LUT/LUTGenerator_Base.py b/Manifold_Generation/LUT/LUTGenerator_Base.py new file mode 100644 index 0000000..02dd4aa --- /dev/null +++ b/Manifold_Generation/LUT/LUTGenerator_Base.py @@ -0,0 +1,511 @@ +############################################################################################### +# # _____ __ _____ ____ __ __ ____ # # +# # / ___// / / /__ \ / __ \____ _/ /_____ _/ |/ (_)___ ___ _____ # # +# # \__ \/ / / /__/ / / / / / __ `/ __/ __ `/ /|_/ / / __ \/ _ \/ ___/ # # +# # ___/ / /_/ // __/ / /_/ / /_/ / /_/ /_/ / / / / / / / / __/ / # # +# # /____/\____//____/ /_____/\__,_/\__/\__,_/_/ /_/_/_/ /_/\___/_/ # # +# # # # +############################################################################################### + +############################# FILE NAME: LUTGenerator_Base.py ################################# +#=============================================================================================# +# author: Evert Bunschoten | +# :PhD Candidate , | +# :Flight Power and Propulsion | +# :TU Delft, | +# :The Netherlands | +# | +# | +# Description: | +# Base class for tabulated methods in SU2 DataMiner | +# | +# Version: 3.1.0 | +# | +#=============================================================================================# +import numpy as np +import pandas as pd +import meshio +import pandas +from os import sep +from sklearn.preprocessing import MinMaxScaler +from multiprocessing import Pool +from scipy.interpolate import RBFInterpolator + +from Common.Interpolators import fluidDataInterpolator +from Common.DataDrivenConfig import Config +from Manifold_Generation.LUT.MeshTools import Mesh2DPlane + + +class SU2TableGenerator_Base: + + _Config:Config = None + _nDim_table:int=2 + _base_cell_size:float = 2e-2 + _target_number_of_nodes:int = None + + _table_vars:list[str] = [] + _table_nodes:list[np.ndarray[float]] = [] + _data_in_table:list[np.ndarray[float]] = [] + _table_connectivity:list[np.ndarray[int]] = [] + _table_hullnodes:list[np.ndarray[int]] = [] + + _conditional_refinement_vars:list[str] = [] # Thermophysical quantities for which refinement is applied within specified bounds + _conditional_refinement_indices:list[int] = [] # Column indices of refinement quantities + _conditional_lower_bound:list[float] = [] # Lower bounds of conditional refinement quantities. + _conditional_upper_bound:list[float] = [] # Upper bounds of conditional refinement quantities. + _conditional_refinement_factor:list[float] = [] # Refinement factors to apply within the bounds + + _scaler_controlling_variables:MinMaxScaler = MinMaxScaler() + _fluid_data_interpolator:fluidDataInterpolator = None + + __smoothTableData:bool = False + __smoothingLevel:float = 0.1 + + __N_nearest_neighbors:int = None + __inverse_distance_exponent:float = None + + _N_table_levels:int = None + _table_levels:np.ndarray[float] = [] + _tableUpperLevel:float = None + _tableLowerLevel:float = None + _table_level_inserts:list[float] = [] + + __run_parallel:bool = False + __N_cores:int = 1 + + _state_quantities:list[str] = [] + + _planarMesher:Mesh2DPlane = Mesh2DPlane() + + _verbosity:int=1 + + def __init__(self, config_in:Config): + self._Config = config_in + self._nDim_table = len(self._Config.GetControllingVariables()) + return + + def defineInterpolator(self): + stateDataFrame = self._getFluidDataPointCloud() + cv_data = np.column_stack(tuple(stateDataFrame[cv] for cv in self._Config.GetControllingVariables())) + cv_data_scaled = self._scaler_controlling_variables.fit_transform(cv_data) + self._fluid_data_interpolator = fluidDataInterpolator(cv_data_scaled, stateDataFrame, self.__N_nearest_neighbors, self.__inverse_distance_exponent) + return + + def _getFluidDataPointCloud(self): + return + + def setMaximumCellSize(self, cell_size_coarse:float=1e-2): + """Specify the coarse level cell size of the table + + :param cell_size_coarse: coarse cell size, defaults to 1e-2 + :type cell_size_coarse: float, optional + :raises Exception: if specified cell size is negative or zero + """ + self._base_cell_size = cell_size_coarse + return + + def setTargetNodeCount(self, target_node_count:int=3000): + self._target_number_of_nodes = target_node_count + return + + def addRefinementCriterion(self, varname:str, lowerbound:float=-np.inf, upperbound:float=np.inf, coef:float=0.5): + """Specify conditional refinement based on interpolated thermochemical state data. Cell sizes are reduced by a factor "coef" where the table data lies between the specified lower bound and upper bound (inclusive) + + :param varname: thermochemical state variable name. + :type varname: str + :param lowerbound: lower bound above which refinement is applied, defaults to -np.inf + :type lowerbound: float, optional + :param upperbound: upper bound below which refinement is applied, defaults to np.inf + :type upperbound: float, optional + :param coef: mesh refinement factor, defaults to 0.5 + :type coef: float, optional + :raises Exception: if variable is not in the set of flamelet thermochemical state variables. + :raises Exception: if lower bound value exceeds upper bound value. + :raises Exception: if the coefficient value is negative. + """ + + if varname not in self._state_quantities: + raise Exception("%s is not in the list of available thermophysical state variables" % varname) + if lowerbound > upperbound: + raise Exception("Upper bound value should exceed lower bound value") + if coef <= 0: + raise Exception("Refinement coeffcient should be positive") + self._conditional_refinement_vars.append(varname) + self._conditional_refinement_indices.append(self._state_quantities.index(varname)) + self._conditional_lower_bound.append(lowerbound) + self._conditional_upper_bound.append(upperbound) + self._conditional_refinement_factor.append(coef) + return + + def generateTable(self): + + self.__preprocessTableSettings() + + self.defineInterpolator() + + self._table_nodes = [None]*self._N_table_levels + self._table_connectivity = [None]*self._N_table_levels + self._table_hullnodes = [None]*self._N_table_levels + self._data_in_table = [None]*self._N_table_levels + if self.__run_parallel: + pool = Pool(self.__N_cores) + results = pool.map(self.meshTableLevel, [i for i in range(self._N_table_levels)]) + pool.close() + + for iLevel in range(self._N_table_levels): + self._table_nodes[iLevel] = results[iLevel][0] + self._table_connectivity[iLevel] = results[iLevel][1] + self._table_hullnodes[iLevel] = results[iLevel][2] + self._data_in_table[iLevel] = results[iLevel][3] + else: + for iLevel, levelValue in enumerate(self._table_levels): + tableLevel = self.meshTableLevel(iLevel) + + self._table_nodes[iLevel] = tableLevel[0] + self._table_connectivity[iLevel] = tableLevel[1] + self._table_hullnodes[iLevel] = tableLevel[2] + self._data_in_table[iLevel] = tableLevel[3] + + if self.__smoothTableData: + self.smoothTableLevelData() + return + + def __preprocessTableSettings(self): + self.__processTableLevels() + return + + def meshTableLevel(self, level_index:int): + pointCloud = self._createPointCloud(self._table_levels[level_index]) + mesher = self._initiateMesher() + if self.__run_parallel: + mesher.setVerbosity(0) + else: + if self._verbosity > 1: + mesher.setVerbosity(1) + if self._verbosity > 2: + mesher.setVerbosity(2) + + mesher.setInitialPointCloud(pointCloud) + + self._passRefinementOptions(mesher) + + mesher.generateMesh() + + cvTable, triangles, hullIDs, tableDataFrame = self.__extractTableLevelData(mesher) + + if self.__is3D(): + self.__printmsg("Finished meshing table level %i at %s=%.2e with %i nodes" % (level_index, \ + self._Config.GetControllingVariables()[2], \ + self._table_levels[level_index], \ + len(cvTable))) + + return [cvTable, triangles,hullIDs, tableDataFrame] + + def __extractTableLevelData(self, mesher:Mesh2DPlane): + meshnodes = mesher.getMeshNodes() + triangles = mesher.getConnectivity() + hullIDs = mesher.getHullIDs() + cvTable_scaled = np.zeros([np.shape(meshnodes)[0], self._nDim_table]) + for iCv in range(self._nDim_table): + cvTable_scaled[:, iCv] = meshnodes[:, iCv] + + table_state_data = self._calculateTableStateData(cvTable_scaled) + + cvTable = self._scaler_controlling_variables.inverse_transform(cvTable_scaled) + + tableDataFrame = pd.DataFrame() + for var in self._table_vars: + tableDataFrame[var] = table_state_data[:, self._state_quantities.index(var)] + + for iCv, cv in enumerate(self._Config.GetControllingVariables()): + tableDataFrame[cv] = cvTable[:, iCv] + + return cvTable, triangles, hullIDs, tableDataFrame + + + def _createPointCloud(self, levelValue:float): + return + + def _initiateMesher(self): + return Mesh2DPlane() + + def _passRefinementOptions(self, mesher:Mesh2DPlane): + mesher.setBaseCellSize(self._base_cell_size) + if self._target_number_of_nodes: + mesher.setTargetNodeCount(self._target_number_of_nodes) + mesher.setRefinementFunction(self._refinelocation) + return + + def _calculateTableStateData(self, cv_table_nodes:np.ndarray[float]): + return self._fluid_data_interpolator(cv_table_nodes) + + def _refinelocation(self, x:float,y:float,z:float): + refinement_factor = 1.0 + if len(self._conditional_refinement_indices) > 0: + cv_input = np.array([x,y,z]) + fluid_state_data = self._fluid_data_interpolator(cv_input[:self._nDim_table]) + valid_pt = len(fluid_state_data) > 0 + if valid_pt: + for ivar, lower, upper, c in zip(self._conditional_refinement_indices, self._conditional_lower_bound, self._conditional_upper_bound, self._conditional_refinement_factor): + + test_val = fluid_state_data[ivar] + if (test_val >= lower) and (test_val <= upper): + refinement_factor = min(refinement_factor, c) + return refinement_factor + + def setTableVars(self, table_vars_in:list[str]): + controlling_variables = self._Config.GetControllingVariables() + + self._table_vars = [] + for cv in controlling_variables: + if cv not in table_vars_in: + print("Controlling variable %s should be in table, adding" % cv) + self._table_vars.append(cv) + + for user_var in table_vars_in: + if self._checkIfVariableIsValid(user_var): + self._table_vars.append(user_var) + else: + raise Exception("%s is not supported for table output variables") + return + + def _checkIfVariableIsValid(self, var_to_check:str): + return True + + + def writeParaviewTable(self, filepath_out:str=None): + self.__printmsg("Writing vtk table to %s..." % filepath_out) + for iLevel in range(self._N_table_levels): + if self._N_table_levels > 1: + table_level_filename = "%s_%i" % (filepath_out, iLevel) + else: + table_level_filename = filepath_out + + cv_table_nodes = self._table_nodes[iLevel] + cv_nodes_scaled = self._scaler_controlling_variables.transform(cv_table_nodes) + + self.__writeParaViewVTK(cv_nodes_scaled, self._data_in_table[iLevel], self._table_connectivity[iLevel], table_level_filename) + self.__printmsg("Done") + return + + def __writeParaViewVTK(self, cv_coordinates:np.ndarray[float], table_data:pandas.DataFrame, connectivity:np.ndarray[int], file_name_out:str): + + pts = cv_coordinates + conn = np.asarray(connectivity, dtype=np.int64) + if conn.min() == 1: + conn = conn - 1 + point_data = {} + for var in self._table_vars: + point_data[var] = np.asarray(table_data[var]) + + mesh = meshio.Mesh( + points=pts, + cells=[("triangle", conn)], + point_data=point_data + ) + mesh.write("%s.vtk" % file_name_out) + + return + + def _writeAdditionalInfoToTable(self, fid): + return + + def smoothTableLevelData(self): + self.__printmsg("Smoothing table data...") + for iLevel in range(self._N_table_levels): + cv_level_scaled = self._scaler_controlling_variables.transform(self._table_nodes[iLevel]) + + data_on_table_level = self._data_in_table[iLevel].values + smoothener = RBFInterpolator(cv_level_scaled[:, :2], data_on_table_level, kernel="linear",smoothing=self.__smoothingLevel,neighbors=100) + smoothened_table_data = smoothener(cv_level_scaled[:, :2]) + for ivar, var in enumerate(list(self._data_in_table[iLevel].keys())): + if var not in self._Config.GetControllingVariables(): + self._data_in_table[iLevel][var] = smoothened_table_data[:, ivar] + self.__printmsg("Done") + return + + def writeSU2Table(self, filepath_out:str=None): + + + if filepath_out: + file_out = filepath_out + ".drg" + else: + file_out = sep.join((self._Config.GetOutputDir(), "LUT_%s.drg" % self._Config.GetConfigName())) + + self.__printmsg("Writing LUT file with name %s" % file_out) + + fid = open(file_out, "w+") + fid.write("Dragon library\n\n") + fid.write("
\n\n") + if self.__is2D(): + fid.write("[Version]\n1.0.1\n\n") + else: + fid.write("[Version]\n1.1.0\n\n") + + self._writeAdditionalInfoToTable(fid) + + if self.__is3D(): + fid.write("[Number of table levels]\n%i\n\n" % self._N_table_levels) + fid.write("[Table levels]\n") + for z in self._table_levels: + fid.write("%+.16e\n" % z) + fid.write("\n") + + fid.write("[Number of points]\n") + for d in self._table_nodes: + Np = np.shape(d)[0] + fid.write("%i\n" % Np) + fid.write("\n") + + fid.write("[Number of triangles]\n") + for trias in self._table_connectivity: + Ntria = np.shape(trias)[0] + fid.write("%i\n" % Ntria) + fid.write("\n") + + fid.write("[Number of hull points]\n") + for hull in self._table_hullnodes: + Nhull = len(hull) + fid.write("%i\n" % Nhull) + fid.write("\n") + + fid.write("[Number of variables]\n%i\n\n" % len(self._table_vars)) + fid.write("[Variable names]\n") + for iVar, Var in enumerate(self._table_vars): + fid.write("%i:%s\n" % (iVar+1, Var)) + fid.write("\n") + + fid.write("
\n\n") + + fid.write("\n") + for iLevel in range(len(self._table_nodes)): + if self.__is3D(): + fid.write("\n") + Np = np.shape(self._table_nodes[iLevel])[0] + for iNode in range(Np): + line_table_data = "\t".join(["%.14e" % self._data_in_table[iLevel][var][iNode] for var in self._table_vars]) + + fid.write(line_table_data + "\n") + if self.__is3D(): + fid.write("\n") + fid.write("\n\n") + + fid.write("\n") + for iLevel in range(len(self._table_connectivity)): + if self.__is3D(): + fid.write("\n") + for iCell in range(len(self._table_connectivity[iLevel])): + fid.write("\t".join("%i" % c for c in self._table_connectivity[iLevel][iCell, :]+1) + "\n") + if self.__is3D(): + fid.write("\n") + fid.write("\n\n") + + fid.write("\n") + for iLevel in range(len(self._table_hullnodes)): + if self.__is3D(): + fid.write("\n") + for iCell in range(len(self._table_hullnodes[iLevel])): + fid.write(("%i" % (self._table_hullnodes[iLevel][iCell]+1)) + "\n") + if self.__is3D(): + fid.write("\n") + fid.write("\n\n") + self.__printmsg("Done") + + fid.close() + + def setTableLevels(self, level_values:np.ndarray[float]): + if self.__optionAppliesFor3D(): + self._table_levels = level_values + return + + def setTableLimits(self, lower_limit:float, upper_limit:float): + if self.__optionAppliesFor3D(): + if lower_limit >= upper_limit: + raise Exception("Table upper level value should exceed lower level value") + self._tableLowerLevel = lower_limit + self._tableUpperLevel = upper_limit + return + + def setNTableLevels(self, N_levels:int=10): + if self.__optionAppliesFor3D(): + if N_levels <= 0: + raise Exception("Number of table levels should be positive") + self._N_table_levels = N_levels + return + + def insertTableLevel(self, level_value:float): + if self.__optionAppliesFor3D(): + self._table_level_inserts.append(level_value) + return + + def setNProcessors(self, N_cores:int=2): + if self.__optionAppliesFor3D(): + if N_cores <= 1: + raise Exception("At least two cores should be used") + self.__run_parallel = True + self.__N_cores = N_cores + return + + def setNNearestNeighbors(self, N_input:int=6): + if N_input < 1: + raise Exception("Number of nearest neighbors should be strictly positive") + self.__N_nearest_neighbors = N_input + return + + def setInverseDistanceExponent(self, p_factor:float=2): + if p_factor <= 0: + raise Exception("Inverse distance parameter value should be positive") + self.__inverse_distance_exponent = p_factor + return + + def setSmoothingParameter(self, smoothing_factor:float=0): + self.__smoothTableData = True + self.__smoothingLevel = smoothing_factor + return + + def __optionAppliesFor3D(self): + if self.__is2D(): + print("Option does not apply for two-dimensional table, ignoring") + return False + else: + return True + + def __is2D(self): + return self._nDim_table==2 + + def __is3D(self): + return self._nDim_table==3 + + def __processTableLevels(self): + if self.__is3D(): + if not any(self._table_levels): + if self._tableUpperLevel and self._tableLowerLevel and self._N_table_levels: + self._table_levels = np.linspace(self._tableLowerLevel, self._tableUpperLevel, self._N_table_levels) + else: + raise Exception("No table level information provided, aborting") + + if any(self._table_level_inserts): + self._table_levels = np.append(self._table_levels, np.array(self._table_level_inserts)) + + unique_table_levels = np.unique(self._table_levels) + sorted_table_levels = np.sort(unique_table_levels) + self._table_levels = sorted_table_levels + self._N_table_levels = len(self._table_levels) + self._tableLowerLevel = min(self._table_levels) + self._tableUpperLevel = max(self._table_levels) + else: + self._table_levels = [0] + self._N_table_levels = 1 + return + + def __printmsg(self, msg:str): + if self._verbosity > 0: + print(msg) + return + + def setVerbosity(self, verbosity_level:int=1): + if verbosity_level < 0 or verbosity_level > 4: + raise Exception("Verbosity level should be between 0 and 4") + self._verbosity = verbosity_level + return + \ No newline at end of file diff --git a/Manifold_Generation/LUT/MeshTools.py b/Manifold_Generation/LUT/MeshTools.py new file mode 100644 index 0000000..167c738 --- /dev/null +++ b/Manifold_Generation/LUT/MeshTools.py @@ -0,0 +1,410 @@ +import numpy as np +import gmsh +from concave_hull import concave_hull, concave_hull_indexes +from Common.CommonMethods import shoelace, FiniteDifferenceDerivative + +def default_refinement_function(this, x:float, y:float, z:float): + return 1.0 + +class Mesh2DPlane: + + __pointCloud:np.ndarray[float] = None + _pointCloud_hullNodes:np.ndarray[float] = None + __mesh_along_coords:list[int] = [0, 1] + + _gmsh_geo:gmsh.model.geo = None + _gmsh_mesher:gmsh.model.mesh = None + _gmsh_verbosity:int = 0 + _mesher_verbosity:int = 1 + + _base_cell_size:float = 2e-2 + + refinement_function = default_refinement_function + + __plane_area:float = None + __perimiter_tag:int=None + __use_target_node_count:bool = False + __target_node_count:int=3000 + + __meshNodes:np.ndarray[float] = None + __connectivity:np.ndarray[int] = None + __hullNodeIDs:np.ndarray[int] = None + + + def __init__(self): + return + + def generateMesh(self): + self.__checkPlanarCoords() + + self._initializeGmesh() + self.__createGeometry() + + if self.__use_target_node_count: + self.__optimizeMeshResolution() + + self.__meshNodes, self.__connectivity, self.__hullNodeIDs = self.__mesh2D() + self._gmsh_geo.synchronize() + return + + def __optimizeMeshResolution(self): + self.__printMessage("Refining table based on target node count") + self.__printMessage("Target node count: %i" % self.__target_node_count) + + sufficient_refinement = False + niter_max = 20 + relaxation = 0.35 + + # Initial guess + self._base_cell_size = 2 * np.sqrt(self.__plane_area / self.__target_node_count) + iter = 0 + self.__printMessage("| Iteration | # Nodes | Cell size | Relative % diff |") + while not sufficient_refinement and iter < niter_max: + nodes, _, _ = self.__mesh2D() + n_nodes = len(nodes) + rel_diff = abs(float(n_nodes - self.__target_node_count)/self.__target_node_count) + + self.__printMessage("| %i | %i | %.3e | %.1f |" % (iter, n_nodes, self._base_cell_size, 100*rel_diff)) + # Terminate if relative difference is less than 1% + if rel_diff > 0.01: + self._base_cell_size *= (1 + relaxation * (float(n_nodes / self.__target_node_count) - 1)) + else: + sufficient_refinement = True + iter += 1 + + if iter == niter_max: + self.__printMessage("Target node count was not reached within %i iterations" % niter_max) + return + + def __createGeometry(self): + + + curvloop_table_outline, perimiter_tags = self.__createHullPerimiter() + self._gmsh_geo.synchronize() + self._createInternalGeometry(curvloop_table_outline, perimiter_tags) + return + + def __mesh2D(self): + + self.__prepareMesher() + + self._gmsh_mesher.generate(2) + + if self._mesher_verbosity > 1: + gmsh.fltk.run() + + meshNodeCoords, triaTags = self.__extractNodesTriangles() + + hullTags = self.__extractHulltags() + return meshNodeCoords, triaTags, hullTags + + def __checkPlanarCoords(self): + all_dims = [0, 1, 2] + for iDim in self.__mesh_along_coords: + all_dims.remove(iDim) + const_dim = all_dims[0] + equal_Z_coords = np.all(self.__pointCloud[:, const_dim]==self.__pointCloud[0, const_dim]) + if not equal_Z_coords: + raise Exception("Point cloud does not contain planar coordinates") + return + + def __createHullPerimiter(self): + self._pointCloud_hullNodes = self.__findHullNodes() + planeCurvLoop, perimiterTags = self.__createHullCurvLoop(self._pointCloud_hullNodes) + self.__perimiter_tag = self._gmsh_geo.addPhysicalGroup(1, perimiterTags, name="perimiter") + return planeCurvLoop, perimiterTags + + + def __findHullNodes(self): + + planarCoords_PointCloud = self.__pointCloud[:, self.__mesh_along_coords] + hullIndices = concave_hull_indexes(planarCoords_PointCloud, length_threshold=self._base_cell_size) + + planarCoords_hull = planarCoords_PointCloud[hullIndices] + self.__plane_area = shoelace(planarCoords_hull) + + return self.__pointCloud[hullIndices] + + def __createHullCurvLoop(self, hull_coords:np.ndarray[float]): + + hull_pts = self.__createHullPointEntities(hull_coords) + hull_perimiter = self.__connectHullPoints(hull_pts) + + curvloop = self._gmsh_geo.addCurveLoop(hull_perimiter) + + self._gmsh_geo.synchronize() + + return curvloop, hull_perimiter + + def __createHullPointEntities(self, coords_hull_pts:np.ndarray[float]): + hull_pts = [] + for i in range(int(len(coords_hull_pts))): + hull_pts.append(self._gmsh_geo.addPoint(coords_hull_pts[i, 0], coords_hull_pts[i, 1], coords_hull_pts[i, 2])) + return hull_pts + + def __connectHullPoints(self, pointIDs:list[int]): + hull_lines = [] + for i in range(len(pointIDs)): + j = (i + 1) % len(pointIDs) + hull_lines.append(self._gmsh_geo.addLine(pointIDs[i], pointIDs[j])) + return hull_lines + + def setInitialPointCloud(self, point_could_3D:np.ndarray[float]): + self.__pointCloud = self.__filterNansFromPointCloud(point_could_3D) + + return + + def __filterNansFromPointCloud(self, point_cloud_3D:np.ndarray[float]): + unique_pts_init = np.unique(point_cloud_3D, axis=0) + nans = np.isnan(unique_pts_init).all(1) + pts_wo_nans = unique_pts_init[np.invert(nans)] + return pts_wo_nans + + + def _createInternalGeometry(self,curvloop_perimiter:int, perimiter_tags:list[int]): + + planeID = self._gmsh_geo.addPlaneSurface([curvloop_perimiter]) + self._gmsh_geo.addPhysicalGroup(2, [planeID]) + return + + def __extractNodesTriangles(self): + nodeTags, coords, _ = self._gmsh_mesher.getNodes() + nodeTags = np.asarray(nodeTags, dtype=np.int64) + MeshPoints = np.asarray(coords, dtype=float).reshape(-1, 3) + + order = np.argsort(nodeTags) + nodeTags_sorted = nodeTags[order] + + # 2) 2D elements + elemTypes, _, elemNodeTags = self._gmsh_mesher.getElements(2) + + tris = [] + quads = [] + + for et, nodes_flat in zip(elemTypes, elemNodeTags): + if et == 2: # triangles with 3 nodes + tri_tags = np.asarray(nodes_flat, dtype=np.int64).reshape(-1, 3) + tris.append(self.__map_tags(tri_tags, nodeTags_sorted,order).reshape(-1, 3)) + elif et == 3: # quad with 4 nodes + quad_tags = np.asarray(nodes_flat, dtype=np.int64).reshape(-1, 4) + quads.append(self.__map_tags(quad_tags, nodeTags_sorted,order).reshape(-1, 4)) + + tris = np.vstack(tris) if tris else np.zeros((0, 3), dtype=np.int64) + + if quads: + quads = np.vstack(quads) + tris = np.vstack([ + tris, + quads[:, [0, 1, 2]], + quads[:, [0, 2, 3]], + ]) + return MeshPoints, tris + + def __map_tags(self,tags,nodeTags_sorted,order): + tags = np.asarray(tags, dtype=np.int64).ravel() + pos = np.searchsorted(nodeTags_sorted, tags) + return order[pos] + + + def __extractHulltags(self): + perimiter_curve_tags = gmsh.model.getEntitiesForPhysicalGroup(1, self.__perimiter_tag) + allHullTags = [] + for line in perimiter_curve_tags: + nodeTags, nodes, _ = self._gmsh_mesher.getNodes(includeBoundary=True,tag=line,dim=1) + nodes = np.asarray(nodes, dtype=float).reshape(-1, 3) + nodeTags = np.asarray(nodeTags, dtype=np.int64) + + allHullTags = np.append(allHullTags, nodeTags) + hullTags = np.unique(np.int64(allHullTags)) + return hullTags + + def setBaseCellSize(self, cell_size_input:float): + if cell_size_input <= 0: + raise Exception("Cell size should be strictly positive") + self._base_cell_size = cell_size_input + return + + def _initializeGmesh(self): + gmsh.initialize() + gmsh.model.add("2D plane") + gmsh.option.setNumber("General.Verbosity", self._gmsh_verbosity) + gmsh.option.setNumber("Mesh.MeshSizeFromPoints",0) + gmsh.option.setNumber("Mesh.MeshSizeExtendFromBoundary",0) + + self._gmsh_geo = gmsh.model.geo + self._gmsh_mesher = gmsh.model.mesh + return + + def __prepareMesher(self): + self._gmsh_geo.synchronize() + self._gmsh_mesher.clear() + gmsh.option.setNumber("Mesh.MeshSizeMax", self._base_cell_size) + + def meshSizeCallback(dim,tag,x,y,z,lc): + refinement_factor = self.refinement_function(x, y, z) + return self._base_cell_size * refinement_factor + + self._gmsh_mesher.setSizeCallback(meshSizeCallback) + return + + def setRefinementFunction(self, function_input): + self.refinement_function = function_input + return + + def getGmshFactory(self): + return self._gmsh_geo + + def getGmshMesher(self): + return self._gmsh_mesher + + def setTargetNodeCount(self, Np_target:int=3000): + if Np_target <= 0: + raise Exception("Target number of nodes should be positive") + self.__target_node_count = Np_target + self.__use_target_node_count = True + return + + def getMeshNodes(self): + return self.__meshNodes + + def getConnectivity(self): + return self.__connectivity + + def getHullIDs(self): + return self.__hullNodeIDs + + def __printMessage(self, msg:str): + if self._mesher_verbosity > 0: + print(msg) + return + + def setVerbosity(self, verbosity_level:int=1): + self._mesher_verbosity = verbosity_level + if self._mesher_verbosity > 1: + self._gmsh_verbosity = 4 + + return + + +class MeshThermodynamicPlane(Mesh2DPlane): + + __includeSaturationCurve:bool=False + __saturation_curve_points:np.ndarray[float] = None + + def __init__(self): + super().__init__() + return + + def _createInternalGeometry(self, perimiter_curvloop:int, perimiter_tags:list[int]): + if self.__includeSaturationCurve: + saturation_curve_crvloops = self.__createSaturationCurveGeom() + fluid_plane_crvloops = [perimiter_curvloop] + [-c for c in saturation_curve_crvloops] + fluid_surf = self._gmsh_geo.addPlaneSurface(fluid_plane_crvloops) + sat_surfs = [self._gmsh_geo.addPlaneSurface([c]) for c in saturation_curve_crvloops] + self._gmsh_geo.addPhysicalGroup(2, [fluid_surf] + sat_surfs) + else: + super()._createInternalGeometry(perimiter_curvloop, perimiter_tags) + return + + def __createSaturationCurveGeom(self): + + sat_curve_pts, sat_curve_lower, sat_curve_upper = self.__saturationOffsetCurves() + + sat_curve_lower_pts, sat_curve_upper_pts, segment_lengths = self.__samplePointsAlongSaturationCurve(sat_curve_pts, sat_curve_lower, sat_curve_upper) + + return self.__encloseSaturationCurve(sat_curve_lower_pts, sat_curve_upper_pts, segment_lengths) + + def __encloseSaturationCurve(self, lower_offset_pts:np.ndarray[int], upper_offset_pts:np.ndarray[int], segment_lengths:list[float]): + connecting_lines = [] + Npoints = len(lower_offset_pts) + for i in range(Npoints): + connecting_lines.append(self._gmsh_geo.addLine(lower_offset_pts[i], upper_offset_pts[i])) + + sat_curve_crvloops = [] + for i in range(Npoints-1): + if segment_lengths[i] < 2*self._base_cell_size: + tangent_line_upper=self._gmsh_geo.addLine(upper_offset_pts[i],upper_offset_pts[i+1]) + tangent_line_lower=self._gmsh_geo.addLine(lower_offset_pts[i],lower_offset_pts[i+1]) + + sat_curve_crvloops.append(self._gmsh_geo.addCurveLoop([tangent_line_upper, connecting_lines[i+1], tangent_line_lower, connecting_lines[i]], reorient=True)) + self._gmsh_geo.synchronize() + return sat_curve_crvloops + + def __samplePointsAlongSaturationCurve(self, saturation_curve_points:np.ndarray[float], offset_lower:np.ndarray[float], offset_upper:np.ndarray[float]): + i = 0 + j = 1 + sat_curve_upper_pts = [] + sat_curve_lower_pts = [] + segment_lengths = [] + sat_curve_upper_pts.append(self._gmsh_geo.addPoint(offset_upper[0,0], offset_upper[0,1],0)) + sat_curve_lower_pts.append(self._gmsh_geo.addPoint(offset_lower[0,0], offset_lower[0,1],0)) + while j < len(saturation_curve_points): + dist = np.sqrt(np.sum(np.power(saturation_curve_points[j] - saturation_curve_points[i],2))) + local_refinement_factor = self.refinement_function(saturation_curve_points[j, 0], saturation_curve_points[j, 1], 0) + if dist < 0.5*local_refinement_factor*self._base_cell_size: + j += 1 + else: + i = j + j += 1 + segment_lengths.append(dist) + sat_curve_upper_pts.append(self._gmsh_geo.addPoint(offset_upper[i, 0], offset_upper[i,1],0)) + sat_curve_lower_pts.append(self._gmsh_geo.addPoint(offset_lower[i, 0], offset_lower[i,1],0)) + return sat_curve_lower_pts, sat_curve_upper_pts, segment_lengths + + def __saturationOffsetCurves(self): + + norm_vector = self.__getSaturationCurveNormal() + + # Create offset curves to ensure that no nodes are generated on the saturation curve itself. + sat_curve_rhoe_lower, sat_curve_rhoe_upper = self.__createSaturationCurveOffsets(norm_vector) + + valid_pts = self.__clipSaturationCurveToPlane(sat_curve_rhoe_lower, sat_curve_rhoe_upper) + + sat_curve_pts = self.__saturation_curve_points[valid_pts, :] + norm_vector = norm_vector[valid_pts, :] + sat_curve_rhoe_lower = sat_curve_rhoe_lower[valid_pts] + sat_curve_rhoe_upper = sat_curve_rhoe_upper[valid_pts] + + return sat_curve_pts, sat_curve_rhoe_lower, sat_curve_rhoe_upper + + def __getSaturationCurveNormal(self): + dedrho_sat_norm = FiniteDifferenceDerivative(self.__saturation_curve_points[:,1], self.__saturation_curve_points[:,0]) + norm_vector = np.column_stack((-dedrho_sat_norm, np.ones(len(dedrho_sat_norm)))) + norm_vector = norm_vector / np.sqrt(np.sum(np.power(norm_vector, 2), axis=1))[:,np.newaxis] + + return norm_vector + + def __createSaturationCurveOffsets(self, normal_vector:np.ndarray[float]): + offset = 5e-4*self._base_cell_size + + sat_curve_rhoe_upper = self.__saturation_curve_points + offset * normal_vector + sat_curve_rhoe_lower = self.__saturation_curve_points - offset * normal_vector + + return sat_curve_rhoe_lower, sat_curve_rhoe_upper + + + def __clipSaturationCurveToPlane(self, pts_offset_lower:np.ndarray[float], pts_offset_upper:np.ndarray[float]): + + ref_area = shoelace(self._pointCloud_hullNodes) + + within_hull = np.zeros(len(self.__saturation_curve_points),dtype=np.bool) + + for i in range(len(self.__saturation_curve_points)): + XY_with_pt = np.vstack((self._pointCloud_hullNodes[:,:2], pts_offset_upper[i,:])) + hull_n = concave_hull(XY_with_pt, length_threshold=self._base_cell_size) + area_n = shoelace(hull_n) + within_hull_upper = (area_n <= ref_area ) + XY_with_pt = np.vstack((self._pointCloud_hullNodes[:,:2], pts_offset_lower[i,:])) + hull_n = concave_hull(XY_with_pt, length_threshold=self._base_cell_size) + area_n = shoelace(hull_n) + within_hull_lower = (area_n <= ref_area ) + within_hull[i] = (within_hull_upper and within_hull_lower) + + return within_hull + + def setSaturationCurvePoints(self, saturation_curve_rhoe:np.ndarray[float]): + self.__saturation_curve_points = saturation_curve_rhoe + self.__includeSaturationCurve = True + return + + \ No newline at end of file From ebde2c59b9fc9d00a327ab4274ee577dff946c7a Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Fri, 29 May 2026 10:48:35 +0200 Subject: [PATCH 04/44] Fixed bug where two-phase data was not computed correctly --- Data_Generation/DataGenerator_NICFD.py | 59 ++++++++++++-------------- 1 file changed, 28 insertions(+), 31 deletions(-) diff --git a/Data_Generation/DataGenerator_NICFD.py b/Data_Generation/DataGenerator_NICFD.py index 008334b..3963a8d 100644 --- a/Data_Generation/DataGenerator_NICFD.py +++ b/Data_Generation/DataGenerator_NICFD.py @@ -499,9 +499,6 @@ def ComputeData(self): self.fluid.update(CP.PT_INPUTS, self.__X_grid[i,j], self.__Y_grid[i,j]) else: self.fluid.update(CP.DmassUmass_INPUTS, self.__X_grid[j,i], self.__Y_grid[j,i]) - p = self.fluid.p() - T = self.fluid.T() - self.fluid.update(CP.PT_INPUTS, p, T) # Check if fluid phase is not vapor or liquid self.__StateVars_fluid[i,j,:], self.__success_locations[i,j] = self.GetStateVector() except: @@ -523,32 +520,32 @@ def __TransportProperties(self, q:float): phase = self.fluid.phase() is_twophase = (phase == CoolP.iphase_twophase) if is_twophase: - vapor_q = q - sat_props = self.__get_saturated_transport_properties(p) - - if sat_props is not None: - # Compute void fraction for volume-weighted models - alpha = self.__compute_void_fraction( - q, sat_props['rho_l'], sat_props['rho_g']) - # Mixture viscosity - viscosity = self.__compute_twophase_viscosity( - q, sat_props['mu_l'], sat_props['mu_g'], - sat_props['rho_l'], sat_props['rho_g'], alpha) - - # Mixture thermal conductivity - conductivity = self.__compute_twophase_conductivity( - q, sat_props['k_l'], sat_props['k_g'], - sat_props['rho_l'], sat_props['rho_g'], alpha) - else: - # Fallback: try to get properties at current state (might fail) - try: - self.fluid.update(CP.DmassUmass_INPUTS, rho, e) - viscosity = self.fluid.viscosity() - conductivity = self.fluid.conductivity() - except: - # Last resort: interpolate based on quality - viscosity = 1e-5 # Reasonable default - conductivity = 0.1 # Reasonable default + vapor_q = q + sat_props = self.__get_saturated_transport_properties(p) + + if sat_props is not None: + # Compute void fraction for volume-weighted models + alpha = self.__compute_void_fraction( + q, sat_props['rho_l'], sat_props['rho_g']) + # Mixture viscosity + viscosity = self.__compute_twophase_viscosity( + q, sat_props['mu_l'], sat_props['mu_g'], + sat_props['rho_l'], sat_props['rho_g'], alpha) + + # Mixture thermal conductivity + conductivity = self.__compute_twophase_conductivity( + q, sat_props['k_l'], sat_props['k_g'], + sat_props['rho_l'], sat_props['rho_g'], alpha) + else: + # Fallback: try to get properties at current state (might fail) + try: + self.fluid.update(CP.DmassUmass_INPUTS, rho, e) + viscosity = self.fluid.viscosity() + conductivity = self.fluid.conductivity() + except: + # Last resort: interpolate based on quality + viscosity = 1e-5 # Reasonable default + conductivity = 0.1 # Reasonable default else: vapor_q = -1.0 viscosity = self.fluid.viscosity() @@ -776,7 +773,7 @@ def SetFDStepSizes(self, fd_step_rho:float=1e-5, fd_step_e:float=1e-5): self.__fd_step_size_e = fd_step_e return - def ComputeSaturationCurve(self): + def ComputeSaturationCurve(self, N_samples:int=4000): """Compute the density and static energy along the fluid saturation curve :return: density and static energy array of saturation curve @@ -785,7 +782,7 @@ def ComputeSaturationCurve(self): eos_fluid = "%s::%s" % (self._Config.GetEquationOfState(), self._Config.GetFluid()) ptriple = CP.PropsSI("PTRIPLE", eos_fluid) pcrit = CP.PropsSI("PCRIT", eos_fluid) - Psat = np.linspace(ptriple, pcrit, 4000) + Psat = np.linspace(ptriple, pcrit, N_samples) rhoLiq=CP.PropsSI("D","P",Psat,"Q",0,eos_fluid) rhoVap=CP.PropsSI("D","P",Psat,"Q",1,eos_fluid) From 34696fc698d2b3cd6b398060a278bd6bead05026 Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Fri, 29 May 2026 10:49:15 +0200 Subject: [PATCH 05/44] Moved commonly used algorithms to CommonMethods and included general fluid data interpolation function --- Common/CommonMethods.py | 71 ++++++++++++- Common/Interpolators.py | 222 +++++++++++++++++++++++++++++++++++----- 2 files changed, 265 insertions(+), 28 deletions(-) diff --git a/Common/CommonMethods.py b/Common/CommonMethods.py index 7081186..050eb43 100644 --- a/Common/CommonMethods.py +++ b/Common/CommonMethods.py @@ -188,4 +188,73 @@ def write_SU2_MLP(file_out:str, weights:list[np.ndarray], biases:list[np.ndarray fid.write("\t".join("%+.16e" % float(b) for b in B) + "\n") fid.close() - return \ No newline at end of file + return + +def shoelace(XY:np.ndarray[float]): + """Shoelace algorithm for area computations + + :param XY: hull node coordinates + :type XY: np.ndarray[float] + :return: area of concave hull + :rtype: float + """ + x = XY[:,0] + y = XY[:,1] + S1 = np.sum(x*np.roll(y,-1)) + S2 = np.sum(y*np.roll(x,-1)) + + area = .5*np.absolute(S1 - S2) + return area + + +def FiniteDifferenceDerivative(y:np.ndarray[float], x:np.ndarray[float]): + """Calculate second-order accurate, one-dimensional finite-difference derivatives of y with respect to x. + + :param y: data to calculate the finite-differences for. + :type y: np.ndarray[float] + :param x: axial coordinates. + :type x: np.ndarray[float] + :return: finite-difference derivatives of y with respect to x. + :rtype: np.ndarray[float] + """ + Np = len(x) + dydx = np.zeros(Np) + for i in range(1, Np-1): + y_m = y[i-1] + y_p = y[i+1] + y_0 = y[i] + x_m = x[i-1] + x_p = x[i+1] + x_0 = x[i] + dx_1 = x_p - x_0 + dx_2 = x_0 - x_m + dx2_1 = dx_1*dx_1 + dx2_2 = dx_2*dx_2 + if (dx_1==0) or (dx_2==0): + dydx[i] = 0.0 + else: + dydx[i] = (dx2_2 * y_p + (dx2_1 - dx2_2)*y_0 - dx2_1*y_m)/(dx_1*dx_2*(dx_1+dx_2)) + dx_1 = x[1] - x[0] + dx_2 = x[2] - x[0] + dx2_1 = dx_1*dx_1 + dx2_2 = dx_2*dx_2 + y_0 = y[0] + y_p = y[1] + y_pp = y[2] + if (dx_1==0) or (dx_2==0): + dydx[0] = 0.0 + else: + dydx[0] = (dx2_1 * y_pp + (dx2_2 - dx2_1)*y_0 - dx2_2*y_p)/(dx_1*dx_2*(dx_1 - dx_2)) + + dx_1 = x[-2] - x[-1] + dx_2 = x[-3] - x[-1] + dx2_1 = dx_1*dx_1 + dx2_2 = dx_2*dx_2 + y_0 = y[-1] + y_p = y[-2] + y_pp = y[-3] + if (dx_1==0) or (dx_2==0): + dydx[-1] = 0.0 + else: + dydx[-1] = (dx2_1 * y_pp + (dx2_2 - dx2_1)*y_0 - dx2_2*y_p)/(dx_1*dx_2*(dx_1 - dx_2)) + return dydx diff --git a/Common/Interpolators.py b/Common/Interpolators.py index 01de02a..e42f7fa 100644 --- a/Common/Interpolators.py +++ b/Common/Interpolators.py @@ -25,7 +25,10 @@ from scipy.spatial import cKDTree as KDTree import numpy as np - +import pandas as pd +from tqdm import tqdm +import matplotlib.pyplot as plt +from sklearn.preprocessing import MinMaxScaler class Invdisttree: """ inverse-distance-weighted interpolation using KDTree: invdisttree = Invdisttree( X, z ) -- data points, values @@ -77,41 +80,206 @@ class Invdisttree: """ # anykernel( dj / av dj ) is also scale-free # error analysis, |f(x) - idw(x)| ? todo: regular grid, nnear ndim+1, 2*ndim + __state_space_data:np.ndarray[float]=None + __nDim_input:int=None + __number_of_states:int=None + __KD_tree:KDTree = None + __single_query_sample:bool=False + __number_of_nearest_neighbors:int=None + __inverse_distance_exponent:float=None + + def __init__( self, samples_state_space:np.ndarray[float], state_data:np.ndarray[float], leafsize:int=10): + assert len(samples_state_space) == len(state_data), "len(X) %d != len(z) %d" % (len(samples_state_space), len(state_data)) + self.__nDim_input = np.shape(samples_state_space)[1] + self.__number_of_states = np.shape(state_data)[1] + self.__KD_tree = KDTree( samples_state_space, leafsize=leafsize ) + self.__state_space_data = state_data + + + def query( self, query_samples:np.ndarray[float], nearest_neighbors:int=3, inverse_distance_exponent:float=2 ): + """Interpolate based based on the inverse-distance-weighted values of a specified number of nearest neighbors. + + :param query_samples: query samples + :type query_samples: np.ndarray[float] + :param nearest_neighbors: number of nearest neighbors, defaults to 3 + :type nearest_neighbors: int, optional + :param inverse_distance_exponent: exponent of the inverse distance to the nearest neighbors, defaults to 2 + :type inverse_distance_exponent: float, optional + :return: interpolated data from nearest neighbors + :rtype: np.ndarray[float] + """ + self.__number_of_nearest_neighbors = nearest_neighbors + self.__inverse_distance_exponent = inverse_distance_exponent + + formatted_query_samples = self.__parse_query_input(query_samples) + + return self.__interpolate_from_inverse_distance(formatted_query_samples) + + def __parse_query_input(self, input_query_samples:np.ndarray[float]): + formatted_query_samples = self.__format_query_input(input_query_samples) + self.__check_correct_input_dimensions(formatted_query_samples) + return formatted_query_samples + + def __format_query_input(self, unformatted_query_samples:np.ndarray[float]): + number_of_dimensions = unformatted_query_samples.ndim + if number_of_dimensions == 1: + self.__single_query_sample = True + return np.expand_dims(unformatted_query_samples, -1) + else: + self.__single_query_sample = False + return unformatted_query_samples + + def __check_correct_input_dimensions(self, query_samples:np.ndarray[float]): + if query_samples.shape[1] != self.__nDim_input: + raise Exception("Incorrect input dimensionality") + return + + def __interpolate_from_inverse_distance(self, query_input_samples:np.ndarray[float]): + distances, nearest_neighbor_indices = self.__evaluate_KD_tree(query_input_samples, self.__number_of_nearest_neighbors) + interpolated_data = self.__interpolate_from_nearest_neighbors(nearest_neighbor_indices, distances) + if self.__single_query_sample: + return interpolated_data[0] + else: + return interpolated_data + + def __evaluate_KD_tree(self, query_input_samples:np.ndarray[float], number_of_nearest_neighbors:int): + distances_to_nearest_neighbors, nearest_neighbor_indices = self.__KD_tree.query(query_input_samples, k=number_of_nearest_neighbors,eps=0) + return distances_to_nearest_neighbors, nearest_neighbor_indices + + def __interpolate_from_nearest_neighbors(self, nearest_neighbor_indices:np.ndarray[int], distances_to_neighbors:np.ndarray[float]): + number_of_query_samples = np.shape(distances_to_neighbors)[0] + interpolated_state_data = np.zeros([number_of_query_samples, self.__number_of_states]) + for j, (dist, ix) in enumerate(zip( distances_to_neighbors, nearest_neighbor_indices )): + if self.__single_nearest_neighbor(): + interpolated_state_data[j] = self.__state_space_data[ix] + elif self.__nearest_sample_is_too_close(dist): + interpolated_state_data[j] = self.__state_space_data[ix[0]] + else: # weight z s by 1/dist -- + nearest_neighbor_coefficients = np.power(dist, -self.__inverse_distance_exponent) + nearest_neighbor_coefficients /= np.sum(nearest_neighbor_coefficients) + interpolated_state_data[j] = np.dot( nearest_neighbor_coefficients, self.__state_space_data[ix]) - def __init__( self, X, z, leafsize=10, stat=0 ): - assert len(X) == len(z), "len(X) %d != len(z) %d" % (len(X), len(z)) - self.tree = KDTree( X, leafsize=leafsize ) # build the tree - self.z = z - self.stat = stat - self.wn = 0 - self.wsum = None + return interpolated_state_data + + def __single_nearest_neighbor(self): + return self.__number_of_nearest_neighbors==1 + + def __nearest_sample_is_too_close(self, ordered_distances:np.ndarray[float]): + return ordered_distances[0] < 1e-10 + + def query_with_local_parameters( self, q:np.ndarray[float], nnear_vals:np.ndarray[int], p_vals:np.ndarray[float]): + """Interpolate with number of nearest neighbors and p-factor for each sample. - def __call__( self, q, nnear=3, eps=0, p=2, weights=None ): + :param q: query samples + :type q: np.ndarray[float] + :param nnear_vals: number of nearest neighbors for each query sample + :type nnear_vals: np.array[int] + :param p_vals: distance exponent factor for each query sample + :type p_vals: np.array[float] + :return: interpolated data from nearest neighbors + :rtype: np.ndarray[float] + """ # nnear nearest neighbours of each query point -- q = np.asarray(q) + max_nnear = max(max(nnear_vals), 2) qdim = q.ndim if qdim == 1: - q = np.array([q]) - if self.wsum is None: - self.wsum = np.zeros(nnear) + q = np.expand_dims(q, -1) + nnear_vals = np.expand_dims(nnear_vals, -1) + p_vals = np.expand_dims(p_vals, -1) - self.distances, self.ix = self.tree.query( q, k=nnear, eps=eps ) - interpol = np.zeros( (len(self.distances),) + np.shape(self.z[0]) ) + eps=0 + self.distances, self.ix = self.__KD_tree.query( q, k=max_nnear, eps=eps ) + interpol = np.zeros([np.shape(q)[0], np.shape(self.z)[1]]) jinterpol = 0 - for dist, ix in zip( self.distances, self.ix ): - if nnear == 1: - wz = self.z[ix] - elif dist[0] < 1e-10: + for p, nnear, dist, ix in zip(p_vals, nnear_vals, self.distances, self.ix ): + if nnear == 1 or dist[0] < 1e-10: wz = self.z[ix[0]] else: # weight z s by 1/dist -- - w = 1 / dist**p - if weights is not None: - w *= weights[ix] # >= 0 + w = np.power(dist[:nnear], -p) w /= np.sum(w) - wz = np.dot( w, self.z[ix]) - if self.stat: - self.wn += 1 - self.wsum += w - interpol[jinterpol] = wz + wz = np.dot(w,self.z[ix[:nnear]]) + interpol[jinterpol, :] = wz jinterpol += 1 - return interpol if qdim > 1 else interpol[0] \ No newline at end of file + return interpol if qdim > 1 else interpol[0] + +class fluidDataInterpolator: + + __controlling_variable_nodes:np.ndarray[float] = None + __lookup_data:np.ndarray[float] = None + __lookup_tree:Invdisttree = None + __n_near_single:int = 6 + __p_fac_single:int = 2 + __state_vars:list[str] = None + + def __init__(self,cv_data:np.ndarray[float], state_data:pd.DataFrame, number_of_nearest_neighbors:int=None, inverse_distance_exponent:float=None): + + self.__controlling_variable_nodes = cv_data + self.__lookup_data = state_data.values + self.__state_vars = list(state_data.keys()) + self.__lookup_tree = Invdisttree(cv_data, state_data.values) + self.__n_near_single = number_of_nearest_neighbors + self.__p_fac_single = inverse_distance_exponent + + if not self.__n_near_single or not self.__p_fac_single: + self.tuneTreeParameters() + return + + def tuneTreeParameters(self): + + Np_total = np.shape(self.__controlling_variable_nodes)[0] + Np_train = int(0.8*Np_total) + + n_control_vars = np.shape(self.__controlling_variable_nodes)[1] + scaler = MinMaxScaler() + lookup_data_scaled = scaler.fit_transform(self.__lookup_data) + full_data_set = np.column_stack((self.__controlling_variable_nodes, lookup_data_scaled)) + np.random.shuffle(full_data_set) + + train_data = full_data_set[:Np_train] + test_data = full_data_set[Np_train:] + + cv_data_train= train_data[:, :n_control_vars] + cv_data_test= test_data[:, :n_control_vars] + + train_tree = Invdisttree(cv_data_train, cv_data_train) + n_near_range = range(1, 20) + p_range = np.linspace(0, 6, 10) + RMS_ppv = np.zeros([len(n_near_range), len(p_range)]) + for i in tqdm(range(len(n_near_range)),desc="Searching for optimal tree parameters..."): + for j in range(len(p_range)): + interpolated_state = train_tree.query(cv_data_test, nearest_neighbors=n_near_range[i], inverse_distance_exponent=p_range[j]) + rms_local = np.sum(np.power(interpolated_state - cv_data_test, 2)) + RMS_ppv[i,j] = rms_local + [imin,jmin] = divmod(RMS_ppv.argmin(), RMS_ppv.shape[1]) + self.__n_near_single = n_near_range[imin] + self.__p_fac_single = p_range[jmin] + + print("Number of nearest neighbors: %i" % self.__n_near_single) + print("Inverse distance exponent: %.2f" % self.__p_fac_single) + # plt.figure() + # ax = plt.axes(projection='3d') + # ax.plot3D(cv_data_test[:,0], cv_data_test[:,1], lookup_data_test[:, 2], 'k.') + # ax.plot3D(cv_data_test[:,0], cv_data_test[:,1], self.__lookup_data[:, 2], 'r.') + # plt.show() + return + + def __call__(self, cv_data_query:np.ndarray[float],varnames:list[str]=[]): + ndim_input = cv_data_query.ndim + if ndim_input < 2: + cv_data_query = cv_data_query[np.newaxis] + + fluid_data_interp = self.__lookup_tree.query(cv_data_query, nearest_neighbors=self.__n_near_single, inverse_distance_exponent=self.__p_fac_single) + nvars =len(varnames) + if nvars > 0: + var_indices = [self.__state_vars.index(var) for var in varnames] + + if fluid_data_interp.shape[0] == 1: + return fluid_data_interp[0,var_indices] + else: + return fluid_data_interp[:, var_indices] + else: + if fluid_data_interp.shape[0] == 1: + return fluid_data_interp[0] + else: + return fluid_data_interp From 08a1b7b1321c773d24b9f28ebf815595bfc24b94 Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Fri, 29 May 2026 10:49:39 +0200 Subject: [PATCH 06/44] Derived classes for FGM and NICFD tabulation --- Manifold_Generation/LUT/LUTGenerators.py | 1095 +++------------------- 1 file changed, 148 insertions(+), 947 deletions(-) diff --git a/Manifold_Generation/LUT/LUTGenerators.py b/Manifold_Generation/LUT/LUTGenerators.py index 60ee796..f02d568 100644 --- a/Manifold_Generation/LUT/LUTGenerators.py +++ b/Manifold_Generation/LUT/LUTGenerators.py @@ -7,7 +7,7 @@ # # # # ############################################################################################### -######################### FILE NAME: FlameletTableGenerator.py ################################ +############################# FILE NAME: LUTGenerators.py ##################################### #=============================================================================================# # author: Evert Bunschoten | # :PhD Candidate , | @@ -17,310 +17,58 @@ # | # | # Description: | -# Table generator class for generating SU2-supported tables of flamelet data. | +# Table generator classes for generating SU2-supported tables for FGM and NICFD problems | # Version: 3.1.0 | # | #=============================================================================================# import numpy as np -from Common.Properties import EntropicVars,DefaultSettings_NICFD +import pandas as pd +import os +from Common.Properties import EntropicVars,DefaultSettings_FGM from su2dataminer.generate_data import DataGenerator_CoolProp -from scipy.spatial import Delaunay -from sklearn.preprocessing import MinMaxScaler -from tqdm import tqdm -from Common.Interpolators import Invdisttree -from Common.DataDrivenConfig import Config_NICFD -import gmsh -from concave_hull import concave_hull -import meshio - -def shoelace(XY:np.ndarray[float]): - """Shoelace algorithm for area computations - - :param XY: hull node coordinates - :type XY: np.ndarray[float] - :return: area of concave hull - :rtype: float - """ - x = XY[:,0] - y = XY[:,1] - S1 = np.sum(x*np.roll(y,-1)) - S2 = np.sum(y*np.roll(x,-1)) - - area = .5*np.absolute(S1 - S2) - return area - -def FiniteDifferenceDerivative(y:np.ndarray[float], x:np.ndarray[float]): - """Calculate second-order accurate, one-dimensional finite-difference derivatives of y with respect to x. - - :param y: data to calculate the finite-differences for. - :type y: np.ndarray[float] - :param x: axial coordinates. - :type x: np.ndarray[float] - :return: finite-difference derivatives of y with respect to x. - :rtype: np.ndarray[float] - """ - Np = len(x) - dydx = np.zeros(Np) - for i in range(1, Np-1): - y_m = y[i-1] - y_p = y[i+1] - y_0 = y[i] - x_m = x[i-1] - x_p = x[i+1] - x_0 = x[i] - dx_1 = x_p - x_0 - dx_2 = x_0 - x_m - dx2_1 = dx_1*dx_1 - dx2_2 = dx_2*dx_2 - if (dx_1==0) or (dx_2==0): - dydx[i] = 0.0 - else: - dydx[i] = (dx2_2 * y_p + (dx2_1 - dx2_2)*y_0 - dx2_1*y_m)/(dx_1*dx_2*(dx_1+dx_2)) - dx_1 = x[1] - x[0] - dx_2 = x[2] - x[0] - dx2_1 = dx_1*dx_1 - dx2_2 = dx_2*dx_2 - y_0 = y[0] - y_p = y[1] - y_pp = y[2] - if (dx_1==0) or (dx_2==0): - dydx[0] = 0.0 - else: - dydx[0] = (dx2_1 * y_pp + (dx2_2 - dx2_1)*y_0 - dx2_2*y_p)/(dx_1*dx_2*(dx_1 - dx_2)) - - dx_1 = x[-2] - x[-1] - dx_2 = x[-3] - x[-1] - dx2_1 = dx_1*dx_1 - dx2_2 = dx_2*dx_2 - y_0 = y[-1] - y_p = y[-2] - y_pp = y[-3] - if (dx_1==0) or (dx_2==0): - dydx[-1] = 0.0 - else: - dydx[-1] = (dx2_1 * y_pp + (dx2_2 - dx2_1)*y_0 - dx2_2*y_p)/(dx_1*dx_2*(dx_1 - dx_2)) - return dydx - -class SU2TableGenerator_NICFD: - _Config:Config_NICFD = None # Config_FGM class from which to read settings. - _DataGenerator:DataGenerator_CoolProp = None - _savedir:str - - _base_cell_size:float = 2e-2 # Table level base cell size. - - __target_node_count:bool=False # Refine grid based on target number of nodes. - __Np_target:int=3000 # Target number of nodes per table level. - - _table_vars:list[str] = [s.name for s in EntropicVars][:-1] - _data_in_table = [] # Progress variable, total enthalpy, and mixture fraction node values for each table level. - _data_in_table_norm = [] # Normalized table nodes for each level. - _table_connectivity = [] # Table node connectivity per table level. - _table_hullnodes = [] # Hull node indices per table level. - - __saturation_curve_table_points:np.ndarray[float] = [] - - _conditional_refinement_vars:list[str] = [] # Thermophysical quantities for which refinement is applied within specified bounds - _conditional_refinement_indices:list[int] = [] # Column indices of refinement quantities - _conditional_lower_bound:list[float] = [] # Lower bounds of conditional refinement quantities. - _conditional_upper_bound:list[float] = [] # Upper bounds of conditional refinement quantities. - _conditional_refinement_factor:list[float] = [] # Refinement factors to apply within the bounds - - _fluid_data_scaler:MinMaxScaler= MinMaxScaler() # Scaler for flamelet data controlling variables. - - __initiate_from_pointcloud:bool = False # Fit table bounds to reference data set. - __initial_solution_filename:str = None # File name from which reference data are read. - - _lookup_tree:Invdisttree = None # Inverse-distance KD tree used for evaluating conditional refinement criteria. - - def __init__(self, Config:Config_NICFD): - """ - Initiate table generator class. Settings regarding the fluid data generation and table resolution are automatically retrieved from the configuration object. - - :param Config: Config_FGM object. - :type Config: Config_FGM - """ - self._Config = Config - - self._DataGenerator = DataGenerator_CoolProp(self._Config) - - self.__setTableOutputVariables() - - return - - def __setTableOutputVariables(self): - entropic_vars = [a.name for a in EntropicVars][:-1] - self._table_vars = entropic_vars.copy() +from Common.DataDrivenConfig import Config_NICFD,Config_FGM +from Manifold_Generation.LUT.LUTGenerator_Base import SU2TableGenerator_Base +from Manifold_Generation.LUT.MeshTools import MeshThermodynamicPlane + +class TableGenerator_NICFD(SU2TableGenerator_Base): + _Config:Config_NICFD = None + __datagenerator:DataGenerator_CoolProp = None + __thermodynamic_data:pd.DataFrame = None + + def __init__(self, config_in:Config_NICFD): + self._state_quantities = [q.name for q in EntropicVars] + super().__init__(config_in) + self.__datagenerator = DataGenerator_CoolProp(self._Config) + self.__datagenerator.SetFDStepSizes(3e-3,3e-3) + self.__defaultTableVariables() + return + + def __defaultTableVariables(self): + vars_to_exclude = [EntropicVars.N_STATE_VARS.name] if not self._Config.TwoPhase(): - self._table_vars.remove(EntropicVars.VaporQuality.name) + vars_to_exclude.append(EntropicVars.VaporQuality.name) if not self._Config.CalcTransportProperties(): - self._table_vars.remove(EntropicVars.ViscosityDyn.name) - self._table_vars.remove(EntropicVars.Conductivity.name) - return - - def tableBoundsFromPointCloud(self, pointcloud_filename:str): - """Determine the table limits from a point cloud of density-static energy pairs stored in a comma-separated file. - - :param pointcloud_filename: file name from which density-static energy data are read. - :type pointcloud_filename: str - :raises Exception: if the labels for density and static energy cannot be found in the file header. - """ - with open(pointcloud_filename,'r') as fid: - variables_in_header = fid.readline().strip().split(',') - variables_in_header = [v.strip("\"") for v in variables_in_header] - - if EntropicVars.Density.name not in variables_in_header or EntropicVars.Energy.name not in variables_in_header: - raise Exception("Density and static energy not found in point cloud data") - - self.__initial_solution_filename = pointcloud_filename - self.__initiate_from_pointcloud = True - return - - def setFDStepSize(self, val_step_size:float=3e-7): - """Set the relative step size for density and static energy for evaluating fluid properties in the two-phase region. - - :param val_step_size: relative finite-difference step size, defaults to 3e-7 - :type val_step_size: float, optional - :raises Exception: if the provided value is negative or zero. - """ - if val_step_size <= 0: - raise Exception("Relative step size for finite-differences should be positive.") - self._DataGenerator.SetFDStepSizes(val_step_size,val_step_size) - return - - def setTargetNumberOfNodes(self, N_nodes_target:int=4000): - """Define the total number of points in the thermodynamic table. - - :param N_nodes_target: desired number of nodes per table level, defaults to 4000 - :type N_nodes_target: int, optional - :raises Exception: if non-positive values are provided. - """ - if N_nodes_target <= 0: - raise Exception("Target number of table nodes should be positive") - self.__Np_target = N_nodes_target - self.__target_node_count = True - - return - - def setNpDensity(self, Np_x:int=DefaultSettings_NICFD.Np_p): - """Specify the number of table nodes in the x-direction of the Cartesian table. - - :param Np_x: number of nodes, defaults to DefaultSettings_NICFD.Np_p - :type Np_x: int, optional - """ - self._Config.SetNpDensity(Np_x) - return - - def setNpEnergy(self, Np_y:int=DefaultSettings_NICFD.Np_temp): - """Specify the number of table nodes in the y-direction of the Cartesian table. - - :param Np_y: number of nodes, defaults to DefaultSettings_NICFD.Np_temp - :type Np_y: int, optional - """ - self._Config.SetNpEnergy(Np_y) - return - - def setTableLimitsDensity(self, Rho_lower:float=DefaultSettings_NICFD.Rho_min, Rho_upper:float=DefaultSettings_NICFD.Rho_max): - """Define the density bounds of the density-energy based fluid data grid. - - :param Rho_lower: lower limit density value, defaults to DefaultSettings_NICFD.Rho_min - :type Rho_lower: float, optional - :param Rho_upper: upper limit for density, defaults to DefaultSettings_NICFD.Rho_max - :type Rho_upper: float, optional - :raises Exception: if lower value for density exceeds upper value. - """ - self._DataGenerator.UseAutoRange(False) - self._DataGenerator.SetDensityBounds(Rho_lower, Rho_upper) - return - - def setTableLimitsEnergy(self, E_lower:float=DefaultSettings_NICFD.Energy_min, E_upper:float=DefaultSettings_NICFD.Energy_max): - """Define the internal energy bounds of the density-energy based fluid data grid. - - :param E_lower: lower limit internal energy value, defaults to DefaultSettings_NICFD.Energy_min - :type E_lower: float, optional - :param E_upper: upper limit for internal energy, defaults to DefaultSettings_NICFD.Energy_max - :type E_upper: float, optional - :raises Exception: if lower value for internal energy exceeds upper value. - """ - self._DataGenerator.UseAutoRange(False) - self._DataGenerator.SetEnergyBounds(E_lower, E_upper) - return - - def setMaximumCellSize(self, cell_size_coarse:float=1e-2): - """Specify the coarse level cell size of the table - - :param cell_size_coarse: coarse cell size, defaults to 1e-2 - :type cell_size_coarse: float, optional - :raises Exception: if specified cell size is negative or zero - """ - if cell_size_coarse <= 0: - raise Exception("Cell size value should be positive") - self._base_cell_size = cell_size_coarse - return - - def addRefinementCriterion(self, varname:str, lowerbound:float=-np.inf, upperbound:float=np.inf, coef:float=0.5): - """Specify conditional refinement based on interpolated thermochemical state data. Cell sizes are reduced by a factor "coef" where the table data lies between the specified lower bound and upper bound (inclusive) - - :param varname: thermochemical state variable name. - :type varname: str - :param lowerbound: lower bound above which refinement is applied, defaults to -np.inf - :type lowerbound: float, optional - :param upperbound: upper bound below which refinement is applied, defaults to np.inf - :type upperbound: float, optional - :param coef: mesh refinement factor, defaults to 0.5 - :type coef: float, optional - :raises Exception: if variable is not in the set of flamelet thermochemical state variables. - :raises Exception: if lower bound value exceeds upper bound value. - :raises Exception: if the coefficient value is negative. - """ - fluid_vars = [a.name for a in EntropicVars][:-1] - if varname not in fluid_vars: - raise Exception("%s is not in the list of available thermophysical state variables" % varname) - if lowerbound > upperbound: - raise Exception("Upper bound value should exceed lower bound value") - if coef <= 0: - raise Exception("Refinement coeffcient should be positive") - self._conditional_refinement_vars.append(varname) - self._conditional_refinement_indices.append(fluid_vars.index(varname)) - self._conditional_lower_bound.append(lowerbound) - self._conditional_upper_bound.append(upperbound) - self._conditional_refinement_factor.append(coef) - return + vars_to_exclude.append(EntropicVars.Conductivity.name) + vars_to_exclude.append(EntropicVars.ViscosityDyn.name) + vars_to_exclude.append(EntropicVars.VaporQuality.name) + for var in EntropicVars: + if var.name not in vars_to_exclude: + self._table_vars.append(var.name) + return - def setDiscretizationMethod(self, method:str=DefaultSettings_NICFD.tabulation_method): - """Overwrite the thermodynamic state space discretization method from the configuration. - - :param method: discratization method, defaults to 'cartesian' - :type method: str, optional - """ - self._Config.SetTableDiscretization(method) - return - def setTableVars(self, table_vars_in:list[str]): - """Specify the thermophysical variables to be included in the table file. All quantities are included by default. The list shoud at least contain "Density" and "Energy". - - :param table_vars_in: list with thermophysical variables to be included in the table. - :type table_vars_in: list[str] - :raises Exception: if any of the specified variables are not supported by SU2 DataMiner. - """ - self._table_vars = [] - if EntropicVars.Density.name not in table_vars_in: - print("Density should always be included in table variables") - self._table_vars.append(EntropicVars.Density.name) - - if EntropicVars.Energy.name not in table_vars_in: - print("Energy should always be included in table variables") - self._table_vars.append(EntropicVars.Energy.name) - - if self._Config.EnableTwophase() and EntropicVars.VaporQuality.name in table_vars_in: + if self._Config.TwoPhase() and EntropicVars.VaporQuality.name in table_vars_in: print("Table generator not configured for two-phase, ignoring vapor quality from table data.") table_vars_in.remove(EntropicVars.VaporQuality.name) if not self._Config.CalcTransportProperties(): if EntropicVars.Conductivity.name in table_vars_in: print("Table generator not configured for transport properties, ignoring conductivity data") + table_vars_in.remove(EntropicVars.Conductivity.name) if EntropicVars.ViscosityDyn.name in table_vars_in: print("Table generator not configured for transport properties, ignoring viscosity data") + table_vars_in.remove(EntropicVars.ViscosityDyn.name) valid_vars = True for v in table_vars_in: @@ -334,678 +82,131 @@ def setTableVars(self, table_vars_in:list[str]): valid_vars = False if not valid_vars: raise Exception("Some specified thermophysical variables are not supported.") - return - - - def generateTable(self): - """Initiate table generation process - """ - - self.__tablePreProcessing() - - # Load initial fluid data and scale it - if self._Config.GetTableDiscretization()=="cartesian": - self.__CartesianTriangulation() - else: - self.__unstructuredTriangulation() - return - - def __tablePreProcessing(self): - self.__generateCoarseTable() - - self._fluid_data_scaler = MinMaxScaler() - self._fluid_data_scaler.fit(self._data_in_table) - - self.__prepareInterpolator() - return - - - def __CartesianTriangulation(self): - """ - Create Delaunay triangulation of valid grid points. - """ - print("Creating Delaunay triangulation...") - - # Extract valid points - rho_table = self.state_data[:,:,EntropicVars.Density.value] - e_table = self.state_data[:,:,EntropicVars.Energy.value] - rho_valid = rho_table[self.valid_mask].flatten() - e_valid = e_table[self.valid_mask].flatten() - - # Stack as (N, 2) array - cv_table = np.column_stack([rho_valid, e_valid]) - - #self._data_in_table = np.column_stack(tuple(self.state_data[:,:,EntropicVars[v].value][self.valid_mask].flatten() for v in self._table_vars)) - self._data_in_table = np.column_stack(tuple(self.state_data[:,:,i][self.valid_mask].flatten() for i in range(EntropicVars.N_STATE_VARS.value))) - - # Create Delaunay triangulation - tri = Delaunay(cv_table) - self._table_connectivity = tri.simplices - - # Identify hull nodes - edges = np.vstack([tri.simplices[:, [0, 1]], - tri.simplices[:, [1, 2]], - tri.simplices[:, [2, 0]]]) - edges = np.sort(edges, axis=1) - unique_edges, counts = np.unique(edges, axis=0, return_counts=True) - boundary_edges = unique_edges[counts == 1] - self._table_hullnodes= np.unique(boundary_edges.flatten()) - - print(f" Triangulation nodes: {len(self._data_in_table):,}") - print(f" Triangles: {len(self._table_connectivity):,}") - print(f" Hull nodes: {len(self._table_hullnodes):,}") - print() - return - - def __unstructuredTriangulation(self): - print("Generating unstructured table with adaptive refinement...") - - if self._Config.TwoPhase(): - self.__saturation_curve_table_points = self.__CreateSaturationCurve() - - table_fluid_data, tria, hullTags = self.__computeMeshAndTableData() - - self._data_in_table = table_fluid_data - self._table_connectivity = tria - self._table_hullnodes = hullTags - return - - def __computeMeshAndTableData(self): - - scaled_fluid_data = self._fluid_data_scaler.transform(self._data_in_table) - rhoe_norm = scaled_fluid_data[:, [EntropicVars.Density.value, EntropicVars.Energy.value]] - - rhoe_norm_mesh_nodes,tria, hullTags = self.__discretize2DTable(rhoe_norm) - - # Calculate thermodynamic state variables of initial table nodes - fluid_data_norm = np.zeros([len(rhoe_norm_mesh_nodes), EntropicVars.N_STATE_VARS.value]) - fluid_data_norm[:, EntropicVars.Density.value] = rhoe_norm_mesh_nodes[:,0] - fluid_data_norm[:, EntropicVars.Energy.value] = rhoe_norm_mesh_nodes[:,1] - fluid_data_mesh = self._fluid_data_scaler.inverse_transform(fluid_data_norm) - fluid_data_mesh = self.__evaluateFluidPropertiesOnMesh(fluid_data_mesh) - return fluid_data_mesh, tria, hullTags - - - def __discretize2DTable(self, rhoe_init:np.ndarray[float]): - - # Discretize thermochemical state space with target node count or user-defined cell sizes. - if self.__target_node_count: - nodes, tris, hulltags = self.__optimizeTableNodes(rhoe_init) - else: - nodes, tris, hulltags = self.__create2DMesh(rhoe_init) - - return nodes, tris, hulltags - - def __optimizeTableNodes(self, rhoe_init:np.ndarray[float]): - print("Refining table based on target node count") - print("Target node count: %i" % self.__Np_target) - - # Iterate base cell size to reach within 1% of target number of table nodes. - sufficient_refinement = False - niter_max = 20 - relaxation = 0.35 - - # Initial guess - self._base_cell_size = 2 * np.sqrt(1.0 / self.__Np_target) - print("| Iteration | Node count | Base cell size | Diff from target(%) |") - iter = 0 - while not sufficient_refinement and iter < niter_max: - nodes, tris, hulltags = self.__create2DMesh(rhoe_init) - n_nodes = len(nodes) - rel_diff = abs(float(n_nodes - self.__Np_target)/self.__Np_target) - print("| %i | %i | %.3e | %.1f |" % (iter, n_nodes, self._base_cell_size, 100*rel_diff)) - # Terminate if relative difference is less than 1% - if rel_diff > 0.01: - self._base_cell_size *= (1 + relaxation * (float(n_nodes / self.__Np_target) - 1)) - else: - sufficient_refinement = True - iter += 1 - - if iter == niter_max: - print("Target node count was not reached within %i iterations" % niter_max) - return nodes, tris, hulltags - - def __create2DMesh(self, rhoe_init:np.ndarray[float]): - XY_hull = self.__findHullNodes(rhoe_init) - - gmsh.initialize() - gmsh.model.add("table_level") - gmsh.option.setNumber("General.Verbosity", 0) - gmsh.option.setNumber("Mesh.MeshSizeFromPoints",0) - gmsh.option.setNumber("Mesh.MeshSizeExtendFromBoundary",0) - - factory = gmsh.model.geo - mesher = gmsh.model.mesh - - hull_curvloop, hull_lines = self.__createHullCurvLoop(XY_hull, factory) - - self.__createFluidPlane(XY_hull, hull_curvloop, factory) - - self.__prepareMesher(mesher) - - mesher.generate(2) - - nodeTags, triaTags = self.__extractNodesTriangles() - hullTags = self.__extractHulltags(hull_lines) - - gmsh.finalize() - - return nodeTags, triaTags, hullTags - - def __findHullNodes(self, initial_point_cloud:np.ndarray[float]): - unique_pts_init = np.unique(initial_point_cloud, axis=0) - nans = np.isnan(unique_pts_init).all(1) - pts_wo_nans = unique_pts_init[np.invert(nans)] - XY_hull = concave_hull(pts_wo_nans, length_threshold=self._base_cell_size) - - i = 0 - hull_indices = [i] - while i < (len(XY_hull)-1): - i_next = i+1 - found_next_pt = False - while not found_next_pt: - dist = np.sqrt(np.sum(np.power(XY_hull[i_next, :] - XY_hull[i, :], 2))) - if (dist >= self._base_cell_size) or (i_next == len(XY_hull)-1): - found_next_pt = True - else: - i_next += 1 - i = i_next - hull_indices.append(i_next) - XY_hull = XY_hull[hull_indices, :] - return XY_hull - - def __createHullCurvLoop(self, XY_hull:np.ndarray[float], factory:gmsh.model.geo): - # Create hull points - hull_pts = [] - for i in range(int(len(XY_hull))): - hull_pts.append(factory.addPoint(XY_hull[i, 0], XY_hull[i, 1], 0)) - - # Connect hull points to a closed multi-component curve - hull_lines = [] - for i in range(len(hull_pts)-1): - hull_lines.append(factory.addLine(hull_pts[i], hull_pts[i+1])) - hull_lines.append(factory.addLine(hull_pts[-1], hull_pts[0])) - - # Create a 2D plane of the enclosed space - curvloop = factory.addCurveLoop(hull_lines) - - # Apply refinement points - self.__table_hull_area = shoelace(concave_hull(XY_hull, length_threshold=self._base_cell_size)) - - factory.synchronize() - - return curvloop, hull_lines + return super().setTableVars(table_vars_in) - def __createFluidPlane(self, rhoe_hull:np.ndarray[float], hull_crvloop:int, factory:gmsh.model.geo): - - add_sat_curve = len(self.__saturation_curve_table_points) > 0 - if add_sat_curve: - sat_curve_crvloops = self.__createSaturationCurveGeom(rhoe_hull, factory) + def _checkIfVariableIsValid(self, var_to_check:str): + if var_to_check in self._state_quantities: + return True else: - sat_curve_crvloops = [] - - fluid_plane_crvloops = [hull_crvloop] + [-c for c in sat_curve_crvloops] - fluid_surf = factory.addPlaneSurface(fluid_plane_crvloops) - sat_surfs = [factory.addPlaneSurface([c]) for c in sat_curve_crvloops] - factory.addPhysicalGroup(2, [fluid_surf] + sat_surfs) - factory.synchronize() - return fluid_surf - + return False - def __createSaturationCurveGeom(self, rhoe_hull:np.ndarray[float], factory:gmsh.model.geo): - - sat_curve_pts, sat_curve_lower, sat_curve_upper = self.__saturationOffsetCurves(rhoe_hull) - - sat_curve_lower_pts, sat_curve_upper_pts = self.__samplePointsAlongSaturationCurve(sat_curve_pts, sat_curve_lower, sat_curve_upper, factory) - - return self.__encloseSaturationCurve(sat_curve_lower_pts, sat_curve_upper_pts, factory) - - def __encloseSaturationCurve(self, lower_offset_pts:np.ndarray[int], upper_offset_pts:np.ndarray[int], factory:gmsh.model.geo): - connecting_lines = [] - Npoints = len(lower_offset_pts) - for i in range(Npoints): - connecting_lines.append(factory.addLine(lower_offset_pts[i], upper_offset_pts[i])) - - sat_curve_crvloops = [] - for i in range(Npoints-1): - tangent_line_upper=factory.addLine(upper_offset_pts[i],upper_offset_pts[i+1]) - tangent_line_lower=factory.addLine(lower_offset_pts[i],lower_offset_pts[i+1]) - - sat_curve_crvloops.append(factory.addCurveLoop([tangent_line_upper, connecting_lines[i+1], tangent_line_lower, connecting_lines[i]], reorient=True)) - factory.synchronize() - return sat_curve_crvloops - - def __samplePointsAlongSaturationCurve(self, saturation_curve_points:np.ndarray[float], offset_lower:np.ndarray[float], offset_upper:np.ndarray[float], factory:gmsh.model.geo): - i = 0 - j = 1 - sat_curve_upper_pts = [] - sat_curve_lower_pts = [] - sat_curve_upper_pts.append(factory.addPoint(offset_upper[0,0], offset_upper[0,1],0)) - sat_curve_lower_pts.append(factory.addPoint(offset_lower[0,0], offset_lower[0,1],0)) - dists = [] - while j < len(saturation_curve_points): - dist = np.sqrt(np.sum(np.power(saturation_curve_points[j] - saturation_curve_points[i],2))) - local_refinement_factor = self.__refinelocation(saturation_curve_points[j, 0], saturation_curve_points[j, 1], 0) - if dist < 0.5*local_refinement_factor*self._base_cell_size: - j += 1 - else: - i = j - j += 1 - dists.append(dist) - sat_curve_upper_pts.append(factory.addPoint(offset_upper[i, 0], offset_upper[i,1],0)) - sat_curve_lower_pts.append(factory.addPoint(offset_lower[i, 0], offset_lower[i,1],0)) - return sat_curve_lower_pts, sat_curve_upper_pts - - def __saturationOffsetCurves(self, rhoe_hull:np.ndarray[float]): - - norm_vector = self.__getSaturationCurveNormal() - - # Create offset curves to ensure that no nodes are generated on the saturation curve itself. - sat_curve_rhoe_lower, sat_curve_rhoe_upper = self.__createSaturationCurveOffsets(norm_vector) - - valid_pts = self.__clipCurveToTableLimits(rhoe_hull, sat_curve_rhoe_lower, sat_curve_rhoe_upper) - - sat_curve_pts = self.__saturation_curve_table_points[valid_pts, :] - norm_vector = norm_vector[valid_pts, :] - sat_curve_rhoe_lower = sat_curve_rhoe_lower[valid_pts] - sat_curve_rhoe_upper = sat_curve_rhoe_upper[valid_pts] - - return sat_curve_pts, sat_curve_rhoe_lower, sat_curve_rhoe_upper - - def __getSaturationCurveNormal(self): - dedrho_sat_norm = FiniteDifferenceDerivative(self.__saturation_curve_table_points[:,0], self.__saturation_curve_table_points[:,1]) - norm_vector = np.column_stack((-1.0 / dedrho_sat_norm, np.ones(len(dedrho_sat_norm)))) - norm_vector = norm_vector / np.sqrt(np.sum(np.power(norm_vector, 2), axis=1))[:,np.newaxis] - return norm_vector - - def __createSaturationCurveOffsets(self, normal_vector:np.ndarray[float]): - offset = 1e-4*self._base_cell_size - - sat_curve_rhoe_upper = self.__saturation_curve_table_points + offset * normal_vector - sat_curve_rhoe_lower = self.__saturation_curve_table_points - offset * normal_vector - - return sat_curve_rhoe_lower, sat_curve_rhoe_upper - - - def __clipCurveToTableLimits(self, rhoe_hull:np.ndarray[float], pts_offset_lower:np.ndarray[float], pts_offset_upper:np.ndarray[float]): - - within_bounds_lower = np.logical_and(pts_offset_lower > 0, pts_offset_lower < 1).all(1) - within_bounds_upper = np.logical_and(pts_offset_upper > 0, pts_offset_upper< 1).all(1) - valid_sat_curve_pts = np.logical_and(within_bounds_lower, within_bounds_upper) - - nans_lower = np.isnan(pts_offset_lower).all(1) - nans_upper = np.isnan(pts_offset_upper).all(1) - - valid_nans = np.logical_and(np.invert(nans_lower), np.invert(nans_upper)) - valid_pts = np.logical_and(valid_sat_curve_pts, valid_nans) - - within_hull = np.zeros(len(self.__saturation_curve_table_points),dtype=np.bool) - for i in range(len(self.__saturation_curve_table_points)): - XY_with_pt = np.vstack((rhoe_hull, pts_offset_upper[i,:])) - hull_n = concave_hull(XY_with_pt, length_threshold=self._base_cell_size) - area_n = shoelace(hull_n) - within_hull_upper = (area_n <= self.__table_hull_area ) - XY_with_pt = np.vstack((rhoe_hull, pts_offset_lower[i,:])) - hull_n = concave_hull(XY_with_pt, length_threshold=self._base_cell_size) - area_n = shoelace(hull_n) - within_hull_lower = (area_n <= self.__table_hull_area ) - within_hull[i] = (within_hull_upper and within_hull_lower) - valid_pts = np.logical_and(valid_pts, within_hull) - - return valid_pts - - def __prepareMesher(self, mesher:gmsh.model.mesh): - mesher.clear() - gmsh.option.setNumber("Mesh.MeshSizeMax", self._base_cell_size) - def meshSizeCallback(dim,tag,x,y,z,lc): - fac = self.__refinelocation(x, y, z) - return self._base_cell_size * fac + def _passRefinementOptions(self, mesher:MeshThermodynamicPlane): + if self._Config.TwoPhase(): + rhoe_saturation_curve = self.__datagenerator.ComputeSaturationCurve(N_samples=1000) + saturation_curve_pts_scaled = self._scaler_controlling_variables.transform(rhoe_saturation_curve) + mesher.setSaturationCurvePoints(saturation_curve_pts_scaled) + return super()._passRefinementOptions(mesher) + + def _initiateMesher(self): + return MeshThermodynamicPlane() + + def _getFluidDataPointCloud(self): + self.__datagenerator.PreprocessData() + self.__datagenerator.ComputeData() + state_data_pointcloud, valid_mask = self.__datagenerator.GetStateData() - mesher.setSizeCallback(meshSizeCallback) - return - - def __refinelocation(self, x:float,y:float,z:float): - state_fluid_norm = np.zeros([1, EntropicVars.N_STATE_VARS.value]) - state_fluid_norm[0, EntropicVars.Density.value] = x - state_fluid_norm[0, EntropicVars.Energy.value] = y - state_fluid_dimensional = self._fluid_data_scaler.inverse_transform(state_fluid_norm) - val_density = state_fluid_dimensional[0, EntropicVars.Density.value] - val_static_energy = state_fluid_dimensional[0, EntropicVars.Energy.value] + state_dataFrame = pd.DataFrame() + for var in EntropicVars: + if var.value != EntropicVars.N_STATE_VARS.value: + state_dataFrame[var.name] = state_data_pointcloud[valid_mask, var.value] - refinement_factor = 1.0 + self.__thermodynamic_data = state_dataFrame - if len(self._conditional_refinement_indices) > 0: - fluid_state_data = self.__interpolateFluidData(val_density, val_static_energy) - valid_pt = len(fluid_state_data) > 0 - - if valid_pt: - for ivar, lower, upper, c in zip(self._conditional_refinement_indices, self._conditional_lower_bound, self._conditional_upper_bound, self._conditional_refinement_factor): - test_val = fluid_state_data[ivar] - if (test_val >= lower) and (test_val <= upper): - refinement_factor = min(refinement_factor, c) - return refinement_factor + return state_dataFrame - def __extractNodesTriangles(self): - nodeTags, coords, _ = gmsh.model.mesh.getNodes() - nodeTags = np.asarray(nodeTags, dtype=np.int64) - MeshPoints = np.asarray(coords, dtype=float).reshape(-1, 3)[:, :2] - - order = np.argsort(nodeTags) - nodeTags_sorted = nodeTags[order] - - # 2) 2D elements - elemTypes, _, elemNodeTags = gmsh.model.mesh.getElements(2) - - tris = [] - quads = [] - - for et, nodes_flat in zip(elemTypes, elemNodeTags): - if et == 2: # triangles with 3 nodes - tri_tags = np.asarray(nodes_flat, dtype=np.int64).reshape(-1, 3) - tris.append(self.__map_tags(tri_tags, nodeTags_sorted,order).reshape(-1, 3)) - elif et == 3: # quad with 4 nodes - quad_tags = np.asarray(nodes_flat, dtype=np.int64).reshape(-1, 4) - quads.append(self.__map_tags(quad_tags, nodeTags_sorted,order).reshape(-1, 4)) - - tris = np.vstack(tris) if tris else np.zeros((0, 3), dtype=np.int64) - - if quads: - quads = np.vstack(quads) - # split quad -> 2 tri: (0,1,2) + (0,2,3) - tris = np.vstack([ - tris, - quads[:, [0, 1, 2]], - quads[:, [0, 2, 3]], - ]) + def _createPointCloud(self, levelValue:float): - return MeshPoints, tris + rhoe_pointcloud = np.column_stack((self.__thermodynamic_data[EntropicVars.Density.name], self.__thermodynamic_data[EntropicVars.Energy.name])) + const_z = np.zeros(len(rhoe_pointcloud)) + rhoe_pointcloud_scaled = self._scaler_controlling_variables.transform(rhoe_pointcloud) + cv_pointcloud = np.column_stack((rhoe_pointcloud_scaled, const_z)) + return cv_pointcloud - def __extractHulltags(self, hull_curves:list[int]): - allHullTags = [] - for line in hull_curves: - nodeTags, nodes, _ = gmsh.model.mesh.getNodes(includeBoundary=True,tag=line,dim=1) - nodes = np.asarray(nodes, dtype=float).reshape(-1, 3) - nodeTags = np.asarray(nodeTags, dtype=np.int64) - - allHullTags = np.append(allHullTags, nodeTags) - hullTags = np.unique(np.int64(allHullTags)) - return hullTags - - - def __map_tags(self,tags,nodeTags_sorted,order): - tags = np.asarray(tags, dtype=np.int64).ravel() - pos = np.searchsorted(nodeTags_sorted, tags) - ok = (pos < len(nodeTags_sorted)) & (nodeTags_sorted[pos] == tags) - if not np.all(ok): - missing = np.unique(tags[~ok]) - raise RuntimeError(f"Node tags non trovati in getNodes(): {missing[:20]} (tot missing={len(missing)})") - return order[pos] - - def __evaluateFluidPropertiesOnMesh(self, fluid_data_mesh:np.ndarray[float]): - """Calculate the fluid thermodynamic state variables for the table nodes - - :param fluid_data_mesh: table mesh nodes of density and static energy - :type fluid_data_mesh: np.ndarray[float] - :return: filtered thermodynamic state data at the table nodes - :rtype: np.ndarray[float] - """ - fluid_data_out = fluid_data_mesh.copy() - self.valid_mask = np.zeros(len(fluid_data_mesh),dtype=np.bool) - for i in tqdm(range(len(fluid_data_mesh)),desc="Evaluating fluid properties on table nodes..."): + def _calculateTableStateData(self, cv_table_nodes:np.ndarray[float]): + rhoe_table_nodes = self._scaler_controlling_variables.inverse_transform(cv_table_nodes[:, :2]) + state_data_out = np.zeros([len(rhoe_table_nodes), EntropicVars.N_STATE_VARS.value]) + for i, rhoe in enumerate(rhoe_table_nodes): + self.__datagenerator.UpdateFluid(rhoe[0], rhoe[1]) try: - self._DataGenerator.UpdateFluid(fluid_data_mesh[i, EntropicVars.Density.value], fluid_data_mesh[i, EntropicVars.Energy.value]) - state_vector, correct_phase = self._DataGenerator.GetStateVector() + state_data, correct_phase = self.__datagenerator.GetStateVector() if correct_phase: - fluid_data_out[i, :] = state_vector - self.valid_mask[i] = True - else: - fluid_data_out[i, :] = None + state_data_out[i] = state_data except: - fluid_data_out[i, :] = None - fluid_data_out = fluid_data_out[self.valid_mask,:] - return fluid_data_out + pass - def __CartesianTableData(self): - print("Generating table on Cartesian grid") - Np_rho = self._Config.GetNpDensity() - Np_e = self._Config.GetNpEnergy() - self._DataGenerator.PreprocessData() - if self._Config.GetAutoRange(): - rho_min, rho_max = self._DataGenerator.GetDensityBounds() - e_min, e_max = self._DataGenerator.GetEnergyBounds() - else: - rho_minmax = self._Config.GetDensityBounds() - rho_min = rho_minmax[0] - rho_max = rho_minmax[1] - e_minmax = self._Config.GetEnergyBounds() - e_min = e_minmax[0] - e_max = e_minmax[1] - rho_range = np.linspace(rho_min, rho_max, Np_rho) - e_range = np.linspace(e_min, e_max, Np_e) - self.rho_grid, self.e_grid = np.meshgrid(rho_range, e_range) + return state_data_out - print(f"Grid Configuration:") - print(f" Density: [{rho_min:.2f}, {rho_max:.2f}] kg/m3 ({Np_rho} points)") - print(f" Energy: [{e_min:.0f}, {e_max:.0f}] J/kg ({Np_e} points)") - print(f" Total grid points: {Np_rho * Np_e:,}") - print() - - shape = self.rho_grid.shape - n_points = shape[0] * shape[1] - - # Initialize storage arrays - self.state_data = np.zeros([shape[0], shape[1], EntropicVars.N_STATE_VARS.value]) - - # Validity mask - self.valid_mask = np.zeros(shape, dtype=bool) - - # Flatten for iteration - rho_flat = self.rho_grid.flatten() - e_flat = self.e_grid.flatten() - - success_count = 0 - for i in tqdm(range(n_points), desc="Evaluating"): - rho = rho_flat[i] - e = e_flat[i] - idx_2d = np.unravel_index(i, shape) - try: - self._DataGenerator.UpdateFluid(rho, e) - state_data, correct_phase = self._DataGenerator.GetStateVector() - if correct_phase: - self.state_data[idx_2d[0], idx_2d[1], :] = state_data - success_count += 1 - self.valid_mask[idx_2d] = True - else: - self.state_data[idx_2d[0], idx_2d[1], :] = None - except: - self.state_data[idx_2d[0], idx_2d[1], :] = None - return - - - def __CreateSaturationCurve(self): - rhoe_sat_curve = self._DataGenerator.ComputeSaturationCurve() - [rho_min, rho_max] = self._Config.GetDensityBounds() - [e_min, e_max] = self._Config.GetEnergyBounds() - within_bounds_density = np.logical_and(rhoe_sat_curve[:,0] > rho_min, rhoe_sat_curve[:,0] < rho_max) - within_bounds_energy = np.logical_and(rhoe_sat_curve[:,1] > e_min, rhoe_sat_curve[:,1] < e_max) - within_bounds = np.logical_and(within_bounds_density, within_bounds_energy) - - state_sat_curve = np.zeros([len(rhoe_sat_curve), EntropicVars.N_STATE_VARS.value]) - state_sat_curve[:, EntropicVars.Density.value] = rhoe_sat_curve[:,0] - state_sat_curve[:, EntropicVars.Energy.value] = rhoe_sat_curve[:,1] - - state_sat_curve_norm = self._fluid_data_scaler.transform(state_sat_curve[within_bounds, :]) - - sat_curve_pts_norm = state_sat_curve_norm[:, [EntropicVars.Density.value,EntropicVars.Energy.value]] - return sat_curve_pts_norm - - - - def __generateCoarseTable(self): - if self.__initiate_from_pointcloud: - with open(self.__initial_solution_filename,'r') as fid: - variables_in_header = fid.readline().strip().split(',') - variables_in_header = [v.strip("\"") for v in variables_in_header] - pointCloudData = np.loadtxt(self.__initial_solution_filename,delimiter=',',skiprows=1)[::10, :] - rhoe_pointCloud = pointCloudData[:, [variables_in_header.index(EntropicVars.Density.name),\ - variables_in_header.index(EntropicVars.Energy.name)]] - rhoe_min, rhoe_max = np.min(rhoe_pointCloud, axis=0), np.max(rhoe_pointCloud,axis=0) - rho_min = rhoe_min[0]*0.9 - rho_max = 1.1*rhoe_max[0] - e_min = rhoe_min[1]*0.99 - e_max = 1.01*rhoe_max[1] - - self._Config.SetDensityBounds(rho_min, rho_max) - self._Config.SetEnergyBounds(e_min, e_max) - - self.__CartesianTableData() - self._data_in_table = np.column_stack(tuple(self.state_data[:,:,i][self.valid_mask].flatten() for i in range(EntropicVars.N_STATE_VARS.value))) - return - - def __prepareInterpolator(self): - fluid_data_normalized = self._fluid_data_scaler.transform(self._data_in_table) - controlling_variable_data = fluid_data_normalized[:, [EntropicVars.Density.value, EntropicVars.Energy.value]] - self._lookup_tree = Invdisttree(controlling_variable_data, self._data_in_table) - return - - def __interpolateFluidData(self, density:float, staticEnergy:float): - cv_data = np.zeros([1, EntropicVars.N_STATE_VARS.value]) - cv_data[0, EntropicVars.Density.value] = density - cv_data[0, EntropicVars.Energy.value] = staticEnergy - cv_data_norm = self._fluid_data_scaler.transform(cv_data) - rhoe_norm = cv_data_norm[0, [EntropicVars.Density.value, EntropicVars.Energy.value]] - return self._lookup_tree(rhoe_norm) - - def WriteOutParaview(self,file_name_out:str="vtk_table"): - """ - write a file containing all the LuT data that can be opened with Paraview - - :param file_name_out: string indicating the name and extension of the saved file - """ - - #x, y = self._data_in_table[:, EntropicVars.Density.value], self._data_in_table[:, EntropicVars.Energy.value] - table_data_norm = self._fluid_data_scaler.transform(self._data_in_table) - x, y = table_data_norm[:, EntropicVars.Density.value], table_data_norm[:, EntropicVars.Energy.value] - # scale_x= self._fluid_data_scaler.data_max_[EntropicVars.Density.value] - self._fluid_data_scaler.data_min_[EntropicVars.Density.value] - # scale_y= self._fluid_data_scaler.data_max_[EntropicVars.Energy.value] - self._fluid_data_scaler.data_min_[EntropicVars.Energy.value] - - pts = np.column_stack([x, y, np.zeros_like(x)]) # z=0 - - conn = np.asarray(self._table_connectivity, dtype=np.int64) - if conn.min() == 1: - conn = conn - 1 - - point_data = {} - for var in self._table_vars: - ivar = EntropicVars[var].value - point_data[var] = np.asarray(self._data_in_table[:, ivar]) - - mesh = meshio.Mesh( - points=pts, - cells=[("triangle", conn)], - point_data=point_data - ) - mesh.write("%s.vtk" % file_name_out) - - return - - # def AddRefinementCriterion(self, TD_variable:str, norm_val_min:float=np.inf, norm_val_max:float=-np.inf): - # """Apply refinement in the table where the normalized value of the thermodynamic variable lies between the specified bounds. - - # :param TD_variable: name of the thermodynamic variable for which to apply refinement - # :type TD_variable: str - # :param norm_val_min: lower bound of the normalized thermodynamic variable, defaults to np.inf - # :type norm_val_min: float, optional - # :param norm_val_max: upper bound of the normalized thermodynamic variable, defaults to -np.inf - # :type norm_val_max: float, optional - # :raises Exception: if thermodynamic state variable is unknown to SU2 DataMiner - # """ - # if TD_variable not in self._table_vars: - # raise Exception("%s is not present in fluid data" % TD_variable) - - # self.__refinement_vars.append(TD_variable) - # self.__refinement_norm_min.append(norm_val_min) - # self.__refinement_norm_max.append(norm_val_max) - # return - - def __ApplyRefinement(self, fluid_data_norm_ref:np.ndarray[float]): - ix_ref = np.array([],dtype=np.int64) - fluid_vars = [a.name for a in EntropicVars][:-1] - fluid_data_inv = self._fluid_data_scaler.inverse_transform(fluid_data_norm_ref) - for TD_var, val_min, val_max in zip(self.__refinement_vars, self.__refinement_norm_min, self.__refinement_norm_max): - norm_data_var = fluid_data_inv[:, fluid_vars.index(TD_var)] - - ix = np.argwhere(np.logical_and(norm_data_var>=val_min, norm_data_var<=val_max))[:,0] - ix_ref = np.append(ix_ref, ix) - if len(ix_ref) > 0: - return np.unique(ix_ref) - else: - return [] - - - def WriteTableFile(self, output_filepath:str=None): - """ - Save the table data and connectivity as a Dragon library file. If no file name is provided, the table file will be named according to the Config_FGM class name. - - :param output_filepath: optional output filepath for table file. - :type output_filepath: str - """ - - if output_filepath: - file_out = output_filepath + ".drg" + def _writeAdditionalInfoToTable(self, fid): + fid.write("Fluid:\n") + fid.write("%s\n" % self._Config.GetFluid()) + fid.write("Equation of state:\n") + fid.write("%s\n" % self._Config.GetEquationOfState()) + if self._Config.TwoPhase(): + fid.write("Table contains two-phase data\n\n") else: - file_out = self._savedir + "/LUT_"+self._Config.GetConfigName()+".drg" - - print("Writing LUT file with name " + file_out) - fid = open(file_out, "w+") - fid.write("Dragon library\n\n") - fid.write("
\n\n") - fid.write("[Version]\n1.0.1\n\n") - - fid.write("[Number of points]\n") - fid.write("%i\n" % np.shape(self._data_in_table)[0]) - fid.write("\n") - - fid.write("[Number of triangles]\n") - fid.write("%i\n" % np.shape(self._table_connectivity)[0]) - fid.write("\n") - - fid.write("[Number of hull points]\n") - fid.write("%i\n" % np.shape(self._table_hullnodes)[0]) - fid.write("\n") - - fid.write("[Number of variables]\n%i\n\n" % (len(self._table_vars))) - fid.write("[Variable names]\n") - for iVar, Var in enumerate(self._table_vars): - fid.write(str(iVar + 1)+":"+Var+"\n") - fid.write("\n") - - fid.write("
\n\n") - - print("Writing table data...") - fid.write("\n") - for iNode in range(len(self._data_in_table)): - for var in self._table_vars: - ivar = EntropicVars[var].value - fid.write("\t%+.14e" % self._data_in_table[iNode, ivar]) - fid.write("\n") - fid.write("\n\n") - print("Done!") - - print("Writing table connectivity...") - fid.write("\n") - for iCell in range(len(self._table_connectivity)): - fid.write("\t".join("%i" % c for c in self._table_connectivity[iCell, :]+1) + "\n") - fid.write("\n\n") - print("Done!") - - print("Writing hull nodes...") - fid.write("\n") - for iCell in range(len(self._table_hullnodes)): - fid.write(("%i" % (self._table_hullnodes[iCell]+1)) + "\n") - fid.write("\n\n") - print("Done!") - - fid.close() - - return + fid.write("Table constains single-phase data\n\n") + return + +class TableGenerator_FGM(SU2TableGenerator_Base): + _Config:Config_FGM = None + + def __init__(self, config_in:Config_FGM): + super().__init__(config_in) + self._getFluidDataPointCloud() + return + + def _getFluidDataPointCloud(self): + flamelet_data_filename = os.sep.join((self._Config.GetOutputDir(), self._Config.GetConcatenationFileHeader()+"_full.csv")) + flameletDataPointCloud = pd.read_csv(flamelet_data_filename) + self._state_quantities = list(flameletDataPointCloud.keys()) + self._table_vars = self._state_quantities.copy() + return flameletDataPointCloud + + def _createPointCloud(self, levelValue:float): + + self._Config.gas.set_mixture_fraction(levelValue, self._Config.GetFuelString(),self._Config.GetOxidizerString()) + self._Config.gas.TP=self._Config.GetUnbTempBounds()[0],DefaultSettings_FGM.pressure + h_min_unb = self._Config.gas.enthalpy_mass + + # Compute reactant progress variable for the current mixture fraction. + pv_unb = self._Config.ComputeProgressVariable(variables=None, flamelet_data=None, Y_flamelet=self._Config.gas.Y[:,np.newaxis])[0] + + # Define maximum enthalpy as the reactant enthalpy at the maximum reactant temperature. + self._Config.gas.TP=self._Config.GetUnbTempBounds()[1],DefaultSettings_FGM.pressure + h_max = self._Config.gas.enthalpy_mass + + # Equilibrate at constant enthalpy to get product progress variable value. + self._Config.gas.equilibrate("TP") + pv_b = self._Config.ComputeProgressVariable(variables=None, flamelet_data=None, Y_flamelet=self._Config.gas.Y[:,np.newaxis])[0] + + # Define minimum enthalpy as the product enthalpy cooled to minimum reactant temperature. + self._Config.gas.TP=self._Config.GetUnbTempBounds()[0],DefaultSettings_FGM.pressure + h_min = self._Config.gas.enthalpy_mass + + # Define 2D grid between minimum and maximum progress variable and total enthalpy + pv_range = np.linspace(pv_unb, pv_b, 100) + h_range = np.linspace(h_min, h_max, 100) + xgrid, ygrid = np.meshgrid(pv_range, h_range) + zgrid = levelValue*np.ones(np.shape(xgrid)) + + # 2: Locate nodes that are above the burner-stabilized enthalpy line + CV_grid_init = np.vstack((xgrid.flatten(), ygrid.flatten(), zgrid.flatten())).transpose() + pv_grid = CV_grid_init[:,0] + h_grid = CV_grid_init[:,1] + + h_limit = ((h_min_unb - h_min) * pv_grid + (h_min*pv_unb - h_min_unb*pv_b))/(pv_unb - pv_b) + idx_keep = h_grid >= h_limit + + cv_pointcloud = CV_grid_init[idx_keep, :] + cv_pointcloud_scaled = self._scaler_controlling_variables.transform(cv_pointcloud) + return cv_pointcloud_scaled + + def _writeAdditionalInfoToTable(self, fid): + fid.write("Fuel:\n") + fid.write(",".join(["%.2f:%s" % (w, sp) for w, sp in zip(self._Config.GetFuelWeights(), self._Config.GetFuelDefinition())])) + fid.write("\nOxidizer:\n") + fid.write(",".join(["%.2f:%s" % (w, sp) for w, sp in zip(self._Config.GetOxidizerWeights(), self._Config.GetOxidizerDefinition())])) + fid.write("\nProgress variable:\n") + fid.write("+".join(["(%+.3e*%s)" % (w, sp) for w, sp in zip(self._Config.GetProgressVariableWeights(), self._Config.GetProgressVariableSpecies())])) + fid.write("\n\n") + return \ No newline at end of file From 76ac57dcdbafbd3a5b12cc1b6548e585f891a53e Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Fri, 29 May 2026 11:07:44 +0200 Subject: [PATCH 07/44] fix whitespaces --- Common/Interpolators.py | 27 ++-- .../LUT/FlameletTableGeneration.py | 143 +----------------- Manifold_Generation/LUT/LUTGenerator_Base.py | 84 +++++----- Manifold_Generation/LUT/LUTGenerators.py | 24 +-- Manifold_Generation/LUT/MeshTools.py | 76 +++++----- 5 files changed, 106 insertions(+), 248 deletions(-) diff --git a/Common/Interpolators.py b/Common/Interpolators.py index e42f7fa..0b09acc 100644 --- a/Common/Interpolators.py +++ b/Common/Interpolators.py @@ -27,7 +27,6 @@ import numpy as np import pandas as pd from tqdm import tqdm -import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler class Invdisttree: """ inverse-distance-weighted interpolation using KDTree: @@ -80,13 +79,13 @@ class Invdisttree: """ # anykernel( dj / av dj ) is also scale-free # error analysis, |f(x) - idw(x)| ? todo: regular grid, nnear ndim+1, 2*ndim - __state_space_data:np.ndarray[float]=None + __state_space_data:np.ndarray[float]=None __nDim_input:int=None __number_of_states:int=None - __KD_tree:KDTree = None - __single_query_sample:bool=False - __number_of_nearest_neighbors:int=None - __inverse_distance_exponent:float=None + __KD_tree:KDTree = None + __single_query_sample:bool=False + __number_of_nearest_neighbors:int=None + __inverse_distance_exponent:float=None def __init__( self, samples_state_space:np.ndarray[float], state_data:np.ndarray[float], leafsize:int=10): assert len(samples_state_space) == len(state_data), "len(X) %d != len(z) %d" % (len(samples_state_space), len(state_data)) @@ -132,7 +131,7 @@ def __format_query_input(self, unformatted_query_samples:np.ndarray[float]): def __check_correct_input_dimensions(self, query_samples:np.ndarray[float]): if query_samples.shape[1] != self.__nDim_input: raise Exception("Incorrect input dimensionality") - return + return def __interpolate_from_inverse_distance(self, query_input_samples:np.ndarray[float]): distances, nearest_neighbor_indices = self.__evaluate_KD_tree(query_input_samples, self.__number_of_nearest_neighbors) @@ -159,7 +158,7 @@ def __interpolate_from_nearest_neighbors(self, nearest_neighbor_indices:np.ndarr nearest_neighbor_coefficients /= np.sum(nearest_neighbor_coefficients) interpolated_state_data[j] = np.dot( nearest_neighbor_coefficients, self.__state_space_data[ix]) - return interpolated_state_data + return interpolated_state_data def __single_nearest_neighbor(self): return self.__number_of_nearest_neighbors==1 @@ -205,10 +204,10 @@ def query_with_local_parameters( self, q:np.ndarray[float], nnear_vals:np.ndarra class fluidDataInterpolator: - __controlling_variable_nodes:np.ndarray[float] = None - __lookup_data:np.ndarray[float] = None - __lookup_tree:Invdisttree = None - __n_near_single:int = 6 + __controlling_variable_nodes:np.ndarray[float] = None + __lookup_data:np.ndarray[float] = None + __lookup_tree:Invdisttree = None + __n_near_single:int = 6 __p_fac_single:int = 2 __state_vars:list[str] = None @@ -223,7 +222,7 @@ def __init__(self,cv_data:np.ndarray[float], state_data:pd.DataFrame, number_of_ if not self.__n_near_single or not self.__p_fac_single: self.tuneTreeParameters() - return + return def tuneTreeParameters(self): @@ -262,7 +261,7 @@ def tuneTreeParameters(self): # ax.plot3D(cv_data_test[:,0], cv_data_test[:,1], lookup_data_test[:, 2], 'k.') # ax.plot3D(cv_data_test[:,0], cv_data_test[:,1], self.__lookup_data[:, 2], 'r.') # plt.show() - return + return def __call__(self, cv_data_query:np.ndarray[float],varnames:list[str]=[]): ndim_input = cv_data_query.ndim diff --git a/Manifold_Generation/LUT/FlameletTableGeneration.py b/Manifold_Generation/LUT/FlameletTableGeneration.py index 3934236..52bf795 100644 --- a/Manifold_Generation/LUT/FlameletTableGeneration.py +++ b/Manifold_Generation/LUT/FlameletTableGeneration.py @@ -29,154 +29,13 @@ from tqdm import tqdm import sys,os from Common.DataDrivenConfig import Config_FGM, Config -from Common.CommonMethods import GetReferenceData from Common.Properties import DefaultSettings_FGM import gmsh import pickle from multiprocessing import Pool from Common.Interpolators import Invdisttree -class SU2TableGenerator_Base: - _Config = None - _savedir:str - _table_variables:list[str] = None - _manifold_variables:list[str] - _controlling_variables:list[str] = None - _manifold_data:np.ndarray[float] = None - _manifold_data_interpolator:Invdisttree = None - _base_cell_size:float = 1e-2#3.7e-3 # Table level base cell size. - - _refined_cell_size:float = 1e-3#2.5e-3#1.5e-3 # Table level refined cell size. - _refinement_radius:float = 5e-3#5e-2 # Table level radius within which refinement is applied. - _curvature_threshold:float = 0.3 # Curvature threshold above which refinement is applied. - _n_near:int = 4 # Number of nearest neighbors from which to evaluate flamelet data. - _p_fac:int = 5 # Power by which to weigh distances from query point. - _control_var_scaler:MinMaxScaler =None - _table_nodes = [] # Progress variable, total enthalpy, and mixture fraction node values for each table level. - _table_nodes_norm = [] # Normalized table nodes for each level. - _table_connectivity = [] # Table node connectivity per table level. - _table_hullnodes = [] # Hull node indices per table level. - - def __init__(self, Config_in): - self._Config = Config_in - self._savedir = self._Config.GetOutputDir() - return - - def SetSaveDir(self, save_dir:str): - if not os.path.isdir(save_dir): - raise Exception("Output directory %s not present on current hardware." % save_dir) - self._savedir = save_dir - return - - def SetBaseCellSize(self, cell_size:float): - """ - Define the base cell size for the table levels. - - :param cell_size: Normalized coarse cell size for each 2D table mesh. - :type cell_size: float - :raise: Exception: if cell size is lower or equal to zero. - """ - if cell_size > 0: - self._base_cell_size = cell_size - else: - raise Exception("Proviced cell size should be higher than zero.") - return - - def SetRefinedCellSize(self, cell_size:float): - """ - Define the refinement cell size for the table levels. - - :param cell_size: Normalized fine cell size for each 2D table mesh. - :type cell_size: float - :raise: Exception: if cell size is lower or equal to zero. - """ - if cell_size > 0: - self._refined_cell_size = cell_size - else: - raise Exception("Proviced cell size should be higher than zero.") - return - - def SetRefinementThreshold(self, val_threshold:float): - """ - Define normalized curvature threshold beyond which refinement should be applied to each table level. - - :param val_threshold: Normalized curvature threshold value. All locations in the mesh with a higher curvature receive refinement. - :type val_threshold: float - :raises: Exception: If the threshold value is lower than zero. - """ - - if val_threshold > 0: - self._curvature_threshold = val_threshold - else: - raise Exception("Curvature threshold value should be higher than zero.") - return - - - def DefineFlameletDataInterpolator(self): - - print("Configuring KD-tree for most accurate lookups") - - print("Loading flamelet data...") - # Define scaler for FGM controlling variables. - full_data_file = self._Config.GetOutputDir()+"/LUT_data_full.csv" - with open(full_data_file,'r') as fid: - self._manifold_variables = fid.readline().strip().split(',') - #D_full = np.loadtxt(full_data_file,delimiter=',',skiprows=1) - self._control_var_scaler = MinMaxScaler() - CV_full, D_full = GetReferenceData(full_data_file, self._controlling_variables, self._manifold_variables) - data_scaler = MinMaxScaler() - data_scaler.fit_transform(D_full) - - CV_full_scaled = self._control_var_scaler.fit_transform(CV_full) - - # Exctract train and test data - train_data_file = self._Config.GetOutputDir()+"/"+self._Config.GetConcatenationFileHeader()+"_train.csv" - test_data_file = self._Config.GetOutputDir()+"/"+self._Config.GetConcatenationFileHeader()+"_test.csv" - - CV_train, D_train = GetReferenceData(train_data_file, self._controlling_variables, self._manifold_variables) - CV_test, D_test = GetReferenceData(test_data_file, self._controlling_variables, self._manifold_variables) - - CV_train_scaled = self._control_var_scaler.transform(CV_train) - CV_test_scaled = self._control_var_scaler.transform(CV_test) - D_train_scaled = data_scaler.transform(D_train) - D_test_scaled = data_scaler.transform(D_test) - - print("Done!") - print("Setting up KD-tree...") - self._lookup_tree = Invdisttree(X=CV_train_scaled,z=D_train_scaled) - print("Done!") - - print("Search for best tree parameters...") - # Do brute-force search to get the optimum number of nearest neighbors and distance power. - n_near_range = range(1, 20) - p_range = range(1, 6) - RMS_ppv = np.zeros([len(n_near_range), len(p_range)]) - for i in tqdm(range(len(n_near_range))): - for j in range(len(p_range)): - PPV_predicted = self._lookup_tree(q=CV_test_scaled, nnear=n_near_range[i], p=p_range[j]) - rms_local = np.average(np.power(PPV_predicted - D_test_scaled, 2)) - RMS_ppv[i,j] = rms_local - [imin,jmin] = divmod(RMS_ppv.argmin(), RMS_ppv.shape[1]) - self._n_near = n_near_range[imin] - self._p_fac = p_range[jmin] - print("Done!") - print("Best found number of nearest neighbors: "+str(self._n_near)) - print("Best found distance power: "+str(self._p_fac)) - print("Setting up KD-tree...") - self._lookup_tree = Invdisttree(X=CV_full_scaled,z=D_full) - print("Done!") - - return - - def EvaluateManifoldInterpolator(self, CV_unscaled:np.ndarray): - CV_scaled = self._control_var_scaler.transform(CV_unscaled) - data_interp = self._lookup_tree(q=CV_scaled,nnear=self._n_near,p=self._p_fac) - return data_interp - - def Compute2DTable(self, CV_1:str, CV_2:str): - - return - + class SU2TableGenerator: _Config:Config_FGM = None # Config_FGM class from which to read settings. diff --git a/Manifold_Generation/LUT/LUTGenerator_Base.py b/Manifold_Generation/LUT/LUTGenerator_Base.py index 02dd4aa..450322a 100644 --- a/Manifold_Generation/LUT/LUTGenerator_Base.py +++ b/Manifold_Generation/LUT/LUTGenerator_Base.py @@ -23,13 +23,13 @@ # | #=============================================================================================# import numpy as np -import pandas as pd -import meshio +import pandas as pd +import meshio import pandas from os import sep from sklearn.preprocessing import MinMaxScaler from multiprocessing import Pool -from scipy.interpolate import RBFInterpolator +from scipy.interpolate import RBFInterpolator from Common.Interpolators import fluidDataInterpolator from Common.DataDrivenConfig import Config @@ -38,9 +38,9 @@ class SU2TableGenerator_Base: - _Config:Config = None + _Config:Config = None _nDim_table:int=2 - _base_cell_size:float = 2e-2 + _base_cell_size:float = 2e-2 _target_number_of_nodes:int = None _table_vars:list[str] = [] @@ -56,13 +56,13 @@ class SU2TableGenerator_Base: _conditional_refinement_factor:list[float] = [] # Refinement factors to apply within the bounds _scaler_controlling_variables:MinMaxScaler = MinMaxScaler() - _fluid_data_interpolator:fluidDataInterpolator = None + _fluid_data_interpolator:fluidDataInterpolator = None - __smoothTableData:bool = False - __smoothingLevel:float = 0.1 + __smoothTableData:bool = False + __smoothingLevel:float = 0.1 - __N_nearest_neighbors:int = None - __inverse_distance_exponent:float = None + __N_nearest_neighbors:int = None + __inverse_distance_exponent:float = None _N_table_levels:int = None _table_levels:np.ndarray[float] = [] @@ -70,7 +70,7 @@ class SU2TableGenerator_Base: _tableLowerLevel:float = None _table_level_inserts:list[float] = [] - __run_parallel:bool = False + __run_parallel:bool = False __N_cores:int = 1 _state_quantities:list[str] = [] @@ -80,19 +80,19 @@ class SU2TableGenerator_Base: _verbosity:int=1 def __init__(self, config_in:Config): - self._Config = config_in + self._Config = config_in self._nDim_table = len(self._Config.GetControllingVariables()) - return + return def defineInterpolator(self): stateDataFrame = self._getFluidDataPointCloud() cv_data = np.column_stack(tuple(stateDataFrame[cv] for cv in self._Config.GetControllingVariables())) cv_data_scaled = self._scaler_controlling_variables.fit_transform(cv_data) self._fluid_data_interpolator = fluidDataInterpolator(cv_data_scaled, stateDataFrame, self.__N_nearest_neighbors, self.__inverse_distance_exponent) - return + return def _getFluidDataPointCloud(self): - return + return def setMaximumCellSize(self, cell_size_coarse:float=1e-2): """Specify the coarse level cell size of the table @@ -106,7 +106,7 @@ def setMaximumCellSize(self, cell_size_coarse:float=1e-2): def setTargetNodeCount(self, target_node_count:int=3000): self._target_number_of_nodes = target_node_count - return + return def addRefinementCriterion(self, varname:str, lowerbound:float=-np.inf, upperbound:float=np.inf, coef:float=0.5): """Specify conditional refinement based on interpolated thermochemical state data. Cell sizes are reduced by a factor "coef" where the table data lies between the specified lower bound and upper bound (inclusive) @@ -168,11 +168,11 @@ def generateTable(self): if self.__smoothTableData: self.smoothTableLevelData() - return + return def __preprocessTableSettings(self): self.__processTableLevels() - return + return def meshTableLevel(self, level_index:int): pointCloud = self._createPointCloud(self._table_levels[level_index]) @@ -224,7 +224,7 @@ def __extractTableLevelData(self, mesher:Mesh2DPlane): def _createPointCloud(self, levelValue:float): - return + return def _initiateMesher(self): return Mesh2DPlane() @@ -234,7 +234,7 @@ def _passRefinementOptions(self, mesher:Mesh2DPlane): if self._target_number_of_nodes: mesher.setTargetNodeCount(self._target_number_of_nodes) mesher.setRefinementFunction(self._refinelocation) - return + return def _calculateTableStateData(self, cv_table_nodes:np.ndarray[float]): return self._fluid_data_interpolator(cv_table_nodes) @@ -267,10 +267,10 @@ def setTableVars(self, table_vars_in:list[str]): self._table_vars.append(user_var) else: raise Exception("%s is not supported for table output variables") - return + return def _checkIfVariableIsValid(self, var_to_check:str): - return True + return True def writeParaviewTable(self, filepath_out:str=None): @@ -286,7 +286,7 @@ def writeParaviewTable(self, filepath_out:str=None): self.__writeParaViewVTK(cv_nodes_scaled, self._data_in_table[iLevel], self._table_connectivity[iLevel], table_level_filename) self.__printmsg("Done") - return + return def __writeParaViewVTK(self, cv_coordinates:np.ndarray[float], table_data:pandas.DataFrame, connectivity:np.ndarray[int], file_name_out:str): @@ -322,7 +322,7 @@ def smoothTableLevelData(self): if var not in self._Config.GetControllingVariables(): self._data_in_table[iLevel][var] = smoothened_table_data[:, ivar] self.__printmsg("Done") - return + return def writeSU2Table(self, filepath_out:str=None): @@ -416,59 +416,59 @@ def writeSU2Table(self, filepath_out:str=None): def setTableLevels(self, level_values:np.ndarray[float]): if self.__optionAppliesFor3D(): self._table_levels = level_values - return + return def setTableLimits(self, lower_limit:float, upper_limit:float): if self.__optionAppliesFor3D(): if lower_limit >= upper_limit: raise Exception("Table upper level value should exceed lower level value") self._tableLowerLevel = lower_limit - self._tableUpperLevel = upper_limit - return + self._tableUpperLevel = upper_limit + return def setNTableLevels(self, N_levels:int=10): if self.__optionAppliesFor3D(): if N_levels <= 0: raise Exception("Number of table levels should be positive") self._N_table_levels = N_levels - return + return def insertTableLevel(self, level_value:float): if self.__optionAppliesFor3D(): self._table_level_inserts.append(level_value) - return + return def setNProcessors(self, N_cores:int=2): if self.__optionAppliesFor3D(): if N_cores <= 1: raise Exception("At least two cores should be used") - self.__run_parallel = True - self.__N_cores = N_cores - return + self.__run_parallel = True + self.__N_cores = N_cores + return def setNNearestNeighbors(self, N_input:int=6): if N_input < 1: raise Exception("Number of nearest neighbors should be strictly positive") self.__N_nearest_neighbors = N_input - return + return def setInverseDistanceExponent(self, p_factor:float=2): if p_factor <= 0: raise Exception("Inverse distance parameter value should be positive") self.__inverse_distance_exponent = p_factor - return + return def setSmoothingParameter(self, smoothing_factor:float=0): - self.__smoothTableData = True - self.__smoothingLevel = smoothing_factor - return + self.__smoothTableData = True + self.__smoothingLevel = smoothing_factor + return def __optionAppliesFor3D(self): if self.__is2D(): print("Option does not apply for two-dimensional table, ignoring") return False else: - return True + return True def __is2D(self): return self._nDim_table==2 @@ -496,16 +496,16 @@ def __processTableLevels(self): else: self._table_levels = [0] self._N_table_levels = 1 - return + return def __printmsg(self, msg:str): if self._verbosity > 0: print(msg) - return + return def setVerbosity(self, verbosity_level:int=1): if verbosity_level < 0 or verbosity_level > 4: raise Exception("Verbosity level should be between 0 and 4") - self._verbosity = verbosity_level - return + self._verbosity = verbosity_level + return \ No newline at end of file diff --git a/Manifold_Generation/LUT/LUTGenerators.py b/Manifold_Generation/LUT/LUTGenerators.py index f02d568..fa9b12d 100644 --- a/Manifold_Generation/LUT/LUTGenerators.py +++ b/Manifold_Generation/LUT/LUTGenerators.py @@ -32,9 +32,9 @@ from Manifold_Generation.LUT.MeshTools import MeshThermodynamicPlane class TableGenerator_NICFD(SU2TableGenerator_Base): - _Config:Config_NICFD = None - __datagenerator:DataGenerator_CoolProp = None - __thermodynamic_data:pd.DataFrame = None + _Config:Config_NICFD = None + __datagenerator:DataGenerator_CoolProp = None + __thermodynamic_data:pd.DataFrame = None def __init__(self, config_in:Config_NICFD): self._state_quantities = [q.name for q in EntropicVars] @@ -42,7 +42,7 @@ def __init__(self, config_in:Config_NICFD): self.__datagenerator = DataGenerator_CoolProp(self._Config) self.__datagenerator.SetFDStepSizes(3e-3,3e-3) self.__defaultTableVariables() - return + return def __defaultTableVariables(self): vars_to_exclude = [EntropicVars.N_STATE_VARS.name] @@ -55,7 +55,7 @@ def __defaultTableVariables(self): for var in EntropicVars: if var.name not in vars_to_exclude: self._table_vars.append(var.name) - return + return def setTableVars(self, table_vars_in:list[str]): if self._Config.TwoPhase() and EntropicVars.VaporQuality.name in table_vars_in: @@ -86,7 +86,7 @@ def setTableVars(self, table_vars_in:list[str]): def _checkIfVariableIsValid(self, var_to_check:str): if var_to_check in self._state_quantities: - return True + return True else: return False @@ -112,7 +112,7 @@ def _getFluidDataPointCloud(self): self.__thermodynamic_data = state_dataFrame - return state_dataFrame + return state_dataFrame def _createPointCloud(self, levelValue:float): @@ -132,7 +132,7 @@ def _calculateTableStateData(self, cv_table_nodes:np.ndarray[float]): if correct_phase: state_data_out[i] = state_data except: - pass + pass return state_data_out @@ -145,10 +145,10 @@ def _writeAdditionalInfoToTable(self, fid): fid.write("Table contains two-phase data\n\n") else: fid.write("Table constains single-phase data\n\n") - return + return class TableGenerator_FGM(SU2TableGenerator_Base): - _Config:Config_FGM = None + _Config:Config_FGM = None def __init__(self, config_in:Config_FGM): super().__init__(config_in) @@ -199,7 +199,7 @@ def _createPointCloud(self, levelValue:float): cv_pointcloud = CV_grid_init[idx_keep, :] cv_pointcloud_scaled = self._scaler_controlling_variables.transform(cv_pointcloud) - return cv_pointcloud_scaled + return cv_pointcloud_scaled def _writeAdditionalInfoToTable(self, fid): fid.write("Fuel:\n") @@ -209,4 +209,4 @@ def _writeAdditionalInfoToTable(self, fid): fid.write("\nProgress variable:\n") fid.write("+".join(["(%+.3e*%s)" % (w, sp) for w, sp in zip(self._Config.GetProgressVariableWeights(), self._Config.GetProgressVariableSpecies())])) fid.write("\n\n") - return \ No newline at end of file + return \ No newline at end of file diff --git a/Manifold_Generation/LUT/MeshTools.py b/Manifold_Generation/LUT/MeshTools.py index 167c738..b3308e2 100644 --- a/Manifold_Generation/LUT/MeshTools.py +++ b/Manifold_Generation/LUT/MeshTools.py @@ -1,38 +1,38 @@ -import numpy as np -import gmsh +import numpy as np +import gmsh from concave_hull import concave_hull, concave_hull_indexes -from Common.CommonMethods import shoelace, FiniteDifferenceDerivative +from Common.CommonMethods import shoelace, FiniteDifferenceDerivative def default_refinement_function(this, x:float, y:float, z:float): return 1.0 class Mesh2DPlane: - __pointCloud:np.ndarray[float] = None - _pointCloud_hullNodes:np.ndarray[float] = None + __pointCloud:np.ndarray[float] = None + _pointCloud_hullNodes:np.ndarray[float] = None __mesh_along_coords:list[int] = [0, 1] - _gmsh_geo:gmsh.model.geo = None - _gmsh_mesher:gmsh.model.mesh = None - _gmsh_verbosity:int = 0 + _gmsh_geo:gmsh.model.geo = None + _gmsh_mesher:gmsh.model.mesh = None + _gmsh_verbosity:int = 0 _mesher_verbosity:int = 1 _base_cell_size:float = 2e-2 refinement_function = default_refinement_function - __plane_area:float = None + __plane_area:float = None __perimiter_tag:int=None __use_target_node_count:bool = False __target_node_count:int=3000 - __meshNodes:np.ndarray[float] = None - __connectivity:np.ndarray[int] = None - __hullNodeIDs:np.ndarray[int] = None + __meshNodes:np.ndarray[float] = None + __connectivity:np.ndarray[int] = None + __hullNodeIDs:np.ndarray[int] = None def __init__(self): - return + return def generateMesh(self): self.__checkPlanarCoords() @@ -45,7 +45,7 @@ def generateMesh(self): self.__meshNodes, self.__connectivity, self.__hullNodeIDs = self.__mesh2D() self._gmsh_geo.synchronize() - return + return def __optimizeMeshResolution(self): self.__printMessage("Refining table based on target node count") @@ -74,7 +74,7 @@ def __optimizeMeshResolution(self): if iter == niter_max: self.__printMessage("Target node count was not reached within %i iterations" % niter_max) - return + return def __createGeometry(self): @@ -82,7 +82,7 @@ def __createGeometry(self): curvloop_table_outline, perimiter_tags = self.__createHullPerimiter() self._gmsh_geo.synchronize() self._createInternalGeometry(curvloop_table_outline, perimiter_tags) - return + return def __mesh2D(self): @@ -106,7 +106,7 @@ def __checkPlanarCoords(self): equal_Z_coords = np.all(self.__pointCloud[:, const_dim]==self.__pointCloud[0, const_dim]) if not equal_Z_coords: raise Exception("Point cloud does not contain planar coordinates") - return + return def __createHullPerimiter(self): self._pointCloud_hullNodes = self.__findHullNodes() @@ -140,7 +140,7 @@ def __createHullPointEntities(self, coords_hull_pts:np.ndarray[float]): hull_pts = [] for i in range(int(len(coords_hull_pts))): hull_pts.append(self._gmsh_geo.addPoint(coords_hull_pts[i, 0], coords_hull_pts[i, 1], coords_hull_pts[i, 2])) - return hull_pts + return hull_pts def __connectHullPoints(self, pointIDs:list[int]): hull_lines = [] @@ -152,7 +152,7 @@ def __connectHullPoints(self, pointIDs:list[int]): def setInitialPointCloud(self, point_could_3D:np.ndarray[float]): self.__pointCloud = self.__filterNansFromPointCloud(point_could_3D) - return + return def __filterNansFromPointCloud(self, point_cloud_3D:np.ndarray[float]): unique_pts_init = np.unique(point_cloud_3D, axis=0) @@ -165,7 +165,7 @@ def _createInternalGeometry(self,curvloop_perimiter:int, perimiter_tags:list[int planeID = self._gmsh_geo.addPlaneSurface([curvloop_perimiter]) self._gmsh_geo.addPhysicalGroup(2, [planeID]) - return + return def __extractNodesTriangles(self): nodeTags, coords, _ = self._gmsh_mesher.getNodes() @@ -222,7 +222,7 @@ def setBaseCellSize(self, cell_size_input:float): if cell_size_input <= 0: raise Exception("Cell size should be strictly positive") self._base_cell_size = cell_size_input - return + return def _initializeGmesh(self): gmsh.initialize() @@ -231,9 +231,9 @@ def _initializeGmesh(self): gmsh.option.setNumber("Mesh.MeshSizeFromPoints",0) gmsh.option.setNumber("Mesh.MeshSizeExtendFromBoundary",0) - self._gmsh_geo = gmsh.model.geo - self._gmsh_mesher = gmsh.model.mesh - return + self._gmsh_geo = gmsh.model.geo + self._gmsh_mesher = gmsh.model.mesh + return def __prepareMesher(self): self._gmsh_geo.synchronize() @@ -245,14 +245,14 @@ def meshSizeCallback(dim,tag,x,y,z,lc): return self._base_cell_size * refinement_factor self._gmsh_mesher.setSizeCallback(meshSizeCallback) - return + return def setRefinementFunction(self, function_input): self.refinement_function = function_input - return + return def getGmshFactory(self): - return self._gmsh_geo + return self._gmsh_geo def getGmshMesher(self): return self._gmsh_mesher @@ -261,11 +261,11 @@ def setTargetNodeCount(self, Np_target:int=3000): if Np_target <= 0: raise Exception("Target number of nodes should be positive") self.__target_node_count = Np_target - self.__use_target_node_count = True - return + self.__use_target_node_count = True + return def getMeshNodes(self): - return self.__meshNodes + return self.__meshNodes def getConnectivity(self): return self.__connectivity @@ -276,24 +276,24 @@ def getHullIDs(self): def __printMessage(self, msg:str): if self._mesher_verbosity > 0: print(msg) - return + return def setVerbosity(self, verbosity_level:int=1): - self._mesher_verbosity = verbosity_level + self._mesher_verbosity = verbosity_level if self._mesher_verbosity > 1: self._gmsh_verbosity = 4 - return + return class MeshThermodynamicPlane(Mesh2DPlane): __includeSaturationCurve:bool=False - __saturation_curve_points:np.ndarray[float] = None + __saturation_curve_points:np.ndarray[float] = None def __init__(self): super().__init__() - return + return def _createInternalGeometry(self, perimiter_curvloop:int, perimiter_tags:list[int]): if self.__includeSaturationCurve: @@ -304,7 +304,7 @@ def _createInternalGeometry(self, perimiter_curvloop:int, perimiter_tags:list[in self._gmsh_geo.addPhysicalGroup(2, [fluid_surf] + sat_surfs) else: super()._createInternalGeometry(perimiter_curvloop, perimiter_tags) - return + return def __createSaturationCurveGeom(self): @@ -404,7 +404,7 @@ def __clipSaturationCurveToPlane(self, pts_offset_lower:np.ndarray[float], pts_o def setSaturationCurvePoints(self, saturation_curve_rhoe:np.ndarray[float]): self.__saturation_curve_points = saturation_curve_rhoe - self.__includeSaturationCurve = True - return + self.__includeSaturationCurve = True + return \ No newline at end of file From 1193c71baa99b15ba0628c06157e37546cdb6b53 Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Fri, 29 May 2026 14:51:34 +0200 Subject: [PATCH 08/44] Test case for two-phase LUT --- .../NICFD/LUT_Twophase/generate_table.py | 46 ++++++++++--------- 1 file changed, 25 insertions(+), 21 deletions(-) diff --git a/TestCases/NICFD/LUT_Twophase/generate_table.py b/TestCases/NICFD/LUT_Twophase/generate_table.py index 81935e4..349aff6 100644 --- a/TestCases/NICFD/LUT_Twophase/generate_table.py +++ b/TestCases/NICFD/LUT_Twophase/generate_table.py @@ -1,7 +1,8 @@ -from su2dataminer.config import Config_NICFD -from su2dataminer.manifold import SU2TableGenerator_NICFD +# Test case for generating thermodynamic tables for two-phase NICFD problems + +from Common.DataDrivenConfig import Config_NICFD +from su2dataminer.manifold import TableGenerator_NICFD -# Generate properties of MM with REFPROP library. config = Config_NICFD() config.SetFluid("MM") config.SetEquationOfState("REFPROP") @@ -14,33 +15,36 @@ # Include visosity, conductivity, and vapor quality. config.IncludeTransportProperties(True) + +# Table limits config.UseAutoRange(False) config.SetDensityBounds(0.1, 500) config.SetEnergyBounds(200e3, 360e3) config.SetNpDensity(50) config.SetNpEnergy(50) -# Initiate table generator with adaptive triangulation. -tablegen = SU2TableGenerator_NICFD(config) -tablegen.setDiscretizationMethod("adaptive") +tgen = TableGenerator_NICFD(config) + +# Specify a target number of nodes in the table +tgen.setTargetNodeCount(4e4) -# Specify table resolution for coarse and refined sections. -tablegen.setTargetNumberOfNodes(10000) +# or the maximum cell size +#tgen.setMaximumCellSize(2.0e-2) -# Relative step size for finite-differences. -tablegen.setFDStepSize(7e-3) +# Custom refinement; reduce cell size by a specified factor where the +# thermodynamic quantity is within specified limits +tgen.addRefinementCriterion("s", lowerbound=725,upperbound=780, coef=0.4) +tgen.addRefinementCriterion("Density",0, 50, 0.4) +tgen.addRefinementCriterion("Density",0, 20, 0.2) -tablegen.tableBoundsFromPointCloud("SU2_solution_data.csv") -# Optionally, specify thermophysical variables to be included in the table. By default, all variables are included. -# tablegen.SetTableVars(["Density","Energy","s","p","T", "dsdrho_e","dsde_rho", "d2sdrho2","d2sde2","d2sdedrho","VaporQuality","ViscosityDyn","Conductivity"]) +# Apply smoothing to tabulated data. High value = more smoothing +tgen.setSmoothingParameter(0.05) -# Specify custom refinement regions (low density, around Trova isentrope) -tablegen.addRefinementCriterion("Density", 0.0, 50.0, 0.2) -tablegen.addRefinementCriterion("s", 729.13-2, 729.13+10, 0.2) +# Terminal message verbosity +tgen.setVerbosity(1) -# Generate table. -tablegen.generateTable() +tgen.generateTable() -# Write SU2 DRG and vtk table. -tablegen.WriteTableFile("LUT_adaptive.drg") -tablegen.WriteOutParaview("vtktable_adaptive") \ No newline at end of file +# Write a table file in vtk format so it can be loaded in Paraview. +tgen.writeParaviewTable("vtktable_twophase") +tgen.writeSU2Table("SU2table_twophase") \ No newline at end of file From 55d654cda0b296ca76d7977a9eeb35eb24284768 Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Thu, 11 Jun 2026 16:50:19 +0200 Subject: [PATCH 09/44] Automatically set controlling variables to pv and enth when generating flamelets for a single equivalence ratio --- Common/DataDrivenConfig.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/Common/DataDrivenConfig.py b/Common/DataDrivenConfig.py index 539da49..f85cb48 100644 --- a/Common/DataDrivenConfig.py +++ b/Common/DataDrivenConfig.py @@ -38,7 +38,7 @@ #---------------------------------------------------------------------------------------------# # Importing DataMiner classes and functions #---------------------------------------------------------------------------------------------# -from Common.Properties import DefaultSettings_NICFD, DefaultSettings_FGM +from Common.Properties import DefaultSettings_NICFD, DefaultSettings_FGM, FGMVars from Common.Config_base import Config from Common.CommonMethods import * @@ -923,6 +923,10 @@ def __SynchronizeSettings(self): self.SetAverageLewisNumbers() + if self.__mix_status_lower==self.__mix_status_upper: + self.SetControllingVariables([FGMVars.ProgressVariable.name, FGMVars.EnthalpyTot.name]) + else: + self.SetControllingVariables([FGMVars.ProgressVariable.name, FGMVars.EnthalpyTot.name, FGMVars.MixtureFraction.name]) return def GetConstSpecieLewisNumbers(self): From ab83a023a0071e7e8fa87b8e93bdeb139a4eab45 Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Thu, 11 Jun 2026 16:51:30 +0200 Subject: [PATCH 10/44] Include function for calculating gradients with finite-differences --- Common/Interpolators.py | 45 ++++++++++++++++++++++++++++++++++++----- 1 file changed, 40 insertions(+), 5 deletions(-) diff --git a/Common/Interpolators.py b/Common/Interpolators.py index 0b09acc..5a7e5e9 100644 --- a/Common/Interpolators.py +++ b/Common/Interpolators.py @@ -140,7 +140,27 @@ def __interpolate_from_inverse_distance(self, query_input_samples:np.ndarray[flo return interpolated_data[0] else: return interpolated_data + + def queryJacobian(self, query_input_samples:np.ndarray[float], nearest_neighbors:int=3, inverse_distance_exponent:float=2): + dx = 1e-2 + grads = np.zeros([self.__nDim_input, query_input_samples.shape[0], self.__state_space_data.shape[1]]) + for i in range(self.__nDim_input): + q_copy = query_input_samples.copy() + if query_input_samples.ndim==1: + q_copy[i] += dx + else: + q_copy[:, i] += dx + interp_data_plus = self.query(q_copy, nearest_neighbors, inverse_distance_exponent) + if query_input_samples.ndim==1: + q_copy[i] -= 2*dx + else: + q_copy[:, i] -= 2*dx + interp_data_minus = self.query(q_copy, nearest_neighbors, inverse_distance_exponent) + + grads[i] = (interp_data_plus - interp_data_minus) / (2*dx) + return grads + def __evaluate_KD_tree(self, query_input_samples:np.ndarray[float], number_of_nearest_neighbors:int): distances_to_nearest_neighbors, nearest_neighbor_indices = self.__KD_tree.query(query_input_samples, k=number_of_nearest_neighbors,eps=0) return distances_to_nearest_neighbors, nearest_neighbor_indices @@ -256,11 +276,6 @@ def tuneTreeParameters(self): print("Number of nearest neighbors: %i" % self.__n_near_single) print("Inverse distance exponent: %.2f" % self.__p_fac_single) - # plt.figure() - # ax = plt.axes(projection='3d') - # ax.plot3D(cv_data_test[:,0], cv_data_test[:,1], lookup_data_test[:, 2], 'k.') - # ax.plot3D(cv_data_test[:,0], cv_data_test[:,1], self.__lookup_data[:, 2], 'r.') - # plt.show() return def __call__(self, cv_data_query:np.ndarray[float],varnames:list[str]=[]): @@ -282,3 +297,23 @@ def __call__(self, cv_data_query:np.ndarray[float],varnames:list[str]=[]): return fluid_data_interp[0] else: return fluid_data_interp + + def Jacobian(self, cv_data_query:np.ndarray[float],varnames:list[str]=[]): + ndim_input = cv_data_query.ndim + if ndim_input < 2: + cv_data_query = cv_data_query[np.newaxis] + + grads = self.__lookup_tree.queryJacobian(cv_data_query, nearest_neighbors=self.__n_near_single, inverse_distance_exponent=self.__p_fac_single) + nvars =len(varnames) + if nvars > 0: + var_indices = [self.__state_vars.index(var) for var in varnames] + + if grads.shape[1] == 1: + return grads[:,0,var_indices] + else: + return grads[:,:,var_indices] + else: + if grads.shape[1] == 1: + return grads[:,0,:] + else: + return grads \ No newline at end of file From 64af1464f5150ccb57e271f4796d6a2b97680df9 Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Thu, 11 Jun 2026 16:51:51 +0200 Subject: [PATCH 11/44] Update variable names --- Manifold_Generation/LUT/FlameletTableGeneration.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/Manifold_Generation/LUT/FlameletTableGeneration.py b/Manifold_Generation/LUT/FlameletTableGeneration.py index cbb7a3a..726a7e3 100644 --- a/Manifold_Generation/LUT/FlameletTableGeneration.py +++ b/Manifold_Generation/LUT/FlameletTableGeneration.py @@ -396,7 +396,7 @@ def __DefineFlameletDataInterpolator(self): PPV_test = D_test[:, self._Flamelet_Variables.index(var_to_test_for)] print("Done!") print("Setting up KD-tree...") - self._lookup_tree = Invdisttree(X=CV_train_scaled,z=D_train) + self._lookup_tree = Invdisttree(samples_state_space=CV_train_scaled,state_data=D_train) print("Done!") if not self._custom_KDtreeparams: @@ -407,7 +407,7 @@ def __DefineFlameletDataInterpolator(self): RMS_ppv = np.zeros([len(n_near_range), len(p_range)]) for i in tqdm(range(len(n_near_range))): for j in range(len(p_range)): - PPV_predicted = self._lookup_tree(q=CV_test_scaled, nnear=n_near_range[i], p=p_range[j])[:, self._Flamelet_Variables.index(var_to_test_for)] + PPV_predicted = self._lookup_tree.query(query_samples=CV_test_scaled, nearest_neighbors=n_near_range[i], inverse_distance_exponent=p_range[j])[:, self._Flamelet_Variables.index(var_to_test_for)] rms_local = np.average(np.power(PPV_predicted - PPV_test, 2)) RMS_ppv[i,j] = rms_local [imin,jmin] = divmod(RMS_ppv.argmin(), RMS_ppv.shape[1]) @@ -418,7 +418,7 @@ def __DefineFlameletDataInterpolator(self): print("Best found distance power: "+str(self._p_fac)) print("Setting up KD-tree...") self._D_full = D_full - self._lookup_tree = Invdisttree(X=CV_full_scaled,z=D_full) + self._lookup_tree = Invdisttree(samples_state_space=CV_full_scaled,state_data=D_full) print("Done!") return @@ -437,7 +437,7 @@ def __Rebuild2DInterpolator(self): CV_full_2d = self._D_full[:, self._plane_cv_idxs] self._scaler_2d = MinMaxScaler() CV_full_2d_scaled = self._scaler_2d.fit_transform(CV_full_2d) - self._lookup_tree = Invdisttree(X=CV_full_2d_scaled, z=self._D_full) + self._lookup_tree = Invdisttree(samples_state_space=CV_full_2d_scaled, state_data=self._D_full) print("Done!") return @@ -446,7 +446,7 @@ def __EvaluateFlameletInterpolator(self, CV_unscaled:np.ndarray): CV_scaled = self._scaler_2d.transform(CV_unscaled[:, self._plane_cv_idxs]) else: CV_scaled = self._scaler.transform(CV_unscaled) - data_interp = self._lookup_tree(q=CV_scaled,nnear=self._n_near,p=self._p_fac) + data_interp = self._lookup_tree.query(query_samples=CV_scaled,nearest_neighbors=self._n_near,inverse_distance_exponent=self._p_fac) return data_interp def VisualizeTableLevel(self, val_mix_frac:float, var_to_plot:str=None): From e4dc7ea5f8f954cb64faeb964e97da82a68a826e Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Thu, 11 Jun 2026 16:52:37 +0200 Subject: [PATCH 12/44] Include refinement options based on gradients and equilibrium --- Manifold_Generation/LUT/LUTGenerator_Base.py | 48 ++++---- Manifold_Generation/LUT/LUTGenerators.py | 117 +++++++++++++++++-- 2 files changed, 134 insertions(+), 31 deletions(-) diff --git a/Manifold_Generation/LUT/LUTGenerator_Base.py b/Manifold_Generation/LUT/LUTGenerator_Base.py index 450322a..107970d 100644 --- a/Manifold_Generation/LUT/LUTGenerator_Base.py +++ b/Manifold_Generation/LUT/LUTGenerator_Base.py @@ -27,6 +27,7 @@ import meshio import pandas from os import sep +from copy import copy from sklearn.preprocessing import MinMaxScaler from multiprocessing import Pool from scipy.interpolate import RBFInterpolator @@ -80,18 +81,18 @@ class SU2TableGenerator_Base: _verbosity:int=1 def __init__(self, config_in:Config): - self._Config = config_in + self._Config = copy(config_in) self._nDim_table = len(self._Config.GetControllingVariables()) return def defineInterpolator(self): - stateDataFrame = self._getFluidDataPointCloud() + stateDataFrame = self._getFluidDataForInterpolator() cv_data = np.column_stack(tuple(stateDataFrame[cv] for cv in self._Config.GetControllingVariables())) cv_data_scaled = self._scaler_controlling_variables.fit_transform(cv_data) self._fluid_data_interpolator = fluidDataInterpolator(cv_data_scaled, stateDataFrame, self.__N_nearest_neighbors, self.__inverse_distance_exponent) return - def _getFluidDataPointCloud(self): + def _getFluidDataForInterpolator(self): return def setMaximumCellSize(self, cell_size_coarse:float=1e-2): @@ -108,7 +109,7 @@ def setTargetNodeCount(self, target_node_count:int=3000): self._target_number_of_nodes = target_node_count return - def addRefinementCriterion(self, varname:str, lowerbound:float=-np.inf, upperbound:float=np.inf, coef:float=0.5): + def applyRefinementWithin(self, varname:str, lowerbound:float=-np.inf, upperbound:float=np.inf, coef:float=0.5): """Specify conditional refinement based on interpolated thermochemical state data. Cell sizes are reduced by a factor "coef" where the table data lies between the specified lower bound and upper bound (inclusive) :param varname: thermochemical state variable name. @@ -171,11 +172,11 @@ def generateTable(self): return def __preprocessTableSettings(self): - self.__processTableLevels() + self._processTableLevels() return def meshTableLevel(self, level_index:int): - pointCloud = self._createPointCloud(self._table_levels[level_index]) + pointCloud = self._createPointCloudForTableLevel(self._table_levels[level_index]) mesher = self._initiateMesher() if self.__run_parallel: mesher.setVerbosity(0) @@ -193,7 +194,7 @@ def meshTableLevel(self, level_index:int): cvTable, triangles, hullIDs, tableDataFrame = self.__extractTableLevelData(mesher) - if self.__is3D(): + if self._is3D(): self.__printmsg("Finished meshing table level %i at %s=%.2e with %i nodes" % (level_index, \ self._Config.GetControllingVariables()[2], \ self._table_levels[level_index], \ @@ -223,7 +224,7 @@ def __extractTableLevelData(self, mesher:Mesh2DPlane): return cvTable, triangles, hullIDs, tableDataFrame - def _createPointCloud(self, levelValue:float): + def _createPointCloudForTableLevel(self, levelValue:float): return def _initiateMesher(self): @@ -337,14 +338,14 @@ def writeSU2Table(self, filepath_out:str=None): fid = open(file_out, "w+") fid.write("Dragon library\n\n") fid.write("
\n\n") - if self.__is2D(): + if self._is2D(): fid.write("[Version]\n1.0.1\n\n") else: fid.write("[Version]\n1.1.0\n\n") self._writeAdditionalInfoToTable(fid) - if self.__is3D(): + if self._is3D(): fid.write("[Number of table levels]\n%i\n\n" % self._N_table_levels) fid.write("[Table levels]\n") for z in self._table_levels: @@ -379,34 +380,34 @@ def writeSU2Table(self, filepath_out:str=None): fid.write("\n") for iLevel in range(len(self._table_nodes)): - if self.__is3D(): + if self._is3D(): fid.write("\n") Np = np.shape(self._table_nodes[iLevel])[0] for iNode in range(Np): line_table_data = "\t".join(["%.14e" % self._data_in_table[iLevel][var][iNode] for var in self._table_vars]) fid.write(line_table_data + "\n") - if self.__is3D(): + if self._is3D(): fid.write("\n") fid.write("\n\n") fid.write("\n") for iLevel in range(len(self._table_connectivity)): - if self.__is3D(): + if self._is3D(): fid.write("\n") for iCell in range(len(self._table_connectivity[iLevel])): fid.write("\t".join("%i" % c for c in self._table_connectivity[iLevel][iCell, :]+1) + "\n") - if self.__is3D(): + if self._is3D(): fid.write("\n") fid.write("\n\n") fid.write("\n") for iLevel in range(len(self._table_hullnodes)): - if self.__is3D(): + if self._is3D(): fid.write("\n") for iCell in range(len(self._table_hullnodes[iLevel])): fid.write(("%i" % (self._table_hullnodes[iLevel][iCell]+1)) + "\n") - if self.__is3D(): + if self._is3D(): fid.write("\n") fid.write("\n\n") self.__printmsg("Done") @@ -459,25 +460,30 @@ def setInverseDistanceExponent(self, p_factor:float=2): return def setSmoothingParameter(self, smoothing_factor:float=0): + """Apply smoothing to table data. High value = more smoothing, low value = no smoothing + + :param smoothing_factor: _description_, defaults to 0 + :type smoothing_factor: float, optional + """ self.__smoothTableData = True self.__smoothingLevel = smoothing_factor return def __optionAppliesFor3D(self): - if self.__is2D(): + if self._is2D(): print("Option does not apply for two-dimensional table, ignoring") return False else: return True - def __is2D(self): + def _is2D(self): return self._nDim_table==2 - def __is3D(self): + def _is3D(self): return self._nDim_table==3 - def __processTableLevels(self): - if self.__is3D(): + def _processTableLevels(self): + if self._is3D(): if not any(self._table_levels): if self._tableUpperLevel and self._tableLowerLevel and self._N_table_levels: self._table_levels = np.linspace(self._tableLowerLevel, self._tableUpperLevel, self._N_table_levels) diff --git a/Manifold_Generation/LUT/LUTGenerators.py b/Manifold_Generation/LUT/LUTGenerators.py index fa9b12d..a5cfe95 100644 --- a/Manifold_Generation/LUT/LUTGenerators.py +++ b/Manifold_Generation/LUT/LUTGenerators.py @@ -30,7 +30,6 @@ from Common.DataDrivenConfig import Config_NICFD,Config_FGM from Manifold_Generation.LUT.LUTGenerator_Base import SU2TableGenerator_Base from Manifold_Generation.LUT.MeshTools import MeshThermodynamicPlane - class TableGenerator_NICFD(SU2TableGenerator_Base): _Config:Config_NICFD = None __datagenerator:DataGenerator_CoolProp = None @@ -100,7 +99,7 @@ def _passRefinementOptions(self, mesher:MeshThermodynamicPlane): def _initiateMesher(self): return MeshThermodynamicPlane() - def _getFluidDataPointCloud(self): + def _getFluidDataForInterpolator(self): self.__datagenerator.PreprocessData() self.__datagenerator.ComputeData() state_data_pointcloud, valid_mask = self.__datagenerator.GetStateData() @@ -114,7 +113,7 @@ def _getFluidDataPointCloud(self): return state_dataFrame - def _createPointCloud(self, levelValue:float): + def _createPointCloudForTableLevel(self, levelValue:float): rhoe_pointcloud = np.column_stack((self.__thermodynamic_data[EntropicVars.Density.name], self.__thermodynamic_data[EntropicVars.Energy.name])) const_z = np.zeros(len(rhoe_pointcloud)) @@ -149,21 +148,73 @@ def _writeAdditionalInfoToTable(self, fid): class TableGenerator_FGM(SU2TableGenerator_Base): _Config:Config_FGM = None + __refineGradients:bool = False + _refine_for_gradients_of:list[str] = [] + _gradient_refinement_factors:list[float] = [] + _gradient_norm_factors:list[float] = [] + + __refine_equilibrium:bool = False + __equilibrium_refinement_factor:float = 0.5 + __margin_equilibrium:float = 2e-2 def __init__(self, config_in:Config_FGM): super().__init__(config_in) - self._getFluidDataPointCloud() + self._getFluidDataForInterpolator() + return + + def applyRefinementForGradientOf(self, varname:str, coef:float=0.5): + """Scale table refinement based on the gradients of thermochemical properties. + + :param varname: quantity for which to evaluate gradients. + :type varname: str + :param coef: refinement coefficient, defaults to 0.5 + :type coef: float, optional + :raises Exception: if quantity is not found in table variables. + :raises Exception: if refinement coefficient value is negative. + """ + if varname not in self._state_quantities: + raise Exception("%s is not in the list of available thermophysical state variables" % varname) + if coef <= 0: + raise Exception("Refinement coeffcient should be positive") + self._refine_for_gradients_of.append(varname) + self._gradient_refinement_factors.append(coef) + self.__refineGradients = True return - def _getFluidDataPointCloud(self): + def refineEquilibrium(self, coef:float=0.5, margin:float=2e-2): + """Apply refinement to equilibrium areas of each table level. + + :param coef: refinement coefficient, defaults to 0.5 + :type coef: float, optional + :raises Exception: if refinement coefficient value is negative. + """ + if coef <= 0: + raise Exception("Refinement coefficient should be positive") + self.__refine_equilibrium = True + self.__equilibrium_refinement_factor = coef + self.__margin_equilibrium = margin + return + + def _getFluidDataForInterpolator(self): flamelet_data_filename = os.sep.join((self._Config.GetOutputDir(), self._Config.GetConcatenationFileHeader()+"_full.csv")) flameletDataPointCloud = pd.read_csv(flamelet_data_filename) self._state_quantities = list(flameletDataPointCloud.keys()) self._table_vars = self._state_quantities.copy() return flameletDataPointCloud - def _createPointCloud(self, levelValue:float): - + def _processTableLevels(self): + super()._processTableLevels() + if self._is2D(): + mixture_status = self._Config.GetMixtureBounds()[0] + if self._Config.DefineMixtureStatus(): + mixture_fraction = mixture_status + else: + self._Config.gas.set_equivalence_ratio(mixture_status, self._Config.GetFuelString(),self._Config.GetOxidizerString()) + mixture_fraction = self._Config.gas.mixture_fraction(self._Config.GetFuelString(),self._Config.GetOxidizerString()) + self._table_levels[0] = mixture_fraction + return + + def _createPointCloudForTableLevel(self, levelValue:float): self._Config.gas.set_mixture_fraction(levelValue, self._Config.GetFuelString(),self._Config.GetOxidizerString()) self._Config.gas.TP=self._Config.GetUnbTempBounds()[0],DefaultSettings_FGM.pressure h_min_unb = self._Config.gas.enthalpy_mass @@ -197,9 +248,55 @@ def _createPointCloud(self, levelValue:float): h_limit = ((h_min_unb - h_min) * pv_grid + (h_min*pv_unb - h_min_unb*pv_b))/(pv_unb - pv_b) idx_keep = h_grid >= h_limit - cv_pointcloud = CV_grid_init[idx_keep, :] - cv_pointcloud_scaled = self._scaler_controlling_variables.transform(cv_pointcloud) - return cv_pointcloud_scaled + cv_pointcloud = CV_grid_init[idx_keep] + if self._is2D(): + cv_pointcloud_scaled = self._scaler_controlling_variables.transform(cv_pointcloud[:,:2]) + zcoords = np.zeros([len(cv_pointcloud), 1]) + pointCloudNodes = np.column_stack((cv_pointcloud_scaled, zcoords)) + else: + cv_pointcloud_scaled = self._scaler_controlling_variables.transform(cv_pointcloud) + pointCloudNodes = cv_pointcloud_scaled + + if self.__refineGradients: + self.__computeGradientNormFactors(cv_pointcloud_scaled) + + return pointCloudNodes + + def __computeGradientNormFactors(self, cv_vals:np.ndarray[float]): + gradients = self._fluid_data_interpolator.Jacobian(cv_vals, varnames=self._refine_for_gradients_of) + grads_mag = np.linalg.norm(gradients,axis=0) + self._gradient_norm_factors = np.max(grads_mag,axis=0) + return + + def _refinelocation(self, x:float,y:float,z:float): + ref_factor = super()._refinelocation(x,y,z) + + if self.__refineGradients: + ref_factor_grads = self.__applyGradientRefinement(x,y,z) + ref_factor = min(ref_factor, ref_factor_grads) + + if self.__refine_equilibrium: + ref_factor_equilibrium = self.__applyEquilibriumRefinement(x,y,z) + ref_factor = min(ref_factor, ref_factor_equilibrium) + + return ref_factor + + def __applyGradientRefinement(self, x:float, y:float, z:float): + cv_input = np.array([x,y,z]) + jac = self._fluid_data_interpolator.Jacobian(cv_input[:self._nDim_table], varnames=self._refine_for_gradients_of) + jac_mag = np.linalg.norm(jac,axis=0) + jac_mag_norm = jac_mag / self._gradient_norm_factors + ref_factor = 1.0 + for i, f in enumerate(self._gradient_refinement_factors): + ref_factor = min(ref_factor, max(f, 1.0 - (1 - f)*jac_mag_norm[i])) + return ref_factor + + def __applyEquilibriumRefinement(self, x:float, y:float, z:float): + ref_factor = 1.0 + if self._is2D(): + if x <= self.__margin_equilibrium or x >= (1-self.__margin_equilibrium): + ref_factor = self.__equilibrium_refinement_factor + return ref_factor def _writeAdditionalInfoToTable(self, fid): fid.write("Fuel:\n") From 227d4310c5e27f17a3860c88dd95523665932d94 Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Thu, 11 Jun 2026 16:53:29 +0200 Subject: [PATCH 13/44] Replace table generator module for test case --- .../3_generate_table.py | 152 +++++++++++------- 1 file changed, 90 insertions(+), 62 deletions(-) diff --git a/TestCases/LUT/Flamelet_Table_CH4_phi080/3_generate_table.py b/TestCases/LUT/Flamelet_Table_CH4_phi080/3_generate_table.py index 4534747..2cdf95f 100644 --- a/TestCases/LUT/Flamelet_Table_CH4_phi080/3_generate_table.py +++ b/TestCases/LUT/Flamelet_Table_CH4_phi080/3_generate_table.py @@ -1,67 +1,95 @@ -from Manifold_Generation.LUT.FlameletTableGeneration import SU2TableGenerator +from Manifold_Generation.LUT.LUTGenerators import TableGenerator_FGM from Common.DataDrivenConfig import Config_FGM # Loading configuration. Config = Config_FGM("TableGeneration.cfg") +Tgen = TableGenerator_FGM(Config) -# Initializing table module and pre-process interpolator. -Tgen = SU2TableGenerator(Config, n_near=14, p_fac=1) - -# Generate a 2D (ProgressVariable, EnthalpyTot) LUT for a single equivalence -# ratio phi = 0.80. Because phi_min == phi_max, the table generator writes a -# Dragon v1.0.1 file without mixture-fraction levels. -Tgen.SetEquivalenceRatioLimits(phi_min=0.80, phi_max=0.80) -Tgen.SetNTableLevels(1) -Tgen.SetRefinementFields(["ProdRateTot_PV", "Y_dot_net-CO", "Y_dot_pos-CO","Y_dot_neg-CO"]) - -# small -#Tgen.SetBaseCellSize(1e-2) -#Tgen.SetRefinedCellSize(1e-2) -#Tgen.SetRefinementRadius(1e-2) -#Tgen.SetRefinementMethod("gradient") -#Tgen.SetMaxRefinementSeeds(500) -#Tgen.SetHullCellSize(1.0e-2) - -# medium -Tgen.SetBaseCellSize(5e-3) -Tgen.SetRefinedCellSize(5e-3) -Tgen.SetRefinementRadius(5e-3) -Tgen.SetRefinementMethod("gradient") -Tgen.SetMaxRefinementSeeds(500) -Tgen.SetHullCellSize(5.0e-3) - -# fine -#Tgen.SetBaseCellSize(2.5e-3) -#Tgen.SetRefinedCellSize(1.0e-3) -#Tgen.SetRefinementRadius(2.5e-3) -#Tgen.SetRefinementMethod("gradient") -#Tgen.SetMaxRefinementSeeds(500) -#Tgen.SetHullCellSize(2.5e-3) - - - - -Tgen.SetTableAxes(level_cv_name="MixtureFraction", - plane_cv_names=["ProgressVariable", "EnthalpyTot"]) - -# Visualize the table mesh and reaction rate at phi = 0.80. -cv_target = Config.GetUnburntScalars(equivalence_ratio=0.80, temperature=300.0) -pv_target = cv_target[0] -z_target = cv_target[2] -print("Target unburnt progress variable:", pv_target) -#Tgen.VisualizeTableLevel(z_target, "ProdRateTot_PV") -#Tgen.VisualizeTableLevel(z_target, "Temperature") -#Tgen.VisualizeTableLevel(z_target, "Y_dot_net-CO") -#Tgen.VisualizeTableLevel(z_target, "Y_dot_pos-CO") -#Tgen.VisualizeTableLevel(z_target, "Y_dot_neg-CO") -#Tgen.VisualizeTableLevel(z_target, "Y_dot_net-NOx") -#Tgen.VisualizeTableLevel(z_target) - -# Generate table connectivity and interpolate flamelet data. -Tgen.GenerateTableNodes() -# from 99% of the max progress variable, set the source terms of CO and H2 to zero -# if the absolute value of the source terms is |S| < 0.1 -Tgen.ClampSourceTerms(species_list=["CO", "H2", "CO2", "H2O"], pv_frac=0.99, abs_tol=0.1) - -# Write SU2 .drg table file (Dragon v1.0.1 format, 2D). -Tgen.WriteTableFile() +# Iterate to achieve a target number of nodes +#Tgen.setTargetNodeCount(4000) +# Or manually specify the coarse cell size +Tgen.setMaximumCellSize(1.9e-2) + +# Apply refinement based on the values of thermochemical quantities: +Tgen.applyRefinementWithin("Temperature", lowerbound=270, upperbound=450.0, coef=0.5) + +# You can do this for any number of variables: +# Tgen.applyRefinementWithin("Cp", lowerbound=1000, upperbound=1200, coef=0.5) + +# Scale refinement based on the gradients of quantities: +Tgen.applyRefinementForGradientOf("ProdRateTot_PV", coef=0.3) + +# Apply refinement in proximity of the reactants and products +Tgen.refineEquilibrium(coef=0.5,margin=2e-2) + +# Optionally: apply smoothing to table data to get rid of any waves or discontinuities +# Higher coefficient = more smoothing +#Tgen.setSmoothingParameter(0.1) + +Tgen.generateTable() + +# Export table in vtk format +Tgen.writeParaviewTable("LUT_vtk") + + +# # Initializing table module and pre-process interpolator. +# Tgen = SU2TableGenerator(Config, n_near=14, p_fac=1) + +# # Generate a 2D (ProgressVariable, EnthalpyTot) LUT for a single equivalence +# # ratio phi = 0.80. Because phi_min == phi_max, the table generator writes a +# # Dragon v1.0.1 file without mixture-fraction levels. +# Tgen.SetEquivalenceRatioLimits(phi_min=0.80, phi_max=0.80) +# Tgen.SetNTableLevels(1) +# Tgen.SetRefinementFields(["ProdRateTot_PV", "Y_dot_net-CO", "Y_dot_pos-CO","Y_dot_neg-CO"]) + +# # small +# #Tgen.SetBaseCellSize(1e-2) +# #Tgen.SetRefinedCellSize(1e-2) +# #Tgen.SetRefinementRadius(1e-2) +# #Tgen.SetRefinementMethod("gradient") +# #Tgen.SetMaxRefinementSeeds(500) +# #Tgen.SetHullCellSize(1.0e-2) + +# # medium +# Tgen.SetBaseCellSize(5e-3) +# Tgen.SetRefinedCellSize(5e-3) +# Tgen.SetRefinementRadius(5e-3) +# Tgen.SetRefinementMethod("gradient") +# Tgen.SetMaxRefinementSeeds(500) +# Tgen.SetHullCellSize(5.0e-3) + +# # fine +# #Tgen.SetBaseCellSize(2.5e-3) +# #Tgen.SetRefinedCellSize(1.0e-3) +# #Tgen.SetRefinementRadius(2.5e-3) +# #Tgen.SetRefinementMethod("gradient") +# #Tgen.SetMaxRefinementSeeds(500) +# #Tgen.SetHullCellSize(2.5e-3) + + + + +# Tgen.SetTableAxes(level_cv_name="MixtureFraction", +# plane_cv_names=["ProgressVariable", "EnthalpyTot"]) + +# # Visualize the table mesh and reaction rate at phi = 0.80. +# cv_target = Config.GetUnburntScalars(equivalence_ratio=0.80, temperature=300.0) +# pv_target = cv_target[0] +# z_target = cv_target[2] +# print("Target unburnt progress variable:", pv_target) +# #Tgen.VisualizeTableLevel(z_target, "ProdRateTot_PV") +# #Tgen.VisualizeTableLevel(z_target, "Temperature") +# #Tgen.VisualizeTableLevel(z_target, "Y_dot_net-CO") +# #Tgen.VisualizeTableLevel(z_target, "Y_dot_pos-CO") +# #Tgen.VisualizeTableLevel(z_target, "Y_dot_neg-CO") +# #Tgen.VisualizeTableLevel(z_target, "Y_dot_net-NOx") +# #Tgen.VisualizeTableLevel(z_target) + +# # Generate table connectivity and interpolate flamelet data. +# Tgen.GenerateTableNodes() +# # from 99% of the max progress variable, set the source terms of CO and H2 to zero +# # if the absolute value of the source terms is |S| < 0.1 +# Tgen.ClampSourceTerms(species_list=["CO", "H2", "CO2", "H2O"], pv_frac=0.99, abs_tol=0.1) + +# # Write SU2 .drg table file (Dragon v1.0.1 format, 2D). +# Tgen.WriteTableFile() From c2ae4f78d04f45efc5136b1c5bbc5228a2a15851 Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Fri, 12 Jun 2026 12:12:52 +0200 Subject: [PATCH 14/44] Reordered functions for readability and provided function hints --- Manifold_Generation/LUT/LUTGenerator_Base.py | 319 +++++++++++++------ 1 file changed, 222 insertions(+), 97 deletions(-) diff --git a/Manifold_Generation/LUT/LUTGenerator_Base.py b/Manifold_Generation/LUT/LUTGenerator_Base.py index 107970d..3d7c3a5 100644 --- a/Manifold_Generation/LUT/LUTGenerator_Base.py +++ b/Manifold_Generation/LUT/LUTGenerator_Base.py @@ -85,7 +85,48 @@ def __init__(self, config_in:Config): self._nDim_table = len(self._Config.GetControllingVariables()) return - def defineInterpolator(self): + def generateTable(self): + + self._processTableLevels() + + self._defineFluidDataInterpolator() + + self._generateTableLevelData() + + if self.__smoothTableData: + self._smoothTableLevelData() + return + + def _processTableLevels(self): + """Prepare table level values depending on the number of dimensions. + + :raises Exception: if no information is specified regarding the table level interval and number of table levels. + """ + if self._is3D(): + if not any(self._table_levels): + if self._tableUpperLevel and self._tableLowerLevel and self._N_table_levels: + self._table_levels = np.linspace(self._tableLowerLevel, self._tableUpperLevel, self._N_table_levels) + else: + raise Exception("No table level information provided, aborting") + + if any(self._table_level_inserts): + self._table_levels = np.append(self._table_levels, np.array(self._table_level_inserts)) + + unique_table_levels = np.unique(self._table_levels) + sorted_table_levels = np.sort(unique_table_levels) + self._table_levels = sorted_table_levels + self._N_table_levels = len(self._table_levels) + self._tableLowerLevel = min(self._table_levels) + self._tableUpperLevel = max(self._table_levels) + else: + self._table_levels = [0] + self._N_table_levels = 1 + return + + + def _defineFluidDataInterpolator(self): + """Prepare the interpolation function for evaluating thermochemical states on the table nodes and scale the controlling variables in the state space. + """ stateDataFrame = self._getFluidDataForInterpolator() cv_data = np.column_stack(tuple(stateDataFrame[cv] for cv in self._Config.GetControllingVariables())) cv_data_scaled = self._scaler_controlling_variables.fit_transform(cv_data) @@ -94,7 +135,48 @@ def defineInterpolator(self): def _getFluidDataForInterpolator(self): return - + + def _generateTableLevelData(self): + """Generate the connectivity for each table level and interpolate the thermochemical state data onto the table nodes. + """ + self._table_nodes = [None]*self._N_table_levels + self._table_connectivity = [None]*self._N_table_levels + self._table_hullnodes = [None]*self._N_table_levels + self._data_in_table = [None]*self._N_table_levels + if self.__run_parallel: + pool = Pool(self.__N_cores) + results = pool.map(self.__meshTableLevel, [i for i in range(self._N_table_levels)]) + pool.close() + + for iLevel in range(self._N_table_levels): + self._table_nodes[iLevel] = results[iLevel][0] + self._table_connectivity[iLevel] = results[iLevel][1] + self._table_hullnodes[iLevel] = results[iLevel][2] + self._data_in_table[iLevel] = results[iLevel][3] + else: + for iLevel in range(len(self._table_levels)): + tableLevel = self.__meshTableLevel(iLevel) + + self._table_nodes[iLevel] = tableLevel[0] + self._table_connectivity[iLevel] = tableLevel[1] + self._table_hullnodes[iLevel] = tableLevel[2] + self._data_in_table[iLevel] = tableLevel[3] + return + + def _smoothTableLevelData(self): + self.__printmsg("Smoothing table data...") + for iLevel in range(self._N_table_levels): + cv_level_scaled = self._scaler_controlling_variables.transform(self._table_nodes[iLevel]) + + data_on_table_level = self._data_in_table[iLevel].values + smoothener = RBFInterpolator(cv_level_scaled[:, :2], data_on_table_level, kernel="linear",smoothing=self.__smoothingLevel,neighbors=100) + smoothened_table_data = smoothener(cv_level_scaled[:, :2]) + for ivar, var in enumerate(list(self._data_in_table[iLevel].keys())): + if var not in self._Config.GetControllingVariables(): + self._data_in_table[iLevel][var] = smoothened_table_data[:, ivar] + self.__printmsg("Done") + return + def setMaximumCellSize(self, cell_size_coarse:float=1e-2): """Specify the coarse level cell size of the table @@ -102,10 +184,21 @@ def setMaximumCellSize(self, cell_size_coarse:float=1e-2): :type cell_size_coarse: float, optional :raises Exception: if specified cell size is negative or zero """ + if cell_size_coarse <= 0: + raise Exception("Maximum cell size should be strictly positive.") + self._base_cell_size = cell_size_coarse return def setTargetNodeCount(self, target_node_count:int=3000): + """Specify a target number of nodes for each table level. The table generator aims to approximate the target number of nodes within 1%. + + :param target_node_count: number of nodes on each table level, defaults to 3000 + :type target_node_count: int, optional + :raises Exception: if the specified value is not strictly positive. + """ + if target_node_count <= 0: + raise Exception("Target number of nodes in the table should be strictly positive.") self._target_number_of_nodes = target_node_count return @@ -138,45 +231,18 @@ def applyRefinementWithin(self, varname:str, lowerbound:float=-np.inf, upperboun self._conditional_refinement_factor.append(coef) return - def generateTable(self): - - self.__preprocessTableSettings() - - self.defineInterpolator() - - self._table_nodes = [None]*self._N_table_levels - self._table_connectivity = [None]*self._N_table_levels - self._table_hullnodes = [None]*self._N_table_levels - self._data_in_table = [None]*self._N_table_levels - if self.__run_parallel: - pool = Pool(self.__N_cores) - results = pool.map(self.meshTableLevel, [i for i in range(self._N_table_levels)]) - pool.close() + + def __meshTableLevel(self, level_index:int): + """Discretize and interpolate the fluid data of a single table level. - for iLevel in range(self._N_table_levels): - self._table_nodes[iLevel] = results[iLevel][0] - self._table_connectivity[iLevel] = results[iLevel][1] - self._table_hullnodes[iLevel] = results[iLevel][2] - self._data_in_table[iLevel] = results[iLevel][3] - else: - for iLevel, levelValue in enumerate(self._table_levels): - tableLevel = self.meshTableLevel(iLevel) - - self._table_nodes[iLevel] = tableLevel[0] - self._table_connectivity[iLevel] = tableLevel[1] - self._table_hullnodes[iLevel] = tableLevel[2] - self._data_in_table[iLevel] = tableLevel[3] + :param level_index: table level index + :type level_index: int + :return: list with table nodes, connectivity, perimiter indices, and interpolated fluid data. + :rtype: list[np.ndarray[float]] + """ - if self.__smoothTableData: - self.smoothTableLevelData() - return - - def __preprocessTableSettings(self): - self._processTableLevels() - return - - def meshTableLevel(self, level_index:int): pointCloud = self._createPointCloudForTableLevel(self._table_levels[level_index]) + mesher = self._initiateMesher() if self.__run_parallel: mesher.setVerbosity(0) @@ -202,7 +268,35 @@ def meshTableLevel(self, level_index:int): return [cvTable, triangles,hullIDs, tableDataFrame] + def _createPointCloudForTableLevel(self, levelValue:float): + """Generate a planar point cloud used as a reference for discretizing the table level. + + :param levelValue: value of the third table dimension corresponding to the table level. + :type levelValue: float + """ + return + + def _initiateMesher(self): + return Mesh2DPlane() + + def _passRefinementOptions(self, mesher:Mesh2DPlane): + """Transfer table refinement information to 2D meshing algorithm. + + """ + mesher.setBaseCellSize(self._base_cell_size) + if self._target_number_of_nodes: + mesher.setTargetNodeCount(self._target_number_of_nodes) + mesher.setRefinementFunction(self._refinelocation) + return + def __extractTableLevelData(self, mesher:Mesh2DPlane): + """Retrieve table level nodes and connectivity from 2D meshing algorithm. + + :param mesher: 2D meshing tool + :type mesher: Mesh2DPlane + :return: table nodes, connectivity, perimiter indices, and interpolated fluid data. + :rtype: tuple + """ meshnodes = mesher.getMeshNodes() triangles = mesher.getConnectivity() hullIDs = mesher.getHullIDs() @@ -212,35 +306,39 @@ def __extractTableLevelData(self, mesher:Mesh2DPlane): table_state_data = self._calculateTableStateData(cvTable_scaled) - cvTable = self._scaler_controlling_variables.inverse_transform(cvTable_scaled) - tableDataFrame = pd.DataFrame() for var in self._table_vars: tableDataFrame[var] = table_state_data[:, self._state_quantities.index(var)] + + cvTable = self._scaler_controlling_variables.inverse_transform(cvTable_scaled) for iCv, cv in enumerate(self._Config.GetControllingVariables()): tableDataFrame[cv] = cvTable[:, iCv] return cvTable, triangles, hullIDs, tableDataFrame - - def _createPointCloudForTableLevel(self, levelValue:float): - return - - def _initiateMesher(self): - return Mesh2DPlane() - - def _passRefinementOptions(self, mesher:Mesh2DPlane): - mesher.setBaseCellSize(self._base_cell_size) - if self._target_number_of_nodes: - mesher.setTargetNodeCount(self._target_number_of_nodes) - mesher.setRefinementFunction(self._refinelocation) - return - def _calculateTableStateData(self, cv_table_nodes:np.ndarray[float]): + """Interpolate fluid thermochemical data onto table nodes. + + :param cv_table_nodes: control variable values of table nodes + :type cv_table_nodes: np.ndarray[float] + :return: interpolated fluid data + :rtype: pd.DataFrame + """ return self._fluid_data_interpolator(cv_table_nodes) def _refinelocation(self, x:float,y:float,z:float): + """Evaluate refinement coefficient based on interpolated fluid data. + + :param x: first controlling variable. + :type x: float + :param y: second controlling variable. + :type y: float + :param z: third controlling variable. + :type z: float + :return: refinement coefficient + :rtype: float + """ refinement_factor = 1.0 if len(self._conditional_refinement_indices) > 0: cv_input = np.array([x,y,z]) @@ -248,15 +346,19 @@ def _refinelocation(self, x:float,y:float,z:float): valid_pt = len(fluid_state_data) > 0 if valid_pt: for ivar, lower, upper, c in zip(self._conditional_refinement_indices, self._conditional_lower_bound, self._conditional_upper_bound, self._conditional_refinement_factor): - test_val = fluid_state_data[ivar] if (test_val >= lower) and (test_val <= upper): refinement_factor = min(refinement_factor, c) return refinement_factor def setTableVars(self, table_vars_in:list[str]): - controlling_variables = self._Config.GetControllingVariables() + """Specify the thermocehmical state variables in the table file. + :param table_vars_in: names of variables to include in table. + :type table_vars_in: list[str] + :raises Exception: if unsupported variables are included. + """ + controlling_variables = self._Config.GetControllingVariables() self._table_vars = [] for cv in controlling_variables: if cv not in table_vars_in: @@ -275,6 +377,11 @@ def _checkIfVariableIsValid(self, var_to_check:str): def writeParaviewTable(self, filepath_out:str=None): + """Write the table level information to vtk files. + + :param filepath_out: file name header, defaults to None + :type filepath_out: str, optional + """ self.__printmsg("Writing vtk table to %s..." % filepath_out) for iLevel in range(self._N_table_levels): if self._N_table_levels > 1: @@ -311,23 +418,12 @@ def __writeParaViewVTK(self, cv_coordinates:np.ndarray[float], table_data:pandas def _writeAdditionalInfoToTable(self, fid): return - def smoothTableLevelData(self): - self.__printmsg("Smoothing table data...") - for iLevel in range(self._N_table_levels): - cv_level_scaled = self._scaler_controlling_variables.transform(self._table_nodes[iLevel]) - - data_on_table_level = self._data_in_table[iLevel].values - smoothener = RBFInterpolator(cv_level_scaled[:, :2], data_on_table_level, kernel="linear",smoothing=self.__smoothingLevel,neighbors=100) - smoothened_table_data = smoothener(cv_level_scaled[:, :2]) - for ivar, var in enumerate(list(self._data_in_table[iLevel].keys())): - if var not in self._Config.GetControllingVariables(): - self._data_in_table[iLevel][var] = smoothened_table_data[:, ivar] - self.__printmsg("Done") - return - def writeSU2Table(self, filepath_out:str=None): - + """Write table information to SU2 drg file. + :param filepath_out: file name header, defaults to None + :type filepath_out: str, optional + """ if filepath_out: file_out = filepath_out + ".drg" else: @@ -415,11 +511,24 @@ def writeSU2Table(self, filepath_out:str=None): fid.close() def setTableLevels(self, level_values:np.ndarray[float]): + """Specify the level values for 3D tables. + + :param level_values: array with table level values. + :type level_values: np.ndarray[float] + """ if self.__optionAppliesFor3D(): self._table_levels = level_values return def setTableLimits(self, lower_limit:float, upper_limit:float): + """Specify the upper and lower limit of the third table dimension. + + :param lower_limit: lower level value + :type lower_limit: float + :param upper_limit: upper level value + :type upper_limit: float + :raises Exception: if lower level value exceeds upper level value. + """ if self.__optionAppliesFor3D(): if lower_limit >= upper_limit: raise Exception("Table upper level value should exceed lower level value") @@ -428,6 +537,12 @@ def setTableLimits(self, lower_limit:float, upper_limit:float): return def setNTableLevels(self, N_levels:int=10): + """Specify the number of table levels. + + :param N_levels: number of equidistant table levels, defaults to 10 + :type N_levels: int, optional + :raises Exception: if the specified number of levels is negative + """ if self.__optionAppliesFor3D(): if N_levels <= 0: raise Exception("Number of table levels should be positive") @@ -435,11 +550,22 @@ def setNTableLevels(self, N_levels:int=10): return def insertTableLevel(self, level_value:float): + """Insert a table level for a specific value. + + :param level_value: table level value. + :type level_value: float + """ if self.__optionAppliesFor3D(): self._table_level_inserts.append(level_value) return def setNProcessors(self, N_cores:int=2): + """Specify the number of parallel workers used to generate the table levels in 3D tables. + + :param N_cores: Number of processors, defaults to 2 + :type N_cores: int, optional + :raises Exception: If fewer than 2 processors are selected. + """ if self.__optionAppliesFor3D(): if N_cores <= 1: raise Exception("At least two cores should be used") @@ -448,12 +574,24 @@ def setNProcessors(self, N_cores:int=2): return def setNNearestNeighbors(self, N_input:int=6): + """Specify the number of nearest neighbors used for fluid data interpolation. + + :param N_input: number of nearest neighbors, defaults to 6 + :type N_input: int, optional + :raises Exception: if fewer than one neighbors are selected. + """ if N_input < 1: raise Exception("Number of nearest neighbors should be strictly positive") self.__N_nearest_neighbors = N_input return def setInverseDistanceExponent(self, p_factor:float=2): + """Specify the inverse distance exponent used for fluid data interpolation. + + :param p_factor: inverse distance exponent value, defaults to 2 + :type p_factor: float, optional + :raises Exception: if the provided value is negative. + """ if p_factor <= 0: raise Exception("Inverse distance parameter value should be positive") self.__inverse_distance_exponent = p_factor @@ -469,6 +607,18 @@ def setSmoothingParameter(self, smoothing_factor:float=0): self.__smoothingLevel = smoothing_factor return + def setVerbosity(self, verbosity_level:int=1): + """Specify verbosity level used to print information in the terminal. + + :param verbosity_level: Verbosity level between 0 and 4, defaults to 1 + :type verbosity_level: int, optional + :raises Exception: if specified value lies outside 0-4 + """ + if verbosity_level < 0 or verbosity_level > 4: + raise Exception("Verbosity level should be between 0 and 4") + self._verbosity = verbosity_level + return + def __optionAppliesFor3D(self): if self._is2D(): print("Option does not apply for two-dimensional table, ignoring") @@ -482,36 +632,11 @@ def _is2D(self): def _is3D(self): return self._nDim_table==3 - def _processTableLevels(self): - if self._is3D(): - if not any(self._table_levels): - if self._tableUpperLevel and self._tableLowerLevel and self._N_table_levels: - self._table_levels = np.linspace(self._tableLowerLevel, self._tableUpperLevel, self._N_table_levels) - else: - raise Exception("No table level information provided, aborting") - - if any(self._table_level_inserts): - self._table_levels = np.append(self._table_levels, np.array(self._table_level_inserts)) - - unique_table_levels = np.unique(self._table_levels) - sorted_table_levels = np.sort(unique_table_levels) - self._table_levels = sorted_table_levels - self._N_table_levels = len(self._table_levels) - self._tableLowerLevel = min(self._table_levels) - self._tableUpperLevel = max(self._table_levels) - else: - self._table_levels = [0] - self._N_table_levels = 1 - return def __printmsg(self, msg:str): if self._verbosity > 0: print(msg) return - def setVerbosity(self, verbosity_level:int=1): - if verbosity_level < 0 or verbosity_level > 4: - raise Exception("Verbosity level should be between 0 and 4") - self._verbosity = verbosity_level - return + \ No newline at end of file From 36204b79249dca4ce40a55585e90ac1d128f340b Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Fri, 12 Jun 2026 13:28:34 +0200 Subject: [PATCH 15/44] Exclude interpolated burner flames from pv optimization --- Data_Processing/OptimizeProgressVariable.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/Data_Processing/OptimizeProgressVariable.py b/Data_Processing/OptimizeProgressVariable.py index 10e37b1..e91fe97 100644 --- a/Data_Processing/OptimizeProgressVariable.py +++ b/Data_Processing/OptimizeProgressVariable.py @@ -563,8 +563,9 @@ def __FilterFlamelets(self): if (val_phi >= self.__phi_min) and (val_phi <= self.__phi_max): flamelets = os.listdir(flamelet_dir + "/burnerflame_data/" + phi) for f in flamelets: - self.__valid_filepathnames.append(flamelet_dir + "/burnerflame_data/"+ phi + "/" + f) - j_flamelet += 1 + if "int" not in f: + self.__valid_filepathnames.append(flamelet_dir + "/burnerflame_data/"+ phi + "/" + f) + j_flamelet += 1 with open(self.__valid_filepathnames[0],'r') as fid: self.__flamelet_variables = fid.readline().strip().split(',') From 75d23f8ccf6c944020bb1aa824ca8fafbf4f4f9e Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Wed, 24 Jun 2026 11:12:23 +0200 Subject: [PATCH 16/44] Maximum number of iterations also applies to simplex algorithm --- Data_Processing/OptimizeProgressVariable.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Data_Processing/OptimizeProgressVariable.py b/Data_Processing/OptimizeProgressVariable.py index e91fe97..08d559c 100644 --- a/Data_Processing/OptimizeProgressVariable.py +++ b/Data_Processing/OptimizeProgressVariable.py @@ -468,7 +468,7 @@ def RunOptimizer(self): result.x, \ method='Nelder-Mead', \ bounds=bounds,\ - callback=self._Optimization_Callback) + callback=self._Optimization_Callback,options={"maxiter" : self._N_generations}) # Post-processing for best solution. self._Optimization_Callback(result.x) From 67f4a6743324afa29510f2a5c9fa793a1310f6a4 Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Wed, 24 Jun 2026 11:12:56 +0200 Subject: [PATCH 17/44] change meshTableLevel to public so it can be run in parallel --- Manifold_Generation/LUT/LUTGenerator_Base.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/Manifold_Generation/LUT/LUTGenerator_Base.py b/Manifold_Generation/LUT/LUTGenerator_Base.py index 3d7c3a5..304c777 100644 --- a/Manifold_Generation/LUT/LUTGenerator_Base.py +++ b/Manifold_Generation/LUT/LUTGenerator_Base.py @@ -145,7 +145,7 @@ def _generateTableLevelData(self): self._data_in_table = [None]*self._N_table_levels if self.__run_parallel: pool = Pool(self.__N_cores) - results = pool.map(self.__meshTableLevel, [i for i in range(self._N_table_levels)]) + results = pool.map(self.meshTableLevel, [i for i in range(self._N_table_levels)]) pool.close() for iLevel in range(self._N_table_levels): @@ -155,7 +155,7 @@ def _generateTableLevelData(self): self._data_in_table[iLevel] = results[iLevel][3] else: for iLevel in range(len(self._table_levels)): - tableLevel = self.__meshTableLevel(iLevel) + tableLevel = self.meshTableLevel(iLevel) self._table_nodes[iLevel] = tableLevel[0] self._table_connectivity[iLevel] = tableLevel[1] @@ -232,7 +232,7 @@ def applyRefinementWithin(self, varname:str, lowerbound:float=-np.inf, upperboun return - def __meshTableLevel(self, level_index:int): + def meshTableLevel(self, level_index:int): """Discretize and interpolate the fluid data of a single table level. :param level_index: table level index From 278f790af7dadf97159120a7da9c742dff8c5acc Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Wed, 24 Jun 2026 14:51:52 +0200 Subject: [PATCH 18/44] Fixed a bug where table generation would fail if CoolProp would not converge --- Manifold_Generation/LUT/LUTGenerators.py | 2 +- .../TableGeneration/NICFD/LUT_test.ref | 3018 +++++------------ .../TableGeneration/NICFD/twophase_lut.py | 13 +- .../FGM/hydrogen_LUT/1_generate_config.py | 17 + .../FGM/hydrogen_LUT/2_compute_flamelets.py | 0 .../hydrogen_LUT/3_accumulate_flamelets.py | 11 + .../FGM/hydrogen_LUT/3_generate_table.py | 15 + 7 files changed, 912 insertions(+), 2164 deletions(-) create mode 100644 TestCases/FGM/hydrogen_LUT/1_generate_config.py create mode 100644 TestCases/FGM/hydrogen_LUT/2_compute_flamelets.py create mode 100644 TestCases/FGM/hydrogen_LUT/3_accumulate_flamelets.py create mode 100644 TestCases/FGM/hydrogen_LUT/3_generate_table.py diff --git a/Manifold_Generation/LUT/LUTGenerators.py b/Manifold_Generation/LUT/LUTGenerators.py index a5cfe95..523281b 100644 --- a/Manifold_Generation/LUT/LUTGenerators.py +++ b/Manifold_Generation/LUT/LUTGenerators.py @@ -125,8 +125,8 @@ def _calculateTableStateData(self, cv_table_nodes:np.ndarray[float]): rhoe_table_nodes = self._scaler_controlling_variables.inverse_transform(cv_table_nodes[:, :2]) state_data_out = np.zeros([len(rhoe_table_nodes), EntropicVars.N_STATE_VARS.value]) for i, rhoe in enumerate(rhoe_table_nodes): - self.__datagenerator.UpdateFluid(rhoe[0], rhoe[1]) try: + self.__datagenerator.UpdateFluid(rhoe[0], rhoe[1]) state_data, correct_phase = self.__datagenerator.GetStateVector() if correct_phase: state_data_out[i] = state_data diff --git a/RegressionTests/TableGeneration/NICFD/LUT_test.ref b/RegressionTests/TableGeneration/NICFD/LUT_test.ref index 1c2f43c..d6a67eb 100644 --- a/RegressionTests/TableGeneration/NICFD/LUT_test.ref +++ b/RegressionTests/TableGeneration/NICFD/LUT_test.ref @@ -5,14 +5,20 @@ Dragon library [Version] 1.0.1 +Fluid: +CarbonDioxide +Equation of state: +HEOS +Table contains two-phase data + [Number of points] -725 +296 [Number of triangles] -1390 +525 [Number of hull points] -71 +65 [Number of variables] 27 @@ -49,2197 +55,897 @@ Dragon library
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-105 2 -106 -104 -103 -107 3 -108 4 -109 5 -110 6 -111 7 -112 8 -113 9 -114 10 -115 11 -116 12 -117 13 -118 14 -119 15 -120 16 -121 17 -122 18 -123 19 -124 20 -125 21 -126 22 -127 23 -35 -128 -129 24 -130 25 -131 26 -132 27 -133 28 -134 29 -135 30 -136 31 -137 32 -138 33 34 +75 +76 +77 +78 +79 +80 +81 +82 +83 +84 +85 +86 +87 +88 +89 +90 +91 +92 +93 +94 +95 +96 +97 +98 +99 +100 +101 +102 +103 +104 +105 +106 diff --git a/RegressionTests/TableGeneration/NICFD/twophase_lut.py b/RegressionTests/TableGeneration/NICFD/twophase_lut.py index 9483d23..64a5c86 100755 --- a/RegressionTests/TableGeneration/NICFD/twophase_lut.py +++ b/RegressionTests/TableGeneration/NICFD/twophase_lut.py @@ -1,7 +1,7 @@ #!/usr/bin/env python3 from su2dataminer.config import Config_NICFD -from su2dataminer.manifold import SU2TableGenerator_NICFD +from su2dataminer.manifold import TableGenerator_NICFD config = Config_NICFD() config.SetFluid("CarbonDioxide") @@ -17,9 +17,8 @@ config.EnableSuperCritical(True) config.IncludeTransportProperties(True) -tablegen = SU2TableGenerator_NICFD(config) -tablegen.SetCellSize_Coarse(8e-2) -tablegen.SetCellSize_Refined(4e-2) -tablegen.SetTableDiscretization("adaptive") -tablegen.GenerateTable() -tablegen.WriteTableFile("LUT_test") \ No newline at end of file +tablegen = TableGenerator_NICFD(config) +tablegen.setMaximumCellSize(8e-2) +tablegen.generateTable() +tablegen.writeSU2Table("LUT_test") +tablegen.writeParaviewTable("LUT_test") \ No newline at end of file diff --git a/TestCases/FGM/hydrogen_LUT/1_generate_config.py b/TestCases/FGM/hydrogen_LUT/1_generate_config.py new file mode 100644 index 0000000..07352d1 --- /dev/null +++ b/TestCases/FGM/hydrogen_LUT/1_generate_config.py @@ -0,0 +1,17 @@ +from su2dataminer.config import Config_FGM + +config = Config_FGM() +config.SetFuelDefinition(["H2"], [1.0]) +config.SetReactionMechanism("h2o2.yaml") +config.SetTransportModel("multicomponent") +config.SetUnbTempBounds(300, 600) +config.DefineMixtureStatus(False) +config.SetMixtureBounds(0.25, 3.0) +config.SetNpMix(60) +config.SetNpTemp(60) +config.RunFreeFlames(True) +config.RunBurnerFlames(True) +config.RunEquilibrium(True) +config.SetAverageLewisNumbers(0.5, 300) +config.SetConfigName("hydrogen_tabulation") +config.SaveConfig() \ No newline at end of file diff --git a/TestCases/FGM/hydrogen_LUT/2_compute_flamelets.py b/TestCases/FGM/hydrogen_LUT/2_compute_flamelets.py new file mode 100644 index 0000000..e69de29 diff --git a/TestCases/FGM/hydrogen_LUT/3_accumulate_flamelets.py b/TestCases/FGM/hydrogen_LUT/3_accumulate_flamelets.py new file mode 100644 index 0000000..28f271b --- /dev/null +++ b/TestCases/FGM/hydrogen_LUT/3_accumulate_flamelets.py @@ -0,0 +1,11 @@ +import numpy as np +from su2dataminer.config import Config_FGM +from su2dataminer.process_data import FlameletConcatenator +c = Config_FGM("hydrogen_tabulation.cfg") +pv_sp, pv_w = np.load("PV_Optimization/hydrogen_tabulation_PV_Def_optim.npy"), np.load("PV_Optimization/hydrogen_tabulation_Weights_optim.npy") + +c.SetProgressVariableDefinition(pv_sp, pv_w) +c.SaveConfig() + +FC = FlameletConcatenator(c) +FC.ConcatenateFlameletData() diff --git a/TestCases/FGM/hydrogen_LUT/3_generate_table.py b/TestCases/FGM/hydrogen_LUT/3_generate_table.py new file mode 100644 index 0000000..829e00c --- /dev/null +++ b/TestCases/FGM/hydrogen_LUT/3_generate_table.py @@ -0,0 +1,15 @@ +from su2dataminer.config import Config_FGM +from su2dataminer.manifold import TableGenerator_FGM +config = Config_FGM("hydrogen_tabulation.cfg") + +tgen = TableGenerator_FGM(config) +tgen.setTableLimits(7e-3, 2.2e-2) +tgen.setNTableLevels(20) +tgen.setVerbosity(1) +tgen.setNNearestNeighbors(19) +tgen.setInverseDistanceExponent(2.0) +tgen.setTargetNodeCount(3000) +tgen.setSmoothingParameter(0.1) +tgen.generateTable() + +tgen.writeParaviewTable("hydrogen_table_test") \ No newline at end of file From ab3c748fc1a990f4f74490431888b534bccc5a5f Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Wed, 24 Jun 2026 14:52:04 +0200 Subject: [PATCH 19/44] whitespaces --- Manifold_Generation/LUT/LUTGenerator_Base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Manifold_Generation/LUT/LUTGenerator_Base.py b/Manifold_Generation/LUT/LUTGenerator_Base.py index 304c777..7f0076f 100644 --- a/Manifold_Generation/LUT/LUTGenerator_Base.py +++ b/Manifold_Generation/LUT/LUTGenerator_Base.py @@ -161,7 +161,7 @@ def _generateTableLevelData(self): self._table_connectivity[iLevel] = tableLevel[1] self._table_hullnodes[iLevel] = tableLevel[2] self._data_in_table[iLevel] = tableLevel[3] - return + return def _smoothTableLevelData(self): self.__printmsg("Smoothing table data...") From 8ef10a447f68253cfc67212e64d40f9c1025041e Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Wed, 24 Jun 2026 14:52:46 +0200 Subject: [PATCH 20/44] precommit --- TestCases/FGM/hydrogen_LUT/1_generate_config.py | 2 +- TestCases/FGM/hydrogen_LUT/3_accumulate_flamelets.py | 2 +- TestCases/FGM/hydrogen_LUT/3_generate_table.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/TestCases/FGM/hydrogen_LUT/1_generate_config.py b/TestCases/FGM/hydrogen_LUT/1_generate_config.py index 07352d1..0e20ad6 100644 --- a/TestCases/FGM/hydrogen_LUT/1_generate_config.py +++ b/TestCases/FGM/hydrogen_LUT/1_generate_config.py @@ -1,4 +1,4 @@ -from su2dataminer.config import Config_FGM +from su2dataminer.config import Config_FGM config = Config_FGM() config.SetFuelDefinition(["H2"], [1.0]) diff --git a/TestCases/FGM/hydrogen_LUT/3_accumulate_flamelets.py b/TestCases/FGM/hydrogen_LUT/3_accumulate_flamelets.py index 28f271b..3f2da15 100644 --- a/TestCases/FGM/hydrogen_LUT/3_accumulate_flamelets.py +++ b/TestCases/FGM/hydrogen_LUT/3_accumulate_flamelets.py @@ -1,5 +1,5 @@ import numpy as np -from su2dataminer.config import Config_FGM +from su2dataminer.config import Config_FGM from su2dataminer.process_data import FlameletConcatenator c = Config_FGM("hydrogen_tabulation.cfg") pv_sp, pv_w = np.load("PV_Optimization/hydrogen_tabulation_PV_Def_optim.npy"), np.load("PV_Optimization/hydrogen_tabulation_Weights_optim.npy") diff --git a/TestCases/FGM/hydrogen_LUT/3_generate_table.py b/TestCases/FGM/hydrogen_LUT/3_generate_table.py index 829e00c..e35b188 100644 --- a/TestCases/FGM/hydrogen_LUT/3_generate_table.py +++ b/TestCases/FGM/hydrogen_LUT/3_generate_table.py @@ -1,4 +1,4 @@ -from su2dataminer.config import Config_FGM +from su2dataminer.config import Config_FGM from su2dataminer.manifold import TableGenerator_FGM config = Config_FGM("hydrogen_tabulation.cfg") From ceff983f0025c91a9730cc909dee313468b95f67 Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Wed, 24 Jun 2026 14:53:32 +0200 Subject: [PATCH 21/44] Update regression test --- .../FluidGeneration/MM/MLP_data_test_ref.csv | 279 +++++++++--------- 1 file changed, 139 insertions(+), 140 deletions(-) diff --git a/RegressionTests/FluidGeneration/MM/MLP_data_test_ref.csv b/RegressionTests/FluidGeneration/MM/MLP_data_test_ref.csv index 08ae9c0..0eeb661 100644 --- a/RegressionTests/FluidGeneration/MM/MLP_data_test_ref.csv +++ b/RegressionTests/FluidGeneration/MM/MLP_data_test_ref.csv @@ -1,141 +1,140 @@ Density,Energy,s,dsdrho_e,dsde_rho,d2sdrho2,d2sdedrho,d2sde2,T,p,c2,dTdrho_e,dTde_rho,dpdrho_e,dpde_rho,dhdrho_e,dhde_rho,dhdrho_p,dhdp_rho,dsdp_rho,dsdrho_p,cp 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-fluid_data* *.cfg From 7e7d1fa48d9702b930027b735e54e684838b93cf Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Thu, 25 Jun 2026 13:57:19 +0200 Subject: [PATCH 23/44] Add training data files instead of generating training data during regression test --- .../FluidTraining/MM_PINN/fluid_data_full.csv | 1398 +++++++++++++++++ .../FluidTraining/MM_PINN/fluid_data_test.csv | 140 ++ .../MM_PINN/fluid_data_train.csv | 1118 +++++++++++++ .../FluidTraining/MM_PINN/fluid_data_val.csv | 142 ++ 4 files changed, 2798 insertions(+) create mode 100644 RegressionTests/FluidTraining/MM_PINN/fluid_data_full.csv create mode 100644 RegressionTests/FluidTraining/MM_PINN/fluid_data_test.csv create mode 100644 RegressionTests/FluidTraining/MM_PINN/fluid_data_train.csv create mode 100644 RegressionTests/FluidTraining/MM_PINN/fluid_data_val.csv diff --git a/RegressionTests/FluidTraining/MM_PINN/fluid_data_full.csv b/RegressionTests/FluidTraining/MM_PINN/fluid_data_full.csv new file mode 100644 index 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+15.619957044472358,675363.4719606028,1473.32948619875,-3.133863795755868,0.001587439791292238,0.2094438130469192,-3.061940715694318e-07,-1.1246879310746208e-09,629.9451516116784,481662.15604598937,30341.497449514627,0.12150726717035762,0.00044631091720328283,29574.90173725473,0.3883144312536605,-80.75749796460364,1.024860147191703,-78136.41473884476,2.639253307900444,0.004088026772961387,-124.03685390535544,2306.9139307399937 +61.696325463486005,607182.5702282361,1299.2079404308367,-0.6855223902738767,0.0016532454791076602,0.012967164200803894,-3.287858561683424e-07,-1.236970432187775e-09,604.8708510848312,1578348.4865210245,22426.89908313394,0.12029246906063933,0.00045256882147231745,21623.33181546355,1.9379293386817265,-64.17245220604892,1.0314107740472915,-11572.609297710414,0.5322231071381103,0.0008530989474736361,-19.13236400291902,2347.646740146368 +70.64853972612457,743544.3736940462,1508.0327525455943,-0.6222644663668035,0.0015014110359133108,0.00986301249704893,-2.360583225020115e-07,-9.710240547395868e-10,666.0401289722093,2068625.0396544202,26977.86367283437,0.10471770341594919,0.00043075545016255876,26098.143379042365,2.122605144606868,-45.044997634030025,1.030044572086491,-12709.789072819287,0.485273756498535,0.0007073435394840665,-19.08261757806128,2448.161276386791 +484.3683226775513,641273.021094419,1274.9901353113687,-0.13618527184018853,0.0015134024825413693,-0.0005526205431019888,-2.423607091749541e-07,-9.84138611546398e-10,660.7627591047412,21111892.49647162,180839.18863716023,0.1058164848679947,0.0004296822238687793,176222.87655135166,51.300247643862505,273.83383039272803,1.1059116487227707,-3525.113365779153,0.02155762787735939,2.9500880639948153e-05,-5.334915319009932,2572.7062306100215 From a8cffcb0667593a2dbe850eb530598e1976c95c2 Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Thu, 25 Jun 2026 13:58:11 +0200 Subject: [PATCH 24/44] Ommit data generation from regression test --- .../FluidTraining/MM_PINN/SU2_MLP.mlp | 34 +++++++++---------- .../FluidTraining/MM_PINN/SU2_MLP_ref.mlp | 34 +++++++++---------- .../FluidTraining/MM_PINN/train_MLP.py | 8 +---- 3 files changed, 35 insertions(+), 41 deletions(-) diff --git a/RegressionTests/FluidTraining/MM_PINN/SU2_MLP.mlp b/RegressionTests/FluidTraining/MM_PINN/SU2_MLP.mlp index 092d345..2626781 100644 --- a/RegressionTests/FluidTraining/MM_PINN/SU2_MLP.mlp +++ b/RegressionTests/FluidTraining/MM_PINN/SU2_MLP.mlp @@ -21,8 +21,8 @@ Energy minmax [input normalization] -+8.5714335909869960e-05 +6.2299851501995522e+02 --4.0535996229226606e+04 +7.7763482467061304e+05 ++8.5714335909869988e-05 +6.2299851501995454e+02 +-4.0535996229164477e+04 +7.7763482455932663e+05 [output names] s @@ -31,29 +31,29 @@ s minmax [output normalization] -+2.4740403703536168e+02 +2.2485138596991110e+03 ++3.2143198366816824e+02 +2.2485138596991110e+03 [weights per layer] -+2.6683041379426203e-01 -1.3569432611562782e+00 +1.0908299800010237e+00 -1.3575138607955064e+00 +1.0289155960334102e+00 -4.4118958250080709e-01 +8.7657942719648996e-01 +1.1102892518068976e+00 +7.2477426243428023e-01 -1.4434075764637788e+00 -+1.3827373736151567e+00 -8.3054341555667288e-01 -1.0737125496097386e+00 +1.2655778018547326e-01 +1.2656017119967911e+00 -2.2729952853675223e-01 +1.2404410860696409e-01 -6.1450205324755824e-01 -1.3117936970934467e+00 -8.0548575947520119e-03 ++2.1954651809566711e-01 -1.3514930816123307e+00 +1.1684071833808520e+00 -1.4270879137439410e+00 +1.1672252346454879e+00 -4.8091454288292834e-01 +9.3854445802959641e-01 +1.2446153826023456e+00 +8.1891259676852757e-01 -1.6350080001134377e+00 ++1.7410234655089991e+00 -2.6761678155405594e-01 -9.3008864776142386e-01 +3.8955549207987988e-01 +1.3411605353575835e+00 -3.8397693755448131e-02 +1.6056355533461966e-01 -4.9150843063928684e-01 -1.3016525223891096e+00 -6.9731124235690903e-02 -+1.2363751540879403e-01 --4.4373633927168493e-01 --2.7590857953760262e-01 -+3.3550987252179665e-01 --1.0531449691544227e-01 --4.6458338791457271e-01 -+5.4087471342507243e-01 --1.3380749635849040e-01 --6.6414120267902776e-03 -+2.4615528584943233e-01 ++3.1628521132773002e-01 +-5.1623566306905377e-01 +-2.7982963702122149e-01 ++3.8198181597161607e-01 +-1.1033043703562426e-01 +-5.4218702399325458e-01 ++6.7330492808182318e-01 +-1.8740708225395042e-01 ++1.9678718288278846e-01 ++6.7603113678014401e-02 [biases per layer] +0.0000000000000000e+00 +0.0000000000000000e+00 --1.4305286272515819e-01 +4.8828106730780467e-02 +9.7095749970430967e-02 -8.1191845288328832e-02 -2.2882081513239144e-01 -1.1299216940908136e-01 -6.1973415709344566e-02 -6.7796272439815064e-02 -2.9692143730566334e-01 -9.9000625176986781e-02 -+1.3698362304010303e-01 ++4.7209948245282039e-02 +1.3989535351341278e-01 +1.2288571689087474e-01 -5.8042350656257267e-03 -1.7110797579602918e-01 -2.0052628053298809e-02 +8.5043957961967856e-02 +5.8627602958463643e-02 -1.9498819377321752e-01 -2.3592404526073443e-01 +-8.2173643770685134e-01 diff --git a/RegressionTests/FluidTraining/MM_PINN/SU2_MLP_ref.mlp b/RegressionTests/FluidTraining/MM_PINN/SU2_MLP_ref.mlp index 092d345..2626781 100644 --- a/RegressionTests/FluidTraining/MM_PINN/SU2_MLP_ref.mlp +++ b/RegressionTests/FluidTraining/MM_PINN/SU2_MLP_ref.mlp @@ -21,8 +21,8 @@ Energy minmax [input normalization] -+8.5714335909869960e-05 +6.2299851501995522e+02 --4.0535996229226606e+04 +7.7763482467061304e+05 ++8.5714335909869988e-05 +6.2299851501995454e+02 +-4.0535996229164477e+04 +7.7763482455932663e+05 [output names] s @@ -31,29 +31,29 @@ s minmax [output normalization] -+2.4740403703536168e+02 +2.2485138596991110e+03 ++3.2143198366816824e+02 +2.2485138596991110e+03 [weights per layer] -+2.6683041379426203e-01 -1.3569432611562782e+00 +1.0908299800010237e+00 -1.3575138607955064e+00 +1.0289155960334102e+00 -4.4118958250080709e-01 +8.7657942719648996e-01 +1.1102892518068976e+00 +7.2477426243428023e-01 -1.4434075764637788e+00 -+1.3827373736151567e+00 -8.3054341555667288e-01 -1.0737125496097386e+00 +1.2655778018547326e-01 +1.2656017119967911e+00 -2.2729952853675223e-01 +1.2404410860696409e-01 -6.1450205324755824e-01 -1.3117936970934467e+00 -8.0548575947520119e-03 ++2.1954651809566711e-01 -1.3514930816123307e+00 +1.1684071833808520e+00 -1.4270879137439410e+00 +1.1672252346454879e+00 -4.8091454288292834e-01 +9.3854445802959641e-01 +1.2446153826023456e+00 +8.1891259676852757e-01 -1.6350080001134377e+00 ++1.7410234655089991e+00 -2.6761678155405594e-01 -9.3008864776142386e-01 +3.8955549207987988e-01 +1.3411605353575835e+00 -3.8397693755448131e-02 +1.6056355533461966e-01 -4.9150843063928684e-01 -1.3016525223891096e+00 -6.9731124235690903e-02 -+1.2363751540879403e-01 --4.4373633927168493e-01 --2.7590857953760262e-01 -+3.3550987252179665e-01 --1.0531449691544227e-01 --4.6458338791457271e-01 -+5.4087471342507243e-01 --1.3380749635849040e-01 --6.6414120267902776e-03 -+2.4615528584943233e-01 ++3.1628521132773002e-01 +-5.1623566306905377e-01 +-2.7982963702122149e-01 ++3.8198181597161607e-01 +-1.1033043703562426e-01 +-5.4218702399325458e-01 ++6.7330492808182318e-01 +-1.8740708225395042e-01 ++1.9678718288278846e-01 ++6.7603113678014401e-02 [biases per layer] +0.0000000000000000e+00 +0.0000000000000000e+00 --1.4305286272515819e-01 +4.8828106730780467e-02 +9.7095749970430967e-02 -8.1191845288328832e-02 -2.2882081513239144e-01 -1.1299216940908136e-01 -6.1973415709344566e-02 -6.7796272439815064e-02 -2.9692143730566334e-01 -9.9000625176986781e-02 -+1.3698362304010303e-01 ++4.7209948245282039e-02 +1.3989535351341278e-01 +1.2288571689087474e-01 -5.8042350656257267e-03 -1.7110797579602918e-01 -2.0052628053298809e-02 +8.5043957961967856e-02 +5.8627602958463643e-02 -1.9498819377321752e-01 -2.3592404526073443e-01 +-8.2173643770685134e-01 diff --git a/RegressionTests/FluidTraining/MM_PINN/train_MLP.py b/RegressionTests/FluidTraining/MM_PINN/train_MLP.py index 9f79f54..9c35f23 100755 --- a/RegressionTests/FluidTraining/MM_PINN/train_MLP.py +++ b/RegressionTests/FluidTraining/MM_PINN/train_MLP.py @@ -2,7 +2,6 @@ import os from su2dataminer.manifold import TrainMLP_NICFD -from su2dataminer.generate_data import DataGenerator_CoolProp from Common.DataDrivenConfig import Config_NICFD C = Config_NICFD() @@ -10,7 +9,7 @@ C.SetEquationOfState("HEOS") C.UseAutoRange(True) C.UsePTGrid(False) -C.SetNpDensity(100) +C.SetNpDensity(50) C.SetNpEnergy(50) C.SetAlphaExpo(-1.8269e+00) C.SetLRDecay(+9.8959e-01) @@ -21,11 +20,6 @@ C.SetBatchExpo(4) C.SaveConfig() -D = DataGenerator_CoolProp(C) -D.PreprocessData() -D.ComputeData() -D.SaveData() - M = TrainMLP_NICFD(C) M.SetTrainHardware("CPU",0) M.SetNEpochs(20) From ef1017b870304f88e9edfc4c554dda2c9bec0b3b Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Thu, 25 Jun 2026 14:02:10 +0200 Subject: [PATCH 25/44] trailing whitespaces --- TestCases/NICFD/LUT_Twophase/generate_table.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/TestCases/NICFD/LUT_Twophase/generate_table.py b/TestCases/NICFD/LUT_Twophase/generate_table.py index 349aff6..e2b5dda 100644 --- a/TestCases/NICFD/LUT_Twophase/generate_table.py +++ b/TestCases/NICFD/LUT_Twophase/generate_table.py @@ -31,7 +31,7 @@ # or the maximum cell size #tgen.setMaximumCellSize(2.0e-2) -# Custom refinement; reduce cell size by a specified factor where the +# Custom refinement; reduce cell size by a specified factor where the # thermodynamic quantity is within specified limits tgen.addRefinementCriterion("s", lowerbound=725,upperbound=780, coef=0.4) tgen.addRefinementCriterion("Density",0, 50, 0.4) From 8b0405db717e4971ac3c6fb04863a48ff2fe626d Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Thu, 25 Jun 2026 14:08:12 +0200 Subject: [PATCH 26/44] Ignore flamelet data in test case --- TestCases/LUT/Flamelet_Table_H2/.gitignore | 1 + 1 file changed, 1 insertion(+) create mode 100644 TestCases/LUT/Flamelet_Table_H2/.gitignore diff --git a/TestCases/LUT/Flamelet_Table_H2/.gitignore b/TestCases/LUT/Flamelet_Table_H2/.gitignore new file mode 100644 index 0000000..629f0c3 --- /dev/null +++ b/TestCases/LUT/Flamelet_Table_H2/.gitignore @@ -0,0 +1 @@ +flamelet_data/** From fdd60e84ee112924ef86dc64cfc3abc0ff57934d Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Fri, 26 Jun 2026 09:15:53 +0200 Subject: [PATCH 27/44] Added test case for hydrogen table --- .../Flamelet_Table_H2/0_generate_config.py | 38 +++++++++++++++++++ .../1_generate_flamelet_data.py | 22 +++++++++++ .../2_optimize_progress_variabele.py | 14 +++++++ .../3_collect_flamelet_data.py | 10 +++++ 4 files changed, 84 insertions(+) create mode 100644 TestCases/LUT/Flamelet_Table_H2/0_generate_config.py create mode 100644 TestCases/LUT/Flamelet_Table_H2/1_generate_flamelet_data.py create mode 100644 TestCases/LUT/Flamelet_Table_H2/2_optimize_progress_variabele.py create mode 100644 TestCases/LUT/Flamelet_Table_H2/3_collect_flamelet_data.py diff --git a/TestCases/LUT/Flamelet_Table_H2/0_generate_config.py b/TestCases/LUT/Flamelet_Table_H2/0_generate_config.py new file mode 100644 index 0000000..21f06f7 --- /dev/null +++ b/TestCases/LUT/Flamelet_Table_H2/0_generate_config.py @@ -0,0 +1,38 @@ +from Common.DataDrivenConfig import Config_FGM +import os + +Config = Config_FGM() +Config.SetConfigName("TableGeneration_H2_n") + +# Methane-air flamelets at a single equivalence ratio phi = 0.80 +Config.SetFuelDefinition(fuel_species=["H2"], fuel_weights=[1.0]) +Config.SetReactionMechanism('h2o2.yaml') + +# Single phi = 0.80 → single mixture fraction point (min == max) +Config.SetMixtureBounds(0.30, 2.0) +Config.SetNpMix(20) + +Config.SetUnbTempBounds(270, 600) +Config.SetNpTemp(49) +Config.SetNpMdot(40) # burner flames across the mdot range +Config.SetMdotDHTarget(20000.0) # J/kg target ΔH between flames +Config.SetNpMdotExtra(40) # synthetic flames linearly interpolated from lowest-mdot burner flame to equilibrium +Config.SetSrcInterpExponent(2.0) # Decay of interpolated flamelets +Config.SetInitialGridLength(1.8e-2) # Initial flamelet domain length in metres + +# Explicitly select which flamelet types to generate. +Config.setFlameletTypes(["FREEFLAME","BURNERFLAME","EQUILIBRIUM","INT_BURNERFLAME"]) + +# Enable preferential diffusion through the multicomponent transport model. +Config.SetTransportModel('multicomponent') +Config.SetConcatenationFileHeader("LUT_data_n") + + +# Preparing flamelet output directory. +flamelet_data_dir = os.getcwd() + os.sep + "flamelet_data_n" +if not os.path.isdir(flamelet_data_dir): + os.mkdir(flamelet_data_dir) +Config.SetOutputDir(flamelet_data_dir) + +Config.PrintBanner() +Config.SaveConfig() diff --git a/TestCases/LUT/Flamelet_Table_H2/1_generate_flamelet_data.py b/TestCases/LUT/Flamelet_Table_H2/1_generate_flamelet_data.py new file mode 100644 index 0000000..b346afa --- /dev/null +++ b/TestCases/LUT/Flamelet_Table_H2/1_generate_flamelet_data.py @@ -0,0 +1,22 @@ +# Generate flamelet data for a single phi = 0.80 methane-air flame + +# Limit inner thread pools BEFORE any library imports to prevent oversubscription +import os +os.environ.setdefault("OMP_NUM_THREADS", "1") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "1") +os.environ.setdefault("MKL_NUM_THREADS", "1") +os.environ.setdefault("VECLIB_MAXIMUM_THREADS", "1") +os.environ.setdefault("NUMEXPR_NUM_THREADS", "1") + +from Common.DataDrivenConfig import Config_FGM +from Data_Generation.DataGenerator_FGM import ComputeFlameletData + +# Load FGM configuration +Config = Config_FGM("TableGeneration_H2_n.cfg") + +# refinement values: +# free_flame_refine={"ratio": 3.0, "slope": 0.1, "curve": 0.1, "prune":0.01}, +# this leads to Np = 180 + +ComputeFlameletData(Config, run_parallel=False, N_processors=1, loglevel=0) + diff --git a/TestCases/LUT/Flamelet_Table_H2/2_optimize_progress_variabele.py b/TestCases/LUT/Flamelet_Table_H2/2_optimize_progress_variabele.py new file mode 100644 index 0000000..41f4e5a --- /dev/null +++ b/TestCases/LUT/Flamelet_Table_H2/2_optimize_progress_variabele.py @@ -0,0 +1,14 @@ +from su2dataminer.config import Config_FGM +from su2dataminer.process_data import PVOptimizer + +config = Config_FGM("TableGeneration_H2.cfg") + +PVO = PVOptimizer(config) +PVO.SetAdditionalProgressVariables(["Temperature","Heat_Release"]) +PVO.SetNGenerations(30) + +PVO.OptimizePV() + +config.SetProgressVariableDefinition(PVO.GetOptimizedSpecies(), PVO.GetOptimizedWeights()) +config.PrintBanner() +config.SaveConfig() diff --git a/TestCases/LUT/Flamelet_Table_H2/3_collect_flamelet_data.py b/TestCases/LUT/Flamelet_Table_H2/3_collect_flamelet_data.py new file mode 100644 index 0000000..2064d1d --- /dev/null +++ b/TestCases/LUT/Flamelet_Table_H2/3_collect_flamelet_data.py @@ -0,0 +1,10 @@ +from su2dataminer.config import Config_FGM +from su2dataminer.process_data import FlameletConcatenator + +config = Config_FGM("TableGeneration_H2.cfg") +config.SetAverageLewisNumbers(0.5, 300) +config.SaveConfig() + +FC = FlameletConcatenator(config) +FC.ConcatenateFlameletData() + From cf9380a86e7ba75bbb484519bf21ee901dd55677 Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Fri, 26 Jun 2026 09:16:31 +0200 Subject: [PATCH 28/44] Flamelet data generation in parallel --- TestCases/LUT/Flamelet_Table_H2/0_generate_config.py | 9 ++++++--- .../LUT/Flamelet_Table_H2/1_generate_flamelet_data.py | 4 ++-- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/TestCases/LUT/Flamelet_Table_H2/0_generate_config.py b/TestCases/LUT/Flamelet_Table_H2/0_generate_config.py index 21f06f7..5d2a439 100644 --- a/TestCases/LUT/Flamelet_Table_H2/0_generate_config.py +++ b/TestCases/LUT/Flamelet_Table_H2/0_generate_config.py @@ -2,7 +2,7 @@ import os Config = Config_FGM() -Config.SetConfigName("TableGeneration_H2_n") +Config.SetConfigName("TableGeneration_H2") # Methane-air flamelets at a single equivalence ratio phi = 0.80 Config.SetFuelDefinition(fuel_species=["H2"], fuel_weights=[1.0]) @@ -21,11 +21,14 @@ Config.SetInitialGridLength(1.8e-2) # Initial flamelet domain length in metres # Explicitly select which flamelet types to generate. -Config.setFlameletTypes(["FREEFLAME","BURNERFLAME","EQUILIBRIUM","INT_BURNERFLAME"]) +Config.RunFreeFlames(True) +Config.RunBurnerFlames(True) +Config.RunExtraInterpolatedBurnerFlames(True) +Config.RunEquilibrium(True) # Enable preferential diffusion through the multicomponent transport model. Config.SetTransportModel('multicomponent') -Config.SetConcatenationFileHeader("LUT_data_n") +Config.SetConcatenationFileHeader("LUT_data") # Preparing flamelet output directory. diff --git a/TestCases/LUT/Flamelet_Table_H2/1_generate_flamelet_data.py b/TestCases/LUT/Flamelet_Table_H2/1_generate_flamelet_data.py index b346afa..ef21523 100644 --- a/TestCases/LUT/Flamelet_Table_H2/1_generate_flamelet_data.py +++ b/TestCases/LUT/Flamelet_Table_H2/1_generate_flamelet_data.py @@ -12,11 +12,11 @@ from Data_Generation.DataGenerator_FGM import ComputeFlameletData # Load FGM configuration -Config = Config_FGM("TableGeneration_H2_n.cfg") +Config = Config_FGM("TableGeneration_H2.cfg") # refinement values: # free_flame_refine={"ratio": 3.0, "slope": 0.1, "curve": 0.1, "prune":0.01}, # this leads to Np = 180 -ComputeFlameletData(Config, run_parallel=False, N_processors=1, loglevel=0) +ComputeFlameletData(Config, run_parallel=True, N_processors=2, loglevel=0) From 14ce4f7ca9210ac2dd79f8bfe82a535815ff8e59 Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Wed, 15 Jul 2026 09:23:15 +0200 Subject: [PATCH 29/44] precommit --- .../Flamelet_Table_H2/0_generate_config.py | 2 +- .../1_generate_flamelet_data.py | 4 ++-- .../2_optimize_progress_variabele.py | 2 +- .../3_collect_flamelet_data.py | 4 ++-- .../LUT/Flamelet_Table_H2/4_generate_table.py | 20 +++++++++++++++++++ 5 files changed, 26 insertions(+), 6 deletions(-) create mode 100644 TestCases/LUT/Flamelet_Table_H2/4_generate_table.py diff --git a/TestCases/LUT/Flamelet_Table_H2/0_generate_config.py b/TestCases/LUT/Flamelet_Table_H2/0_generate_config.py index 5d2a439..db73e45 100644 --- a/TestCases/LUT/Flamelet_Table_H2/0_generate_config.py +++ b/TestCases/LUT/Flamelet_Table_H2/0_generate_config.py @@ -15,7 +15,7 @@ Config.SetUnbTempBounds(270, 600) Config.SetNpTemp(49) Config.SetNpMdot(40) # burner flames across the mdot range -Config.SetMdotDHTarget(20000.0) # J/kg target ΔH between flames +Config.SetMdotDHTarget(50000.0) # J/kg target ΔH between flames Config.SetNpMdotExtra(40) # synthetic flames linearly interpolated from lowest-mdot burner flame to equilibrium Config.SetSrcInterpExponent(2.0) # Decay of interpolated flamelets Config.SetInitialGridLength(1.8e-2) # Initial flamelet domain length in metres diff --git a/TestCases/LUT/Flamelet_Table_H2/1_generate_flamelet_data.py b/TestCases/LUT/Flamelet_Table_H2/1_generate_flamelet_data.py index ef21523..b346afa 100644 --- a/TestCases/LUT/Flamelet_Table_H2/1_generate_flamelet_data.py +++ b/TestCases/LUT/Flamelet_Table_H2/1_generate_flamelet_data.py @@ -12,11 +12,11 @@ from Data_Generation.DataGenerator_FGM import ComputeFlameletData # Load FGM configuration -Config = Config_FGM("TableGeneration_H2.cfg") +Config = Config_FGM("TableGeneration_H2_n.cfg") # refinement values: # free_flame_refine={"ratio": 3.0, "slope": 0.1, "curve": 0.1, "prune":0.01}, # this leads to Np = 180 -ComputeFlameletData(Config, run_parallel=True, N_processors=2, loglevel=0) +ComputeFlameletData(Config, run_parallel=False, N_processors=1, loglevel=0) diff --git a/TestCases/LUT/Flamelet_Table_H2/2_optimize_progress_variabele.py b/TestCases/LUT/Flamelet_Table_H2/2_optimize_progress_variabele.py index 41f4e5a..1a99af7 100644 --- a/TestCases/LUT/Flamelet_Table_H2/2_optimize_progress_variabele.py +++ b/TestCases/LUT/Flamelet_Table_H2/2_optimize_progress_variabele.py @@ -1,4 +1,4 @@ -from su2dataminer.config import Config_FGM +from su2dataminer.config import Config_FGM from su2dataminer.process_data import PVOptimizer config = Config_FGM("TableGeneration_H2.cfg") diff --git a/TestCases/LUT/Flamelet_Table_H2/3_collect_flamelet_data.py b/TestCases/LUT/Flamelet_Table_H2/3_collect_flamelet_data.py index 2064d1d..cdf2f59 100644 --- a/TestCases/LUT/Flamelet_Table_H2/3_collect_flamelet_data.py +++ b/TestCases/LUT/Flamelet_Table_H2/3_collect_flamelet_data.py @@ -1,5 +1,5 @@ -from su2dataminer.config import Config_FGM -from su2dataminer.process_data import FlameletConcatenator +from su2dataminer.config import Config_FGM +from su2dataminer.process_data import FlameletConcatenator config = Config_FGM("TableGeneration_H2.cfg") config.SetAverageLewisNumbers(0.5, 300) diff --git a/TestCases/LUT/Flamelet_Table_H2/4_generate_table.py b/TestCases/LUT/Flamelet_Table_H2/4_generate_table.py new file mode 100644 index 0000000..7e1b2b0 --- /dev/null +++ b/TestCases/LUT/Flamelet_Table_H2/4_generate_table.py @@ -0,0 +1,20 @@ +from su2dataminer.config import Config_FGM +from Manifold_Generation.LUT.LUTGenerators import TableGenerator_FGM + +config = Config_FGM("TableGeneration_H2.cfg") + +Tgen = TableGenerator_FGM(config) +Tgen.setTableLimits(7e-3, 4e-2) +Tgen.setNTableLevels(30) +Tgen.setNNearestNeighbors(19) +Tgen.setInverseDistanceExponent(2.67) + +Tgen.applyRefinementForGradientOf("ProdRateTot_PV", coef=0.2) +#Tgen.applyRefinementWithin("Temperature", 600, 2000) +Tgen.setMaximumCellSize(1e-2) +Tgen.setSmoothingParameter(0.1) +Tgen.setVerbosity(2) +Tgen.setNProcessors(4) +Tgen.generateTable() +Tgen.writeParaviewTable("LUT_paraview") +Tgen.writeSU2Table("LUT_hydrogen") \ No newline at end of file From e76f2547e351fc37f06c1131fe0ec581ab81f0af Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Wed, 15 Jul 2026 09:24:50 +0200 Subject: [PATCH 30/44] trailing whitespace --- TestCases/LUT/Flamelet_Table_H2/4_generate_table.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/TestCases/LUT/Flamelet_Table_H2/4_generate_table.py b/TestCases/LUT/Flamelet_Table_H2/4_generate_table.py index 7e1b2b0..7d8e256 100644 --- a/TestCases/LUT/Flamelet_Table_H2/4_generate_table.py +++ b/TestCases/LUT/Flamelet_Table_H2/4_generate_table.py @@ -1,4 +1,4 @@ -from su2dataminer.config import Config_FGM +from su2dataminer.config import Config_FGM from Manifold_Generation.LUT.LUTGenerators import TableGenerator_FGM config = Config_FGM("TableGeneration_H2.cfg") From 951c13d673e88f761d94dc3ea437dd34405d1ddf Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Wed, 15 Jul 2026 09:33:39 +0200 Subject: [PATCH 31/44] Update method names in tutorial --- .../source/Tutorials/NICFD/tablegeneration.rst | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/Documentation/source/Tutorials/NICFD/tablegeneration.rst b/Documentation/source/Tutorials/NICFD/tablegeneration.rst index 037e052..ce6d8d3 100644 --- a/Documentation/source/Tutorials/NICFD/tablegeneration.rst +++ b/Documentation/source/Tutorials/NICFD/tablegeneration.rst @@ -226,7 +226,7 @@ For example, adding the following criterion refines the table in the region wher .. code-block:: - tgen.AddRefinementCriterion("Density", 0, 100.0) + tgen.applyRefinementWithin("Density", 0, 100.0) .. figure:: refined_density.png @@ -239,7 +239,7 @@ Similarly, if additional refinement is required in the two-phase region, the ref .. code-block:: - tgen.AddRefinementCriterion("VaporQuality", 0.0, 1.0) + tgen.applyRefinementWithin("VaporQuality", 0.0, 1.0) .. figure:: refined_vaporquality.png :scale: 50 % @@ -254,7 +254,7 @@ This results in a refined region around a desired isentrope. .. code-block:: isentrope = 2300 - tgen.AddRefinementCriterion("s", isentrope-10.0, isentrope+10.0) + tgen.applyRefinementWithin("s", isentrope-10.0, isentrope+10.0) .. figure:: refined_isentrope.png @@ -269,8 +269,8 @@ Finally, the table generator is not limited to one user-defined refinement crite .. code-block:: isentrope = 2300 - tgen.AddRefinementCriterion("s", isentrope-10.0, isentrope+10.0) - tgen.AddRefinementCriterion("Density", 0.0, 30.0) + tgen.applyRefinementWithin("s", isentrope-10.0, isentrope+10.0) + tgen.applyRefinementWithin("Density", 0.0, 30.0) .. figure:: refined_density_isentrope.png From 3c4ef364687917077c161090afae4465eba5ffd7 Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Wed, 15 Jul 2026 13:13:26 +0200 Subject: [PATCH 32/44] Completed tutorial for two-phase NICFD tabulation --- .../Tutorials/NICFD/tablegeneration.rst | 221 +++++++++--------- 1 file changed, 116 insertions(+), 105 deletions(-) diff --git a/Documentation/source/Tutorials/NICFD/tablegeneration.rst b/Documentation/source/Tutorials/NICFD/tablegeneration.rst index ce6d8d3..4251acc 100644 --- a/Documentation/source/Tutorials/NICFD/tablegeneration.rst +++ b/Documentation/source/Tutorials/NICFD/tablegeneration.rst @@ -24,7 +24,9 @@ To get started, you will need to have installed SU2 DataMiner according to the : As for any process within the SU2 DataMiner workflow, all settings regarding the setup of the fluid data generation and tabulation are stored in an SU2 DataMiner :ref:`configuration object `. The tutorial for setting up a generic SU2 DataMiner configuration can be found :ref:`here `. -In this example, a look-up table will be created for the application of modeling fluid properties of carbondioxide in supercritical conditions. +In this example, a look-up table will be created for the application of modeling fluid properties of **carbondioxide** in **two-phase**, **super-critical** conditions. +The **Helmholtz equation of state** model is used to calculate thermodynamic properties of carbon dioxide throughout the thermodynamic state space with the density ranging between 0.2 and 400 kg/m3 and the static energy ranging between 1000 and 1e6 J/kg. + The following Python code snippet shows the initial set-up of the configuration object. @@ -39,12 +41,13 @@ The following Python code snippet shows the initial set-up of the configuration config.SetFluid("CarbonDioxide") config.SetEquationOfState("HEOS") - # Automatically determine fluid data range. config.IncludeTransportProperties(True) - config.UseAutoRange(True) + config.UseAutoRange(False) config.SetNpDensity(200) config.SetNpEnergy(200) - + config.SetDensityBounds(2.0, 400) + config.SetEnergyBounds(1000, 1e6) + # Enable gas, liquid, two-phase, and supercritical phases. config.EnableGasPhase(True) config.EnableLiquidPhase(True) @@ -59,17 +62,14 @@ The following Python code snippet shows the initial set-up of the configuration Running this code snippet will display all relevant information of the configuration in the terminal and will save the configuration as a binary file titled "tabulation_carbondioxide.cfg". -Configuration settings such as the number of nodes along the density and energy direction can later be changed when setting up the table generator. - - 2. Tabulation Example --------------------- Thermodynamic tables can be created with the :ref:`SU2TableGenerator_NICFD ` class which is initiated with the SU2 DataMiner configuration object. -The following code snippet shows how to initiate the table generator and generate a basic look-up table with refinement around the saturation curve. -Running this code snippet produces two table files. The "LUT_vtk.vtk" file can be used to inspect the table contents using post-processing software such as *ParaView*. -The "LUT_SU2.drg" file is the table file which can be loaded into SU2 for NICFD simulations. +The following code snippet shows how to initiate the table generator and generate a basic look-up table for two-phase applications. +Running this code snippet produces **two table files**. The "LUTtest.vtk" can be loaded into **ParaView** to visually inspect the table contents. +The "LUT_test.drg" file is the table file which can be loaded into **SU2** for NICFD simulations. .. code-block:: @@ -79,17 +79,19 @@ The "LUT_SU2.drg" file is the table file which can be loaded into SU2 for NICFD config = Config_NICFD("tabulation_carbondioxide.cfg") + config.EnableTwophase(True) tgen = SU2TableGenerator_NICFD(config) - tgen.SetTableDiscretization("adaptive") - - tgen.GenerateTable() - tgen.WriteOutParaview("LUT_vtk") - tgen.WriteTableFile("LUT_SU2") - + tgen.setNNearestNeighbors(19) + tgen.setInverseDistanceExponent(2) + tgen.setMaximumCellSize(1e-2) + tgen.setVerbosity(2) + tgen.generateTable() + tgen.writeSU2Table("LUT_test") + tgen.writeParaviewTable("LUTtest") -You can inspect the table content by loading the vtk file in ParaView with the x- and y-dimensions corresponding to the scaled density and static energy. +You can **inspect** the table content by loading the vtk file in ParaView with the x- and y-dimensions corresponding to the **scaled density and static energy**. .. figure:: tables_1.png :scale: 50 % @@ -97,93 +99,82 @@ You can inspect the table content by loading the vtk file in ParaView with the x Temperature, pressure, and speed of sound of the look-up table generated with the previous code snippet. -3. Discretization type ----------------------- +The figure below shows the connectivity of the table nodes. All tables generated with *SU2 DataMiner* use **triangular cells** to connect the table nodes. +The topology of the cells **around the saturation curve** is modified for tables with two-phase fluid data. Consult the :ref:`NICFD table generation documentation page ` for details. -Currently, the table generator supports two discretization types: Cartesian and adaptive. -With Cartesian discretization, the table nodes are placed on a mesh grid in the density-static energy space within the specified bounds. -The table resolution can be adapted by changing the number of nodes in the density and energy direction. See the :ref:`documentation page on table refinement settings ` for details. -A Cartesian table generally contains fewer nodes than an adaptive table and is therefore more memory efficient in NICFD simulations. -The following code snippet generates a Cartesian table with a maximum of 400x400 nodes. +.. figure:: discretization_base.png + :scale: 50 % + :alt: this is a detailed caption of the image -.. code-block:: + Cell topology of the example table. - tgen = SU2TableGenerator_NICFD(config) - tgen.SetTableDiscretization("cartesian") - tgen.SetNpDensity(400) - tgen.SetNpEnergy(400) - tgen.GenerateTable() - tgen.WriteOutParaview("LUT_cartesian") - -With an adaptive approach, the thermodynamic state space is discretized using an unstructured approach. -Using the adaptive approach, the thermodynamic state space is initially discretized with a Cartesian approach. -Then, the thermodynamic state space is discretized with a coarse, unstructured grid. -Finally, the table is locally refined based on user-defined criteria. For two-phase applications, the region around the saturation curve is refined in order to accurately capture phase transitions. -More information on the adaptive table refinement method can be found :ref:`here `. -The following code snippet generates a thermodynamic table using the adaptive method. +3. Changing table limits +------------------------ + +The table limits are determined based on the settings in the loaded configuration. Therefore, in order to generate two tables with different limits, the table generator needs to be re-initialized from the configuration with adjusted table limits. .. code-block:: + config = Config_NICFD("tabulation_carbondioxide.cfg") + config.SetDensityBounds(2.0, 400) + config.SetEnergyBounds(1000, 1e6) + + tgen = SU2TableGenerator_NICFD(config) + + tgen.generateTable() + tgen.writeParaviewTable("LUT_small") + + config.SetEnergyBounds(1000, 2e6) tgen = SU2TableGenerator_NICFD(config) - tgen.SetTableDiscretization("adaptive") - tgen.SetNpDensity(20) - tgen.SetNpEnergy(20) - tgen.SetCellSize_Coarse(2e-2) - tgen.SetCellSize_Refined(1e-2) - tgen.GenerateTable() - tgen.WriteOutParaview("LUT_adaptive") + tgen.generateTable() + tgen.writeParaviewTable("LUT_large") -.. figure:: Cartesian_adaptive_table.png +.. figure:: small_large_table.png :scale: 50 % :alt: - Section of the Cartesian look-up table (left) and of the adaptively adapted look-up table(right) - + Temperature field of look-up table with energy range between 1e3 and 1e6 J/kg (left) and with energy range between 1e3 and 2e6 J/kg (right). -4. Changing table limits ------------------------- -By default, the table limits are determined based on the settings in the loaded configuration. -These settings can be overwritten by the table generator. The following code snippet shows how to manually change the table limits. +4. Table resolution and refinement +---------------------------------- +The thermodynamic state space is discretized by 2D elements with constant size by default. The cell size can be specified manually or determined automatically based on a target number of nodes. +For more information on the specific methods, go to the :ref:`documentation page for table refinement settings `. +The following code snippet shows how to generate two thermodynamic tables with the maximum cell size manually specified. .. code-block:: + #!/usr/bin/env python3 + from su2dataminer.config import Config_NICFD + from su2dataminer.manifold import SU2TableGenerator_NICFD + + config = Config_NICFD("tabulation_carbondioxide.cfg") tgen = SU2TableGenerator_NICFD(config) - tgen.SetNpDensity(200) - tgen.SetNpEnergy(200) - tgen.SetDensityBounds(2.0, 400) - tgen.SetEnergyBounds(1000, 1e6) - tgen.SetTableDiscretization("cartesian") - tgen.GenerateTable() - tgen.WriteOutParaview("LUT_small") + tgen.setMaximumCellSize(2e-2) + tgen.generateTable() + tgen.writeParaviewTable("LUT_coarse") - tgen.SetEnergyBounds(1000, 2e6) - tgen.GenerateTable() - tgen.WriteOutParaview("LUT_large") + tgen.setMaximumCellSize(1e-2) + tgen.generateTable() + tgen.writeParaviewTable("LUT_fine") -.. figure:: small_large_table.png + +.. figure:: Coarse_refined_cell_size.png :scale: 50 % :alt: - Temperature field of look-up table with energy range between 1e3 and 1e6 J/kg (left) and with energy range between 1e3 and 2e6 J/kg (right). - - + Look-up table with lower resolution (left) and higher resolution (right) -5. Adaptive table resolution and refinement -------------------------------------------- +For better control of the runtime memory requirement of the table, it is also possible to generate a table with a specified approximate number of nodes. +Using this approach, the table generator will iterate the maximum cell size until the number of nodes in the table lies within 1% of the target. +The following code snippet shows how to generate tables with a specified target number of nodes. By setting the verbosity level to 2, the convergence history is printed in the terminal. -The adaptive method for table refinement enables the generation of highly refined thermodynamic tables which are more suitable for challenging NICFD applications compared to using Cartesian tables. -This section showcases some of the functionalities of the adaptive table refinement methods. -For more information on the specific methods, go to the :ref:`documentation page for table refinement settings `. - -Using the adaptive method, the thermodynamic state space is discretized in coarse and fine cells. -The size of the coarse and refined elements can be changed, based on the desired table resolution. The following code snippet shows how to generate two thermodynamic tables with different refinement settings. .. code-block:: @@ -195,38 +186,34 @@ The size of the coarse and refined elements can be changed, based on the desired config = Config_NICFD("tabulation_carbondioxide.cfg") tgen = SU2TableGenerator_NICFD(config) - tgen.SetNpDensity(20) - tgen.SetNpEnergy(20) - tgen.SetDensityBounds(2.0, 400) - tgen.SetEnergyBounds(1000, 1e6) - tgen.SetTableDiscretization("adaptive") - tgen.SetCellSize_Coarse(2e-2) - tgen.SetCellSize_Refined(1e-2) - tgen.GenerateTable() - tgen.WriteOutParaview("LUT_coarse") - - tgen.SetCellSize_Coarse(1e-2) - tgen.SetCellSize_Refined(5e-3) - tgen.GenerateTable() - tgen.WriteOutParaview("LUT_fine") - + tgen.setVerbosity(2) + tgen.setTargetNodeCount(2000) + tgen.generateTable() + tgen.writeParaviewTable("LUT_coarse_n") + tgen.setTargetNodeCount(4000) + tgen.generateTable() + tgen.writeParaviewTable("LUT_fine_n") -.. figure:: coarse_refined.png +.. figure:: Coarse_refined_node_count.png :scale: 50 % :alt: Look-up table with lower resolution (left) and higher resolution (right) -For adaptive tables of fluid data which include two-phase data, the thermodynamic region around the saturation curve is automatically refined. -In addition, the user can specify user-defined refinement criteria, where the table is refined **within** bounds of specific thermodynamic state properties. -For example, adding the following criterion refines the table in the region where the fluid density lies between 0.0 and 100 kg/m3. - +The *SU2 DataMiner* table generator supports the use of adaptive local refinement based on thermodynamic quantities. +The user can specify a thermodynamic quantity, the bounds **within** which refinement is applied, and the **refinement factor** applied to the affected area of the table. +The refined cell size is equal to the product of the refinement factor and the maximum cell size. +The following code snippet shows the command used to double the mesh resolution in the area of the table where the density lies between 0 and 100 kg/m3. .. code-block:: - tgen.applyRefinementWithin("Density", 0, 100.0) + tgen.applyRefinementWithin("Density", 0, 100.0, 0.5) + + +Keep in mind that applying local refinement notably increases the time it takes to generate the table, especially when iterating to reach a target number of nodes. +The figure below shows the edges of a thermodynamic table with 4000 nodes with the density refinement criterion applied, alongside the contour of density equal to 100 kg/m3. .. figure:: refined_density.png @@ -235,7 +222,9 @@ For example, adding the following criterion refines the table in the region wher Example of adaptive table refinement based on density criteria. -Similarly, if additional refinement is required in the two-phase region, the refinement criterion can be based on the value of the vapor quality. + +Local adaptive refinement applies to all thermophysical state quantities calculated by the NICFD data generator. For two-phase problems, it is therefore also possible to apply additional refinement in only the two-phase region, demonstrated by the following code snippet. +The refinement factor value does not need to be specified; the default value is set to 0.5. .. code-block:: @@ -247,14 +236,14 @@ Similarly, if additional refinement is required in the two-phase region, the ref Adaptive table refinement in the two-phase region. -To increase the accuracy of the look-up table in isentropic or near-isentropic flows, the refinement criterion can be applied around the expected value of the entropy. -This results in a refined region around a desired isentrope. +For fluid simulations of nearly isentropic flows, an effective method for improving the efficiency of the table is to apply refinement around an isentrope. +The following code snippet demonstrates how to apply refinement around a specific isentrope in the table. .. code-block:: isentrope = 2300 - tgen.applyRefinementWithin("s", isentrope-10.0, isentrope+10.0) + tgen.applyRefinementWithin("s", isentrope-100.0, isentrope+100.0) .. figure:: refined_isentrope.png @@ -263,15 +252,16 @@ This results in a refined region around a desired isentrope. Example of adaptive table refinement around an isentrope. -Finally, the table generator is not limited to one user-defined refinement criterion. Multiple criteria can be applied. +Finally, multiple local adaptive refinement criteria with different settings can be applied simultaneously. +In the following code snippet, refinement is applied around the isentrope and in the low-density region of the table. +The order in which these refinement criteria is specified does not affect the outcome; the local cell size is determined based on the criterion with the lowest refinement factor. .. code-block:: isentrope = 2300 - tgen.applyRefinementWithin("s", isentrope-10.0, isentrope+10.0) - tgen.applyRefinementWithin("Density", 0.0, 30.0) - + tgen.applyRefinementWithin("s", isentrope-100.0, isentrope+100.0, 0.5) + tgen.applyRefinementWithin("Density", 0, 100, 0.3) .. figure:: refined_density_isentrope.png :scale: 50 % @@ -280,7 +270,7 @@ Finally, the table generator is not limited to one user-defined refinement crite Example of adaptive table refinement based on multiple criteria. -6. Table quantities +5. Table quantities ------------------- The size of the look-up table depends on the number of nodes in the table and the number of thermophyscial variables. @@ -292,6 +282,27 @@ Density and static energy are always included in the list. If the generation of .. code-block:: - tgen.SetTableVars(["Density","Energy", "c2", "T", "p"]) + tgen.setTableVars(["Density","Energy", "c2", "T", "p"]) + + +6. Table data smoothing +----------------------- + + +Several thermodynamic quantities are discontinuous accross the saturation curve. Although this is a physical process, it may cause convergence problems when running NICFD simulations, especially when initializing the flow field. +To improve the robustness of NICFD simulations, it helps to smoothen the table data. This will smoothen the transition between two-phase and single-phase fluid properties accross the saturation curve, making it easier for the flow solver to handle phenomena such as vaprisation onset and condensation. +The level of table smoothing can be specified through the **smoothing parameter**, demonstrated by the command in the following code snippet. + +.. code-block:: + + tgen.setSmoothingParameter(0.5) + + +Increasing the value of the smoothing parameter increases the amount of smoothing applied to the table data, but will also decrease the accuracy of the thermodynamic data in the table. For more details on the table smoothing method, consult the :ref:`documentation page `. for table generation. + +.. figure:: table_smoothing.png + :scale: 50 % + :alt: + Tabulated vapor quality without smoothing (left) and with a smoothing factor of 0.5 (right). \ No newline at end of file From 91a9cae8bf7d10876c3eb4e2a1ceb423d05a08c2 Mon Sep 17 00:00:00 2001 From: EvertBunschoten Date: Wed, 15 Jul 2026 13:14:10 +0200 Subject: [PATCH 33/44] Updated tutorial figures --- .../Tutorials/NICFD/refined_density.png | Bin 1042633 -> 858230 bytes .../NICFD/refined_density_isentrope.png | Bin 1053827 -> 784816 bytes .../Tutorials/NICFD/refined_isentrope.png | Bin 1037130 -> 887164 bytes .../Tutorials/NICFD/refined_vaporquality.png | Bin 997656 -> 796557 bytes .../source/Tutorials/NICFD/tables_1.png | Bin 149837 -> 78117 bytes 5 files changed, 0 insertions(+), 0 deletions(-) diff --git a/Documentation/source/Tutorials/NICFD/refined_density.png 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