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/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):
diff --git a/Common/Interpolators.py b/Common/Interpolators.py
index 01de02a..5a7e5e9 100644
--- a/Common/Interpolators.py
+++ b/Common/Interpolators.py
@@ -25,7 +25,9 @@
from scipy.spatial import cKDTree as KDTree
import numpy as np
-
+import pandas as pd
+from tqdm import tqdm
+from sklearn.preprocessing import MinMaxScaler
class Invdisttree:
""" inverse-distance-weighted interpolation using KDTree:
invdisttree = Invdisttree( X, z ) -- data points, values
@@ -77,41 +79,241 @@ 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 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
+
+ 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)
+ 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
+
+ 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
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)
diff --git a/Data_Processing/OptimizeProgressVariable.py b/Data_Processing/OptimizeProgressVariable.py
index 10e37b1..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)
@@ -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(',')
diff --git a/Manifold_Generation/LUT/FlameletTableGeneration.py b/Manifold_Generation/LUT/FlameletTableGeneration.py
index 0a7f27c..726a7e3 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.
@@ -537,7 +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:
@@ -548,7 +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])
@@ -559,7 +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
@@ -578,7 +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
@@ -587,7 +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):
diff --git a/Manifold_Generation/LUT/LUTGenerator_Base.py b/Manifold_Generation/LUT/LUTGenerator_Base.py
new file mode 100644
index 0000000..7f0076f
--- /dev/null
+++ b/Manifold_Generation/LUT/LUTGenerator_Base.py
@@ -0,0 +1,642 @@
+###############################################################################################
+# # _____ __ _____ ____ __ __ ____ # #
+# # / ___// / / /__ \ / __ \____ _/ /_____ _/ |/ (_)___ ___ _____ # #
+# # \__ \/ / / /__/ / / / / / __ `/ __/ __ `/ /|_/ / / __ \/ _ \/ ___/ # #
+# # ___/ / /_/ // __/ / /_/ / /_/ / /_/ /_/ / / / / / / / / __/ / # #
+# # /____/\____//____/ /_____/\__,_/\__/\__,_/_/ /_/_/_/ /_/\___/_/ # #
+# # # #
+###############################################################################################
+
+############################# 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 copy import copy
+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 = copy(config_in)
+ self._nDim_table = len(self._Config.GetControllingVariables())
+ return
+
+ 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)
+ self._fluid_data_interpolator = fluidDataInterpolator(cv_data_scaled, stateDataFrame, self.__N_nearest_neighbors, self.__inverse_distance_exponent)
+ return
+
+ 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
+
+ :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("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
+
+ 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.
+ :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 meshTableLevel(self, level_index:int):
+ """Discretize and interpolate the fluid data of a single table level.
+
+ :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]]
+ """
+
+ pointCloud = self._createPointCloudForTableLevel(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 _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()
+ 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)
+
+ 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 _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])
+ 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]):
+ """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:
+ 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):
+ """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:
+ 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 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:
+ 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]):
+ """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")
+ self._tableLowerLevel = lower_limit
+ self._tableUpperLevel = upper_limit
+ 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")
+ self._N_table_levels = N_levels
+ 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")
+ self.__run_parallel = True
+ self.__N_cores = N_cores
+ 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
+ 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 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")
+ return False
+ else:
+ return True
+
+ def _is2D(self):
+ return self._nDim_table==2
+
+ def _is3D(self):
+ return self._nDim_table==3
+
+
+ def __printmsg(self, msg:str):
+ if self._verbosity > 0:
+ print(msg)
+ return
+
+
+
\ No newline at end of file
diff --git a/Manifold_Generation/LUT/LUTGenerators.py b/Manifold_Generation/LUT/LUTGenerators.py
index 33c9095..523281b 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 , |
@@ -17,257 +17,57 @@
# |
# |
# 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.DataDrivenConfig import Config_NICFD
-import gmsh
-from concave_hull import concave_hull, concave_hull_indexes
-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.
-
- _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] = []
- _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.
- _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
- _fluid_data_scaler:MinMaxScaler= MinMaxScaler() # Scaler for flamelet data controlling variables.
-
- 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._controlling_variables= [c for c in self._Config.GetControllingVariables()]
-
- self._DataGenerator = DataGenerator_CoolProp(self._Config)
- 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 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 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 SetDensityBounds(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 SetEnergyBounds(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 SetCellSize_Coarse(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 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
+ 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 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
- """
- if refinement_radius <= 0:
- raise Exception("Refinement radius should be positive")
- self._refinement_radius = refinement_radius
- return
-
- def SetTableDiscretization(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:
+
+ def setTableVars(self, table_vars_in:list[str]):
+ 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:
@@ -281,599 +81,229 @@ 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 __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
- """
- # 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), :]
-
- 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, :]
-
- # Initiate gmsh
- gmsh.initialize()
- gmsh.model.add("table_level")
- gmsh.option.setNumber("General.Verbosity", 0)
- 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))
-
- # 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
- 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))
-
- 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)
+ return super().setTableVars(table_vars_in)
+
+ def _checkIfVariableIsValid(self, var_to_check:str):
+ if var_to_check in self._state_quantities:
+ return True
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)
-
- 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)
-
- 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
- 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
- if fluid_surf is None:
- elemTypes, _, elemNodeTags = gmsh.model.mesh.getElements(2)
- else:
- elemTypes, _, elemNodeTags = gmsh.model.mesh.getElements(2, fluid_surf)
-
- 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]],
- ])
- gmsh.finalize()
-
- 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)
- 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 __CalcMeshData(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..."):
+ return False
+
+ 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 _getFluidDataForInterpolator(self):
+ self.__datagenerator.PreprocessData()
+ self.__datagenerator.ComputeData()
+ state_data_pointcloud, valid_mask = self.__datagenerator.GetStateData()
+
+ 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]
+
+ self.__thermodynamic_data = state_dataFrame
+
+ return state_dataFrame
+
+ 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))
+ rhoe_pointcloud_scaled = self._scaler_controlling_variables.transform(rhoe_pointcloud)
+ cv_pointcloud = np.column_stack((rhoe_pointcloud_scaled, const_z))
+ return cv_pointcloud
+
+ 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):
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()
+ self.__datagenerator.UpdateFluid(rhoe[0], rhoe[1])
+ 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
-
- 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)
-
- 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()
+ pass
- shape = self.rho_grid.shape
- n_points = shape[0] * shape[1]
+ return state_data_out
- # 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
+ 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:
+ fid.write("Table constains single-phase data\n\n")
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()
+
+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._getFluidDataForInterpolator()
return
-
- def __CreateSaturationCurve(self):
- rhoe_sat_curve = self._DataGenerator.ComputeSaturationCurve()
- rho_min, rho_max = self._DataGenerator.GetDensityBounds()
- e_min, e_max = self._DataGenerator.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 __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 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.
"""
-
- 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
-
+ 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 refineEquilibrium(self, coef:float=0.5, margin:float=2e-2):
+ """Apply refinement to equilibrium areas of each table level.
- 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 WriteOutParaview(self,file_name_out:str="vtk_table"):
+ :param coef: refinement coefficient, defaults to 0.5
+ :type coef: float, optional
+ :raises Exception: if refinement coefficient value is negative.
"""
- 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._table_nodes[:, EntropicVars.Density.value], self._table_nodes[:, EntropicVars.Energy.value]
- table_data_norm = self._fluid_data_scaler.transform(self._table_nodes)
- 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._table_nodes[:, ivar])
-
- mesh = meshio.Mesh(
- points=pts,
- cells=[("triangle", conn)],
- point_data=point_data
- )
- mesh.write("%s.vtk" % file_name_out)
-
+ 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 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)
+
+ 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 _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 __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)
+
+ 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
+
+ # 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]
+ 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:
- 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"
- 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._table_nodes)[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._table_nodes)):
- for var in self._table_vars:
- ivar = EntropicVars[var].value
- fid.write("\t%+.14e" % self._table_nodes[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()
-
+ 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")
+ 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
diff --git a/Manifold_Generation/LUT/MeshTools.py b/Manifold_Generation/LUT/MeshTools.py
new file mode 100644
index 0000000..b3308e2
--- /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
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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diff --git a/RegressionTests/FluidTraining/MM_PINN/.gitignore b/RegressionTests/FluidTraining/MM_PINN/.gitignore
index 00ce95c..fc67d24 100644
--- a/RegressionTests/FluidTraining/MM_PINN/.gitignore
+++ b/RegressionTests/FluidTraining/MM_PINN/.gitignore
@@ -1,3 +1,2 @@
Worker_0/*
-fluid_data*
*.cfg
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]
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[biases per layer]
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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]
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--4.0535996229226606e+04 +7.7763482467061304e+05
++8.5714335909869988e-05 +6.2299851501995454e+02
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[output names]
s
@@ -31,29 +31,29 @@ s
minmax
[output normalization]
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++3.2143198366816824e+02 +2.2485138596991110e+03
[weights per layer]
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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 0000000..058cc78
--- /dev/null
+++ b/RegressionTests/FluidTraining/MM_PINN/fluid_data_full.csv
@@ -0,0 +1,1398 @@
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diff --git a/RegressionTests/FluidTraining/MM_PINN/fluid_data_test.csv b/RegressionTests/FluidTraining/MM_PINN/fluid_data_test.csv
new file mode 100644
index 0000000..0eeb661
--- /dev/null
+++ b/RegressionTests/FluidTraining/MM_PINN/fluid_data_test.csv
@@ -0,0 +1,140 @@
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diff --git a/RegressionTests/FluidTraining/MM_PINN/fluid_data_train.csv b/RegressionTests/FluidTraining/MM_PINN/fluid_data_train.csv
new file mode 100644
index 0000000..3344b4b
--- /dev/null
+++ b/RegressionTests/FluidTraining/MM_PINN/fluid_data_train.csv
@@ -0,0 +1,1118 @@
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diff --git a/RegressionTests/FluidTraining/MM_PINN/fluid_data_val.csv b/RegressionTests/FluidTraining/MM_PINN/fluid_data_val.csv
new file mode 100644
index 0000000..182b6eb
--- /dev/null
+++ b/RegressionTests/FluidTraining/MM_PINN/fluid_data_val.csv
@@ -0,0 +1,142 @@
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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)
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
- +5.73333333333333e+01 +2.77777777777633e+05 +2.39623864276139e+02 +1.26593633714091e+06 +2.18760044017927e+04 +1.44558823827878e+03 -1.60719071238416e+00 +4.17320705106239e-03 +3.45754587071883e-02 -7.63566241122398e-06 -2.71310327149769e-09 +4.38436653369441e-01 +1.55785294652708e-04 +1.92427762918260e+04 +6.83740021074886e+00 +1.11925698042004e+00 -4.94914300867366e+01 +1.63696280153455e-01 -3.19946232888374e+03 +6.10349975492414e-04 -1.33520187505061e+01 +1.12106631006004e+03 +7.09536557809368e+02 +2.99857616711175e+05 +1.66539628922819e-02 +2.04913429063991e-05 +5.59943975179771e-01
- +4.10314144566862e+01 +3.66068110194070e+05 +2.42480046100161e+02 +1.39593958392331e+06 +3.70316330155777e+04 +1.85004946055535e+03 -3.41946088043760e+00 +4.12405068410012e-03 +9.67553448027964e-02 -1.17231935889434e-05 -1.96556143839823e-09 +6.89283604793618e-01 +1.15568276120617e-04 +3.25118295094044e+04 +5.45112209011335e+00 +1.13285240497540e+00 -3.67866949929862e+01 +2.07820038929241e-01 -6.79339636929827e+03 +7.56550782742488e-04 -2.80163109441678e+01 +1.13665943617772e+03 +7.15878818010295e+02 +4.00088723900579e+05 +1.44928625651338e-02 +1.38056512735688e-05 +8.79689075753059e-01
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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..0e20ad6
--- /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..3f2da15
--- /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..e35b188
--- /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
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()
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/**
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..5d2a439
--- /dev/null
+++ b/TestCases/LUT/Flamelet_Table_H2/0_generate_config.py
@@ -0,0 +1,41 @@
+from Common.DataDrivenConfig import Config_FGM
+import os
+
+Config = Config_FGM()
+Config.SetConfigName("TableGeneration_H2")
+
+# 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.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")
+
+
+# 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..ef21523
--- /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.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)
+
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()
+
diff --git a/TestCases/NICFD/LUT_Twophase/generate_table.py b/TestCases/NICFD/LUT_Twophase/generate_table.py
index a53b377..e2b5dda 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, 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")
+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.SetCellSize_Coarse(2e-2)
-tablegen.SetCellSize_Refined(2e-3)
+# 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)
-# 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, 10.0)
-tablegen.AddRefinementCriterion("s", 729.13-2, 729.13+40)
+# 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