Merge branch 'develop' of https://github.com/EvertBunschoten/SU2_DataMiner

Author: EvertBunschoten

.gitignore

@@ -1,2 +1,2 @@
 Documentation/html/*
-_site/*
+_site/*

Common/.gitignore

@@ -1 +1 @@
-__pycache__/*
+__pycache__/*

Common/Config_base.py

@@ -49,6 +49,7 @@ class Config:
     _alpha_expo:float = DefaultProperties.init_learning_rate_expo       # Initial learning rate exponent (base 10)
     _lr_decay:float = DefaultProperties.learning_rate_decay             # Learning rate decay parameter.
     _batch_expo:int = DefaultProperties.batch_size_exponent             # Mini-batch size exponent (base 2).
+    _n_epochs:int = DefaultProperties.N_epochs                          # Maximum number of training epochs.
     _hidden_layer_architecture:list[int] = DefaultProperties.hidden_layer_architecture  # Hidden layer perceptron count.
     _activation_function:str = DefaultProperties.activation_function    # Hidden layer activation function name.
 
@@ -105,7 +106,7 @@ def GetOutputDir(self):
     def SetConcatenationFileHeader(self, header:str=DefaultProperties.output_file_header):
         """Define the file name header of the processed fluid manifold data.
 
-        :param header: manifold data file header, defaults to DefaultProperties.output_file_header
+        :param header: manifold data file header, defaults to "fluid_data"
         :type header: str, optional
         """
 
@@ -142,7 +143,7 @@ def GetConfigName(self):
         return self._config_name 
 
     def SetControllingVariables(self, names_cv:list[str]):
-        """Define the set of controlling variables used for defining the manifold.
+        """Define the set of controlling variable names used as inputs for the networks of the data-driven fluid model.
 
         :param names_cv: list with controlling variable names.
         :type names_cv: list[str]
@@ -155,7 +156,7 @@ def SetControllingVariables(self, names_cv:list[str]):
         return 
 
     def GetControllingVariables(self):
-        """Get the controlling variables used for defining the manifold.
+        """Retrieve the set of controlling variable names used as inputs for the networks of the data-driven fluid model.
 
         :return: list of controlling variable names.
         :rtype: list[str]
@@ -166,7 +167,7 @@ def GetControllingVariables(self):
     def SetTrainFraction(self, input:float=DefaultProperties.train_fraction):
         """Define the fraction of fluid data used for MLP training.
 
-        :param input: fluid data train fraction, defaults to DefaultProperties.train_fraction
+        :param input: fluid data train fraction, defaults to 0.8
         :type input: float, optional
         :raises Exception: if provided value lies outside 0-1.
         """
@@ -180,7 +181,7 @@ def SetTrainFraction(self, input:float=DefaultProperties.train_fraction):
     def SetTestFraction(self, input:float=DefaultProperties.test_fraction):
         """Define the fraction of fluid data used for MLP prediction accuracy evaluation.
 
-        :param input: fluid data test set fraction, defaults to DefaultProperties.test_fraction
+        :param input: fluid data test set fraction, defaults to 0.1
         :type input: float, optional
         :raises Exception: if provided value lies outside 0-1.
         """
@@ -220,7 +221,7 @@ def GetAlphaExpo(self):
     def SetAlphaExpo(self, alpha_expo_in:float=DefaultProperties.init_learning_rate_expo):
         """Define the initial learning rate exponent (base 10).
 
-        :param alpha_expo_in: log10 of initial learning rate, defaults to DefaultProperties.init_learning_rate_expo
+        :param alpha_expo_in: log10 of initial learning rate, defaults to -1.8269
         :type alpha_expo_in: float, optional
         :raises Exception: if provided value is positive.
         """
@@ -251,8 +252,29 @@ def SetLRDecay(self, lr_decay_in:float=DefaultProperties.learning_rate_decay):
         self._lr_decay = lr_decay_in
         return 
 
+    def SetNEpochs(self, n_epochs_in:int=DefaultProperties.N_epochs):
+        """Specify the maximum number of epochs for training of the networks.
+
+        :param n_epochs_in: maximum number of epochs, defaults to 1000
+        :type n_epochs_in: int, optional
+        :raises Exception: if the number is lower than 1.
+        """
+        if n_epochs_in < 1:
+            raise Exception("Number of epochs should be higher than 1")
+        
+        self._n_epochs = int(n_epochs_in)
+        return 
+    
+    def GetNEpochs(self):
+        """Retrieve the maximum number of epochs the networks are trained for.
+
+        :return: maximum number of training epochs.
+        :rtype: int
+        """
+        return self._n_epochs
+    
     def SetBatchExpo(self, batch_expo_in:int=DefaultProperties.batch_size_exponent):
-        """Set the mini-batch size exponent for MLP training.
+        """Set the mini-batch size exponent (base 2) for MLP training.
 
         :param batch_expo_in: Mini-batch size exponent (base 2) used for MLP training, defaults to DefaultProperties.batch_size_exponent
         :type batch_expo_in: int, optional
@@ -272,13 +294,27 @@ def GetBatchExpo(self):
         """
         return self._batch_expo 
 
+    def __HiddenLayerChecks(self,hidden_layer_architecture:list[int]):
+        if not hidden_layer_architecture:
+            raise Exception("At least one hidden layer should be specified.")
+        for h in hidden_layer_architecture:
+            if h < 1:
+                raise Exception("Number of nodes in the hidden layers should be positive.")
+            if type(h) is not int:
+                raise Exception("Nodes in the hidden layers should be integers.")
+        return 
+    
     def SetHiddenLayerArchitecture(self, hidden_layer_architecture:list[int]=DefaultProperties.hidden_layer_architecture):
         """
         Define the hidden layer architecture of the multi-layer perceptron used for the MLP-based manifold.
 
-        :param hidden_layer_architecture: listed neuron count per hidden layer, defaults to DefaultProperties.hidden_layer_architecture
+        :param hidden_layer_architecture: listed neuron count per hidden layer, defaults to [20,20,20]
         :type hidden_layer_architecture: list[int], optional
+        :raises Exception: if an empty list is provided or if input contains non-integer data or the number of nodes is less than 1.
         """
+
+        self.__HiddenLayerChecks(hidden_layer_architecture)
+
         self._hidden_layer_architecture = []
         for n in hidden_layer_architecture:
             self._hidden_layer_architecture.append(n)
@@ -292,33 +328,57 @@ def GetHiddenLayerArchitecture(self):
         """
         return self._hidden_layer_architecture
 
+    def __WeightsCheck(self, weights:list[np.ndarray[float]]):
+        if not weights:
+            raise Exception("Weights list should contain at least one array.")
+        for i in range(len(weights)-1):
+            w_i = weights[i]
+            w_ip = weights[i+1]
+            if np.shape(w_i)[1] != np.shape(w_ip)[0]:
+                raise Exception("Weight arrays are improperly formatted. Check rows and columns.")
+        return 
+    
+        
     def SetWeights(self, weights:list[np.ndarray[float]]):
         """Store the weight values of the neural network.
 
         :param weights: weight arrays for the network hidden layers.
         :type weights: list[np.ndarray[float]]
+        :raises: Exception: if an empty list is provided or the weights arrays are improperly formatted.
         """
+        self.__WeightsCheck(weights)
 
         self._MLP_weights = []
         for w in weights:
             self._MLP_weights.append(w)
         return 
 
+    def __BiasesCheck(self, biases:list[np.ndarray[float]]):
+        if not biases:
+            raise Exception("Biases list should contain at least one entry.")
+        for b in biases:
+            if b.size == 0:
+                raise Exception("Biases for hidden layers should contain at least one value.")
+        return 
+    
     def SetBiases(self, biases:list[np.ndarray[float]]):
         """Store the bias values of the neural network.
 
         :param weights: bias arrays for the network hidden layers.
         :type weights: list[np.ndarray[float]]
+        :raises: Exception: if an empty list is provided or contains empty arrays.
         """
+        self.__BiasesCheck(biases)
+
         self._MLP_biases = []
         for w in biases:
             self._MLP_biases.append(w)
         return 
 
     def SetActivationFunction(self, activation_function_in:str=DefaultProperties.activation_function):
-        """Define the hidden layer activation function for the MLP-based manifold.
+        """Define the hidden layer activation function for the MLP-based manifold. See Common.Properties.ActivationFunctionOptions for the supported options.
 
-        :param activation_function_in: hidden layer activation function name, defaults to DefaultProperties.activation_function
+        :param activation_function_in: hidden layer activation function name, defaults to "gelu"
         :type activation_function_in: str, optional
         :raises Exception: if the provided name does not appear in the list of available activation function options.
         """
@@ -346,6 +406,7 @@ def UpdateMLPHyperParams(self, trainer):
         self._alpha_expo = trainer.alpha_expo
         self._lr_decay = trainer.lr_decay
         self._batch_expo = trainer.batch_expo
+        self._n_epochs = trainer.n_epochs
         self._hidden_layer_architecture = trainer.architecture.copy()
         self._activation_function = trainer.activation_function
 
@@ -361,7 +422,7 @@ def GetWeightsBiases(self):
         """Return values for weights and biases for the hidden layers in the MLP.
 
         :return: weight arrays, biases arrays
-        :rtype: list[np.ndarray[float]]
+        :rtype: tuple(np.ndarray[float])
         """
         return self._MLP_weights, self._MLP_biases
 

Common/DataDrivenConfig.py

@@ -32,6 +32,8 @@
 import pickle 
 import CoolProp
 import cantera as ct 
+from CoolProp.CoolProp import get_global_param_string
+supported_fluid_names = get_global_param_string("FluidsList").split(',')
 
 #---------------------------------------------------------------------------------------------#
 # Importing DataMiner classes and functions
@@ -85,7 +87,7 @@ class Config_NICFD(Config):
     _config_type = DefaultSettings_NICFD.config_type
 
     def __init__(self, load_file:str=None):
-        """EntropicAI SU2 DataMiner configuration class.
+        """SU2 DataMiner configuration class for real-gas applications.
 
         :param load_file: configuration file name to load, defaults to None
         :type load_file: str, optional
@@ -152,9 +154,9 @@ def PrintBanner(self):
         return 
 
 
-    def SetFluid(self, fluid_name):
+    def SetFluid(self, fluid_name=DefaultSettings_NICFD.fluid_name):
         """
-        Define the fluid name used for entropic data generation. By default, \"MM\" is used.
+        Define the fluid name used for entropic data generation. By default, "Air" is used. Specify a list of fluids for mixtures.
 
         :param fluid_name: CoolProp fluid name or list of names.
         :type fluid_name: str or list[str]
@@ -170,11 +172,17 @@ def SetFluid(self, fluid_name):
             self.__fluid_names = []
             fluid_mixing = []
             for f in fluid_name:
+                if type(f) is not str:
+                    raise Exception("Fluid name should be provided in string format.")
+                if f not in supported_fluid_names:
+                    raise Exception("Fluid name should be one of the following: %s" % (", ".join(q for q in supported_fluid_names)))
                 self.__fluid_names.append(f)
             if len(self.__fluid_mole_fractions) == 0:
                 self.__fluid_mole_fractions = np.ones(len(self.__fluid_names))/len(self.__fluid_names)
 
         elif type(fluid_name) == str:
+            if fluid_name not in supported_fluid_names:
+                raise Exception("Fluid name should be one of the following: %s" % (", ".join(q for q in supported_fluid_names)))
             self.__fluid_names = [fluid_name]
 
         fluid_string = "&".join(f for f in self.__fluid_names)
@@ -186,10 +194,26 @@ def SetFluid(self, fluid_name):
         return 
 
     def SetEquationOfState(self, EOS_type_in:str=DefaultSettings_NICFD.EOS_type):
-        self.__EOS_type=EOS_type_in 
+        """Define the equation of state backend used by CoolProp to generate fluid data.
+
+        :param EOS_type_in: backend used by CoolProp, defaults to "HEOS"
+        :type EOS_type_in: str, optional
+        :raises Exception: if the specified backend is not supported.
+        """
+        if type(EOS_type_in) is not str:
+            raise Exception("Equation of state should be provided in string format.")
+        if EOS_type_in.upper() not in DefaultSettings_NICFD.supported_backends:
+            raise Exception("Equation of state not supported, should be one of the following : %s" % ", ".join(e for e in DefaultSettings_NICFD.supported_backends))
+        
+        self.__EOS_type=EOS_type_in.upper()
         return
 
     def GetEquationOfState(self):
+        """Retrieve the equation of state backend used by CoolProp for fluid data calculations.
+
+        :return: name of the equation of state model.
+        :rtype: str
+        """
         return self.__EOS_type 
 
     def SetFluidMoleFractions(self, mole_fractions:list[float]):
@@ -215,8 +239,7 @@ def SetFluidMoleFractions(self, mole_fractions:list[float]):
         return 
 
     def GetFluid(self):
-        """
-        Get the fluid used for entropic data generation.
+        """Get the name of the fluid for which thermodynamic data is generated.
         :return: fluid name
         :rtype: str
 
@@ -230,12 +253,12 @@ def GetMoleFractions(self):
         return self.__fluid_mole_fractions.copy()
 
     def UseAutoRange(self, use_auto_range:bool=True):
-        """Automatically determine fluid data range based on available fluid properties.
+        """Automatically determine the span of the thermodynamic state space for which fluid data are generated.
 
         :param use_auto_range: automatically set fluid data range, defaults to True
         :type use_auto_range: bool, optional
         """
-        self.__use_auto_range = use_auto_range
+        self.__use_auto_range = bool(use_auto_range)
         return 
 
     def GetAutoRange(self):
@@ -249,10 +272,10 @@ def GetAutoRange(self):
     def UsePTGrid(self, PT_grid:bool=DefaultSettings_NICFD.use_PT_grid):
         """Define fluid data grid in the pressure-temperature space. If not, the fluid data grid is defined in the density-energy space.
 
-        :param PT_grid: use pressure-temperature based grid, defaults to DefaultSettings_NICFD.use_PT_grid
+        :param PT_grid: use pressure-temperature based grid, defaults to False
         :type PT_grid: bool, optional
         """
-        self.__use_PT = PT_grid 
+        self.__use_PT = bool(PT_grid) 
         return 
 
     def GetPTGrid(self):
@@ -265,14 +288,16 @@ def GetPTGrid(self):
         return self.__use_PT 
 
     def SetTemperatureBounds(self, T_lower:float=DefaultSettings_NICFD.T_min, T_upper:float=DefaultSettings_NICFD.T_max):
-        """Set the upper and lower temperature limits for the fluid data grid.
+        """Set the upper and lower temperature limits between which fluid data are generated.
 
         :param T_lower: lower temperature limit in Kelvin.
         :type T_lower: float
         :param T_upper: upper temperature limit in Kelvin.
         :type T_upper: float
         :raises Exception: if lower temperature limit exceeds upper temperature limit.
         """
+        if (T_lower <=0 or T_upper <=0):
+            raise Exception("Temperature values should be positive.")
         if (T_lower >= T_upper):
             raise Exception("Lower temperature should be lower than upper temperature.")
         else:
@@ -1843,7 +1868,7 @@ def AddOutputGroup(self, variable_names_in:list[str]):
         self.__activation_function.append(DefaultSettings_FGM.activation_function)
         return 
 
-    def DefineOutputGroup(self, i_group:int, variable_names_in:list[str]):
+    def EditOutputGroup(self, i_group:int, variable_names_in:list[str]):
         """Re-define the variables in a specific MLP output group.
 
         :param i_group: MLP output group index to adapt.
@@ -1864,7 +1889,13 @@ def DefineOutputGroup(self, i_group:int, variable_names_in:list[str]):
         return 
 
     def RemoveOutputGroup(self, i_group:int):
-        if i_group > len(self.__MLP_output_groups):
+        """Remove one of the MLP output groups.
+
+        :param i_group: MLP output group index to remove.
+        :type i_group: int
+        :raises Exception: if group index is outside the range of stored groups.
+        """
+        if (i_group > len(self.__MLP_output_groups)) or (i_group<0):
             raise Exception("Group not present in MLP outputs.")
         print("Removing output group %i: %s" % (i_group, ",".join(s for s in self.__MLP_output_groups[i_group-1])))
         self.__MLP_output_groups.remove(self.__MLP_output_groups[i_group-1])
@@ -1954,12 +1985,14 @@ def GetWeightsBiases(self, i_group:int=0):
     def SetWeights(self, weights: list[np.ndarray[float]], i_group:int=0):
         self._MLP_weights[i_group] = []
         for w in weights:
+            self.__WeightsCheck(w)
             self._MLP_weights[i_group].append(w)
         return 
 
     def SetBiases(self, biases:list[np.ndarray[float]], i_group:int=0):
         self._MLP_biases[i_group] = []
         for w in biases:
+            self.__BiasesCheck(w)
             self._MLP_biases[i_group].append(w)
         return 
 

Common/Properties.py

@@ -151,7 +151,8 @@ class DefaultSettings_NICFD(DefaultProperties):
     activation_function:str = "exponential"
     config_type:str = "EntropicAI"
     supported_state_vars:list[str] = ["s","T","p","c2","dTdrho_e","dTde_rho","dpdrho_e","dpde_rho"]
-
+    supported_backends:list[str] = ["HEOS","PR", "SRK", "IF97","REFPROP"]
+    
 class DefaultSettings_FGM(DefaultProperties):
     config_name:str = "config_FGM"
 

Data_Generation/.gitignore

@@ -1 +1 @@
-__pycache__/*
+__pycache__/*

Data_Processing/.gitignore

@@ -1 +1 @@
-__pycache__/*
+__pycache__/*