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Merge pull request #62 from su2code/develop
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‎compressible_flow/NICFD_nozzle/PhysicsInformed/0:generate_config.py

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###############################################################################################
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# # _____ __ _____ ____ __ __ ____ # #
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# # / ___// / / /__ \ / __ \____ _/ /_____ _/ |/ (_)___ ___ _____ # #
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# # # #
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###############################################################################################
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############################# FILE NAME: 0:generate_config.py #################################
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#=============================================================================================#
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# author: Evert Bunschoten |
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# :PhD Candidate , |
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# :Flight Power and Propulsion |
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# :TU Delft, |
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# :The Netherlands |
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# |
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# |
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# Description: |
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# Generate configuration for defining a physics-informed neural network for modeling the |
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# fluid properties of siloxane MM in NICFD with the data-driven fluid model in SU2. |
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# |
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# Version: 2.0.0 |
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# |
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#=============================================================================================#
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from su2dataminer.config import Config_NICFD
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# The fluid data for siloxane MM are generated with the CoolProp module using the Helmoltz
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# equation of state.
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fluid_name = "MM"
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EoS_type = "HEOS"
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Config = Config_NICFD()
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Config.SetFluid(fluid_name)
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Config.SetEquationOfState(EoS_type)
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# Fluid data are generated on a density-energy grid rather than pressure-temperature.
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Config.UsePTGrid(False)
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# Display configuration settings and save config object.
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Config.SetConfigName("SU2DataMiner_MM")
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Config.PrintBanner()
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Config.SaveConfig()

‎compressible_flow/NICFD_nozzle/PhysicsInformed/1:generate_fluid_data.py

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###############################################################################################
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# # _____ __ _____ ____ __ __ ____ # #
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# # / ___// / / /__ \ / __ \____ _/ /_____ _/ |/ (_)___ ___ _____ # #
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# # \__ \/ / / /__/ / / / / / __ `/ __/ __ `/ /|_/ / / __ \/ _ \/ ___/ # #
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# # ___/ / /_/ // __/ / /_/ / /_/ / /_/ /_/ / / / / / / / / __/ / # #
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# # /____/\____//____/ /_____/\__,_/\__/\__,_/_/ /_/_/_/ /_/\___/_/ # #
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# # # #
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###############################################################################################
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########################### FILE NAME: 1:generate_fluid_data.py ###############################
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#=============================================================================================#
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# author: Evert Bunschoten |
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# :PhD Candidate , |
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# :Flight Power and Propulsion |
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# :TU Delft, |
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# :The Netherlands |
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# |
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# |
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# Description: |
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# Generate the single-phase fluid data for siloxane MM which is used to train the |
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# physics-informed neural network. |
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# |
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# Version: 2.0.0 |
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# |
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#=============================================================================================#
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from su2dataminer.config import Config_NICFD
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from su2dataminer.generate_data import DataGenerator_CoolProp
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# Load SU2 DataMiner configuration.
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Config = Config_NICFD("SU2DataMiner_MM.cfg")
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# Initiate data generator.
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DG = DataGenerator_CoolProp(Config)
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DG.PreprocessData()
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DG.ComputeData()
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# Visualize and save fluid data.
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DG.VisualizeFluidData()
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DG.SaveData()

‎compressible_flow/NICFD_nozzle/PhysicsInformed/2:train_PINN.py

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# # / ___// / / /__ \ / __ \____ _/ /_____ _/ |/ (_)___ ___ _____ # #
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# # \__ \/ / / /__/ / / / / / __ `/ __/ __ `/ /|_/ / / __ \/ _ \/ ___/ # #
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# # ___/ / /_/ // __/ / /_/ / /_/ / /_/ /_/ / / / / / / / / __/ / # #
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# # # #
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###############################################################################################
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################################ FILE NAME: 2:train_PINN.py ###################################
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#=============================================================================================#
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# author: Evert Bunschoten |
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# :PhD Candidate , |
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# :Flight Power and Propulsion |
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# :TU Delft, |
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# :The Netherlands |
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# |
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# |
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# Description: |
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# Initate physics-informed machine learning process for training the neural network used to |
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# model the fluid properties of siloxane MM in NICFD with the SU2 data-driven fluid model. |
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# |
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# Version: 2.0.0 |
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# |
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#=============================================================================================#
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from su2dataminer.config import Config_NICFD
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from su2dataminer.manifold import TrainMLP_NICFD
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# Load SU2 DataMiner configuration.
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Config = Config_NICFD("SU2DataMiner_MM.cfg")
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# Set learning rate parameters and define network architecture.
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Eval = TrainMLP_NICFD(Config)
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# Initial learning rate: 10^-3, learning rate decay parameter: 9.8787, mini-batch size: 2^6.
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Eval.SetAlphaExpo(-3.0)
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Eval.SetLRDecay(+9.8787e-01)
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Eval.SetBatchExpo(6)
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# Network architecture: two hidden layers with 12 nodes.
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Eval.SetHiddenLayers([12,12])
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# Hidden layer activation function: exp(x)
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Eval.SetActivationFunction("exponential")
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# Display training progress in the terminal.
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Eval.SetVerbose(1)
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# Initiate training process.
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Eval.CommenceTraining()
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Eval.TrainPostprocessing()
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Config.UpdateMLPHyperParams(Eval)
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Config.SaveConfig()

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