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Merge pull request #46 from su2code/develop
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‎compressible_flow/NICFD_nozzle/DataDriven/Generate_Dataset.py

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#!/usr/bin/env python
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## \file Generate_Dataset.py
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# \brief Example python script for generating training data for
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# data-driven fluid model in SU2
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# \author E.C.Bunschoten
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# \version 7.5.1 "Blackbird"
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#
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# SU2 Project Website: https://su2code.github.io
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#
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# The SU2 Project is maintained by the SU2 Foundation
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# (http://su2foundation.org)
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#
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# Copyright 2012-2023, SU2 Contributors (cf. AUTHORS.md)
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#
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# SU2 is free software; you can redistribute it and/or
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# modify it under the terms of the GNU Lesser General Public
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# License as published by the Free Software Foundation; either
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# version 2.1 of the License, or (at your option) any later version.
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#
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# SU2 is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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# Lesser General Public License for more details.
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#
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# You should have received a copy of the GNU Lesser General Public
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# License along with SU2. If not, see <http://www.gnu.org/licenses/>.
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# make print(*args) function available in PY2.6+, does'nt work on PY < 2.6
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import CoolProp
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import numpy as np
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from tqdm import tqdm
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import csv
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# Name of the fluid in the CoolProp library.
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fluidName = 'Air'
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# Type of equation of state to be used by CoolProp.
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CP_eos = "HEOS"
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# Minimum and maximum dataset temperatures [K].
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T_min = 280
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T_max = 1000
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# Minimum and maximum dataset pressures [Pa].
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P_min = 5e4
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P_max = 2e6
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# Number of data points along each axis.
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Np_grid = 500
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# Fraction of data points to be used as training data for MLP training (0-1).
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f_train = 0.8
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# Fraction of data poins to be used as test data for MLP validation (0-1).
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f_test = 0.1
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# Prepare data grid
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T_range = np.linspace(T_min, T_max, Np_grid)
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P_range = np.linspace(P_min, P_max, Np_grid)
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T_grid, P_grid = np.meshgrid(T_range, P_range)
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T_dataset = T_grid.flatten()
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P_dataset = P_grid.flatten()
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density_dataset = np.zeros(np.shape(T_dataset))
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energy_dataset = np.zeros(np.shape(T_dataset))
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s_dataset = np.zeros(np.shape(T_dataset))
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dsde_dataset = np.zeros(np.shape(T_dataset))
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dsdrho_dataset = np.zeros(np.shape(T_dataset))
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d2sde2_dataset = np.zeros(np.shape(T_dataset))
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d2sdedrho_dataset = np.zeros(np.shape(T_dataset))
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d2sdrho2_dataset = np.zeros(np.shape(T_dataset))
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# Evaluate CoolProp on data grid.
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fluid = CoolProp.AbstractState(CP_eos, fluidName)
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idx_failed_below = []
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idx_failed_above = []
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print("Generating CoolProp data set...")
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for i in tqdm(range(len(T_dataset))):
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try:
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fluid.update(CoolProp.PT_INPUTS, P_dataset[i], T_dataset[i])
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density_dataset[i] = fluid.rhomass()
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energy_dataset[i] = fluid.umass()
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s_dataset[i] = fluid.smass()
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dsde_dataset[i] = fluid.first_partial_deriv(CoolProp.iSmass, CoolProp.iUmass, CoolProp.iDmass)
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dsdrho_dataset[i] = fluid.first_partial_deriv(CoolProp.iSmass, CoolProp.iDmass, CoolProp.iUmass)
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d2sde2_dataset[i] = fluid.second_partial_deriv(CoolProp.iSmass, CoolProp.iUmass, CoolProp.iDmass, CoolProp.iUmass, CoolProp.iDmass)
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d2sdedrho_dataset[i] = fluid.second_partial_deriv(CoolProp.iSmass, CoolProp.iUmass, CoolProp.iDmass, CoolProp.iDmass, CoolProp.iUmass)
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d2sdrho2_dataset[i] = fluid.second_partial_deriv(CoolProp.iSmass, CoolProp.iDmass, CoolProp.iUmass, CoolProp.iDmass, CoolProp.iUmass)
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except:
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idx_failed_below.append(i)
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print("CoolProp failed at temperature "+str(T_dataset[i]) + ", pressure "+str(P_dataset[i]))
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print("Done!")
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# Collect all data arrays and fill in failed data points.
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collected_data = np.vstack([density_dataset,
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energy_dataset,
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s_dataset,
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dsde_dataset,
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dsdrho_dataset,
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d2sde2_dataset,
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d2sdedrho_dataset,
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d2sdrho2_dataset]).T
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for i_failed in idx_failed_below:
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collected_data[i_failed, :] = 0.5*(collected_data[i_failed+1, :] + collected_data[i_failed-1, :])
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# Shuffle data set and extract training, validation, and test data.
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np.random.shuffle(collected_data)
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np_train = int(f_train*len(density_dataset))
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np_val = int(f_test*len(density_dataset))
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np_test = len(density_dataset) - np_train - np_val
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train_data = collected_data[:np_train, :]
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dev_data = collected_data[np_train:(np_train+np_val), :]
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test_data = collected_data[(np_train+np_val):, :]
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# Write output files.
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with open(fluidName + "_dataset_full.csv", "w+") as fid:
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fid.write("Density,Energy,s,dsde_rho,dsdrho_e,d2sde2,d2sdedrho,d2sdrho2\n")
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csvWriter = csv.writer(fid,delimiter=',')
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csvWriter.writerows(collected_data)
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with open(fluidName + "_dataset_train.csv", "w+") as fid:
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fid.write("Density,Energy,s,dsde_rho,dsdrho_e,d2sde2,d2sdedrho,d2sdrho2\n")
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csvWriter = csv.writer(fid,delimiter=',')
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csvWriter.writerows(train_data)
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with open(fluidName + "_dataset_dev.csv", "w+") as fid:
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fid.write("Density,Energy,s,dsde_rho,dsdrho_e,d2sde2,d2sdedrho,d2sdrho2\n")
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csvWriter = csv.writer(fid,delimiter=',')
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csvWriter.writerows(dev_data)
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with open(fluidName + "_dataset_test.csv", "w+") as fid:
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fid.write("Density,Energy,s,dsde_rho,dsdrho_e,d2sde2,d2sdedrho,d2sdrho2\n")
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csvWriter = csv.writer(fid,delimiter=',')
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csvWriter.writerows(test_data)

‎compressible_flow/NICFD_nozzle/DataDriven/LUTWriter.m

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%!/usr/bin/env matlab
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%% \file LUTWriter.m
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% \brief Example MATLAB script for generating a look-up table file
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% compatible with the CDataDriven_Fluid class in SU2.
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% \author E.C.Bunschoten
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% \version 7.5.1 "Blackbird"
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%
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% SU2 Project Website: https://su2code.github.io
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%
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% The SU2 Project is maintained by the SU2 Foundation
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% (http://su2foundation.org)
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%
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% Copyright 2012-2023, SU2 Contributors (cf. AUTHORS.md)
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%
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% SU2 is free software; you can redistribute it and/or
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% modify it under the terms of the GNU Lesser General Public
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% License as published by the Free Software Foundation; either
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% version 2.1 of the License, or (at your option) any later version.
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%
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% SU2 is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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% Lesser General Public License for more details.
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%
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% You should have received a copy of the GNU Lesser General Public
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% License along with SU2. If not, see <http://www.gnu.org/licenses/>.
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% make print(*args) function available in PY2.6+, does'nt work on PY < 2.6
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% CoolProp input data file.
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input_datafile = "Air_dataset_full.csv";
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% Data point frequency (the larger, the coarser the table).
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data_freq = 2;
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% LUT output file name.
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output_LUTfile = "reftable.drg";
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%% provide import data file
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% space delimited
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data_ref = importdata(input_datafile);
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% Identify data entries
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rho = data_ref.data(1:data_freq:end, 1);
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e = data_ref.data(1:data_freq:end, 2);
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s = data_ref.data(1:data_freq:end, 3);
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ds_de = data_ref.data(1:data_freq:end, 4);
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ds_drho = data_ref.data(1:data_freq:end, 5);
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d2s_de2 = data_ref.data(1:data_freq:end, 6);
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d2s_dedrho = data_ref.data(1:data_freq:end, 7);
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d2s_drho2 = data_ref.data(1:data_freq:end, 8);
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rho_min = min(rho);
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rho_max = max(rho);
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e_min = min(e);
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e_max = max(e);
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% Normalize density and energy
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rho_norm = (rho - rho_min)/(rho_max - rho_min);
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e_norm = (e - e_min)/(e_max - e_min);
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%% Define table connectivity
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T = delaunayTriangulation(rho_norm, e_norm);
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data_LUT = [rho, e, s, ds_de, ds_drho, d2s_de2, d2s_dedrho, d2s_drho2];
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[~, boundNodes] = boundaryFacets(alphaShape(rho_norm, e_norm, 0.05));
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hullIDs = find(ismember([rho_norm, e_norm], boundNodes, "rows"));
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%% Write table data to output
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fid = fopen(output_LUTfile, 'w+');
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header = ['Dragon library' newline newline];
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header = [header '<Header>' newline];
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header = [header '[Version]' newline '1.0.1' newline newline];
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header = [header '[Number of points]' newline];
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header = [header sprintf('%3d',length(rho)) newline newline];
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header = [header '[Number of triangles]' newline];
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header = [header sprintf('%3d',length(T.ConnectivityList)) newline newline];
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header = [header '[Number of hull points]' newline];
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header = [header sprintf('%3d',length(hullIDs)) newline newline];
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header = [header '[Number of variables]' newline];
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header = [header sprintf('%3d',8) newline newline];
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header = [header '[Variable names]' newline];
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header = [header sprintf('1:Density\n2:Energy\n3:s\n4:dsde_rho\n5:dsdrho_e\n6:d2sde2\n7:d2sdedrho\n8:d2sdrho2\n')];
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header = [header newline '</Header>' newline newline];
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header = [header '<Data>'];
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fprintf(fid,'%s', header);
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printformat = '\n';
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for iTabVar=1:8
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printformat = [printformat '%.14e\t'];
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end
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fprintf(fid,printformat,data_LUT');
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fprintf(fid,'%s', newline);
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fprintf(fid,'%s', '</Data>');
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fprintf(fid,'%s', newline);
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fprintf(fid,'%s', newline);
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fprintf(fid,'%s', '<Connectivity>');
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printformat = ['\n' '%5i\t' '%5i\t' '%5i\t'];
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fprintf(fid,printformat,T.ConnectivityList');
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fprintf(fid,'%s', newline);
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fprintf(fid,'%s', '</Connectivity>');
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%% print hull block
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fprintf(fid,'%s', newline);
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fprintf(fid,'%s', newline);
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fprintf(fid,'%s', '<Hull>');
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printformat = ['\n' '%5i\t'];
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fprintf(fid,printformat,hullIDs);
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fprintf(fid,'%s', newline);
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fprintf(fid,'%s', '</Hull>');
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%% close .dat file
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fclose(fid);

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