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