SU2 DataMiner

This repository describes the workflow for manifold generation for data-driven fluid modeling in SU2. The workflow allows the user to generate fluid data and convert these into tables and train multi-layer perceptrons in order to retrieve thermo-chemical quantities during simulations in SU2. The applications are currently limited to non-ideal computational fluid dynamics and flamelet-generated manifold simulations for arbitrary fluids and reactants respectively.

Capabilities

The SU2 DataMiner workflow allows the user to generate fluid data and convert these into look-up tables (LUT) or multi-layer perceptrons (MLP) for usage in SU2 simulations. The types of simulations for which this workflow is suitable are flamelet-generated manifold (FGM) and non-ideal computational fluid dynamics (NICFD) simulations. This tool allows the user to start from scratch and end up with a table input file or a set of MLP input files which can immediately be used within SU2.

Requirements and Set-Up

The SU2 DataMiner tool is python-based and was generated with python 3.13. Currently only Linux distributions are supported. To install the required python modules, navigate to the SU2 DataMiner source code directory and run the following command:

python -m pip install -r required_packages.txt

Alternatively, a suitable conda environment can be created using the environment recipe through the following command:

conda env create -f environment.yml

After cloning this repository, add the following lines to your ~/.bashrc in order to update your pythonpath accordingly:

export PINNTRAINING_HOME=<PATH_TO_SOURCE>
export PYTHONPATH=$PYTHONPATH:$PINNTRAINING_HOME
export PATH=$PATH:$PINNTRAINING_HOME/bin

where <PATH_TO_SOURCE> is the path to where you cloned the repository.

Tutorials can be found under TestCases, proper documentation will follow soon.

Getting Started

Generating a fluid data manifold for FGM or NICFD applications consists of the following steps:

1. Generate a configuration: The manifold settings such as the type of fluid, storage directory and range are stored in a configuration class. Configurations can be defined through python (example scripts are found in the TestCases folder, named generate_config.py) or interactively through terminal inputs. In order to generate a configuration interactively, run the command GenerateConfig.py in the terminal.

2. Generate fluid data: Raw fluid data can be generated once the configuration is defined. Similarly to the configuration set-up, fluid data can be generated through a python interface enabling more flexibility, or through the terminal. Run the command GenerateFlameletData.py -h to see the available options. Optionally, flamelet data can be visualized through the PlotFlamelets.py command.

3. Process fluid data: Raw fluid data needs to be processed in order to be converted into a manifold usable in SU2. Especially a flamelet-based manifold requires additional steps to convert raw flamelet data into a usable manifold. These steps include optimizing the progress variable, homogenizing the flamelet data, and grouping the various flamelet data into groups of high correlation. TODO: write executable for this step.

4. Generate manifold: The processed fluid data is ready for conversion into a manifold for SU2 simulations. The available formats are the look-up table (LUT) and multi-layer perceptron (MLP).