| title | SL-GPS |
|---|---|
| emoji | 🧪 |
| colorFrom | blue |
| colorTo | green |
| sdk | docker |
| app_file | app.py |
| pinned | false |
- Install basic libraries
pip install matplotlib tensorflow - Install cantera libraries
pip install cantera - Install numpy
pip install numpy - Install networkx (for parallel compute)
pip install networkx - Install scikit
pip install scikit-learn - Install the SL-GPS library
pip install "git+https://github.com/ctftamu/SL-GPS.git" - Test your installation by running any of the files in the tests folder
- The trained neural network is stored as .h5 file which can be accessed and utilized to produce reduced mechanisms for any given composition, temperature and pressure.
- Next, convert the generated .h5 file to .pb (frozen graph) to be used in OpenFOAM using the script converth5ToPb.ipynb in tests folder.
NOTE: The default neural network architecture is 16x8 (2 hidden layers). To change the neural network architecture, go to the file and edit the function spec_train according to your needs.
For questions and discussions please join : https://discord.com/channels/1333609076726431798/1333610748424880128
Please feel free to ask any questions related to SL-GPS there.
This repository contains the means to create a neural network architecture for dynamic chemistry reduction based on reduction results from Global Pathway Selection. The basic procedure is to first run adaptive GPS for 0D auto-ignition simulation so as to create a dataset. This dataset is later used for training the Artificial Neural Network (ANN). You can reach out to us at rmishra@tamu.edu (Rohit Mishra) or aaronnelson@tamu.edu (Aaron Nelson) for code issues, suggestions and/or pull requests.
This code was developed entirely in Python 3. Dependent packages include Cantera 3, Tensorflow 2, pandas, sklearn, numpy, pickle, and networkx. Code for GPS has been copied from https://github.com/golsun/GPS and modified to work in Python 3.
- Mishra, R., Nelson, A., Jarrahbashi, D., "Adaptive global pathway selection using artificial neural networks: A-priori study", Combustion and Flame, 244 (2022) 112279 [link]
If you use the validation studies or results from the validation/ directory, please cite this work.
- X. Gao, S. Yang, W. Sun, "A global pathway selection algorithm for the reduction of detailed chemical kinetic mechanisms", Combustion and Flame, 167 (2016) 238-247 [link]