DFODE-kit: Deep Learning Package for Combustion Kinetics

DFODE-kit is an open-source Python package designed to accelerate combustion simulations by efficiently solving flame chemical kinetics governed by high-dimensional stiff ordinary differential equations (ODEs). This package integrates deep learning methodologies to replace conventional numerical integration, enabling significant speedups and improved accuracy.

Features

• Efficient Sampling Module: Extracts high-quality thermochemical states from low-dimensional manifolds in canonical flames.
• Data Augmentation: Enhances training datasets to approximate high-dimensional composition spaces in turbulent flames.
• Neural Network Implementation: Supports optimized training with physical constraints to ensure model fidelity.
• Seamless Integration: Easily deploy trained models within the DeepFlame CFD solver or other platforms like OpenFOAM.
• Robust Performance: Achieves high accuracy with up to two orders of magnitude speedup in various combustion scenarios.

Environment Setup

Create a conda environment with Python 3.9:

conda create --name dfode_env python=3.9
conda activate dfode_env

Installation

To install DFODE-kit, clone the repository and install the dependencies:

git clone https://github.com/deepflame-ai/DFODE-kit.git
cd DFODE-kit
pip install -e .

Usage

Once you have installed DFODE-kit, you can use it to sample data, augment datasets, train models, and make predictions. Below is a basic command-line interface (CLI) format:

dfode-kit CMD ARGS

Commands Available:

• sample: Perform raw data sampling from canonical flame simulations.
• augment: Apply random noise and physical constraints to improve the training dataset.
• label: Generate supervised learning labels using Cantera's CVODE solver.
• train: Train neural network models based on the specified datasets and parameters.

Comprehensive tutorials are provided in the tutorials/ directory, including step-by-step guides for 1D premixed flames and 2D HIT flames.

Note that running the simulations requires DeepFlame to be installed. Refer to the DeepFlame GitHub repository and documentation for installation instructions.

Directories

• dfode-kit: Main procedure and functions.
• mechanisms: Thermochemical mechanism folder.
• canonical_cases: Canonical cases for data sampling.
• tutorials: Tutorials with sampling cases.