Physics Informed Machine Learning for Chemistry Tabulation

TL;DR

This paper introduces a physics-informed machine learning approach that jointly learns reaction progress variables and lookup models for turbulent combustion, improving accuracy over traditional separate methods.

Contribution

It presents a novel joint learning framework for chemistry tabulation in combustion, integrating dynamic thermochemical state variables with deep neural networks.

Findings

Joint learning improves model accuracy.

The proposed method outperforms existing independent approaches.

Demonstrated superior performance through experiments.

Abstract

Modeling of turbulent combustion system requires modeling the underlying chemistry and the turbulent flow. Solving both systems simultaneously is computationally prohibitive. Instead, given the difference in scales at which the two sub-systems evolve, the two sub-systems are typically (re)solved separately. Popular approaches such as the Flamelet Generated Manifolds (FGM) use a two-step strategy where the governing reaction kinetics are pre-computed and mapped to a low-dimensional manifold, characterized by a few reaction progress variables (model reduction) and the manifold is then ``looked-up'' during the runtime to estimate the high-dimensional system state by the flow system. While existing works have focused on these two steps independently, in this work we show that joint learning of the progress variables and the look--up model, can yield more accurate results. We build on the base formulation and implementation ChemTab to include the dynamically generated Themochemical State Variables (Lower Dimensional Dynamic Source Terms). We discuss the challenges in the implementation of this deep neural network architecture and experimentally demonstrate it's superior performance.