A graph neural network based chemical mechanism reduction method for combustion applications

TL;DR

This paper introduces two novel graph neural network-based methods for reducing complex chemical reaction mechanisms, enabling faster simulations of turbulent reacting flows while maintaining accuracy across various conditions.

Contribution

The paper presents two new GNN-based reduction methods, GNN-SM and GNN-AE, that outperform traditional techniques in accuracy and reduction efficiency for detailed chemical mechanisms.

Findings

GNN-SM achieves reductions comparable to DRGEP with broad accuracy.

GNN-AE reduces mechanisms by up to 95% while maintaining accuracy.

Both methods outperform traditional reduction techniques in various scenarios.

Abstract

Direct numerical simulations of turbulent reacting flows involving millions of grid points and detailed chemical mechanisms with hundreds of species and thousands of reactions are computationally prohibitive. To address this challenge, we present two data-driven chemical mechanism reduction formulations based on graph neural networks (GNNs) with message-passing transformer layers that learn nonlinear dependencies among species and reactions. The first formulation, GNN-SM, employs a pre-trained surrogate model to guide reduction across a broad range of reactor conditions. The second formulation, GNN-AE, uses an autoencoder formulation to obtain highly compact mechanisms that remain accurate within the thermochemical regimes used during training. The approaches are demonstrated on detailed mechanisms for methane (53 species, 325 reactions), ethylene (96 species, 1054 reactions), and iso-octane (1034 species, 8453 reactions). GNN-SM achieves reductions comparable to the established graph-based method DRGEP while maintaining accuracy across a wide range of thermochemical states. In contrast, GNN-AE achieves up to 95% reduction in species and reactions and outperforms DRGEP within its target conditions. Overall, the proposed framework provides an automated, machine-learning-based pathway for chemical mechanism reduction that can complement traditional expert-guided analytical approaches.