Petrov-Galerkin model reduction for thermochemical nonequilibrium gas mixtures

TL;DR

This paper introduces a Petrov-Galerkin model reduction method for thermochemical nonequilibrium gas mixtures that significantly reduces computational costs while maintaining high accuracy, enabling efficient large-scale plasma simulations.

Contribution

The paper presents a novel Petrov-Galerkin projection-based model reduction pipeline that leverages low-rank dynamics and linearized equations to efficiently approximate complex kinetic systems.

Findings

Achieves less than 1% error in macroscopic quantities

Attains 10% error in microscopic energy level populations

Outperforms existing techniques in speed and compression

Abstract

State-specific thermochemical collisional models are crucial to accurately describe the physics of systems involving nonequilibrium plasmas, but they are also computationally expensive and impractical for large-scale, multi-dimensional simulations. Historically, computational cost has been mitigated by using empirical and physics-based arguments to reduce the complexity of the governing equations. However, the resulting models are often inaccurate and they fail to capture the important features of the original physics. Additionally, the construction of these models is often impractical, as it requires extensive user supervision and time-consuming parameter tuning. In this paper, we address these issues through an easily-implementable and computationally-efficient model reduction pipeline based on the Petrov-Galerkin projection of the nonlinear kinetic equations. Our approach is justified by the observation that kinetic systems in thermal nonequilibrium tend to exhibit low-rank dynamics that rapidly drive the state towards a low-dimensional subspace. Furthermore, despite the nonlinear nature of the governing equations, we observe that the dynamics of these systems evolve on subspaces that can be accurately identified using the linearized equations about thermochemical equilibrium, which significantly reduce the cost associated with the construction of the model. The approach is demonstrated on a rovibrational collisional model for the O$_2$-O system, and a vibrational collisional model for the combined O$_2$-O and O$_2$-O$_2$ systems. Our method achieves high accuracy, with relative errors of less than 1% for macroscopic quantities (i.e., moments) and 10% for microscopic quantities (i.e., energy levels population), while also delivering excellent compression rates and speedups, outperforming existing state-of-the-art techniques.