Multi-Group Maximum Entropy Method: Modeling Translational Non-Equilibrium
Anthony Chang, Narendra Singh, Marco Panesi

TL;DR
This paper introduces a reduced-order modeling approach for non-equilibrium gas dynamics by combining coarse-graining with the maximum entropy principle, enabling efficient simulation of complex kinetic phenomena.
Contribution
It develops a novel multi-group maximum entropy method that simplifies Boltzmann equation modeling while maintaining high accuracy in non-equilibrium gas flow simulations.
Findings
Accurately predicts non-equilibrium relaxation in gases.
Effectively models shockwave structures across various Mach numbers.
Aligns well with direct Boltzmann solutions in key metrics.
Abstract
The most rigorous physical description of non-equilibrium gas dynamics is rooted in the numerical solution of the Boltzmann equation. Yet, the large number of degrees of freedom and the wide range of both spatial and temporal scales render these equations intractable for many relevant problems. This study constructs a reduced-order model for the Boltzmann equation, by combining coarse-graining modeling framework with the maximum entropy principle. This is accomplished by projecting the high-dimensional Boltzmann equation onto a carefully chosen lower-dimensional subspace, resulting from the discretization of the velocity space into sub-volumes. Within each sub-volume, the distribution function is reconstructed through the maximum entropy principle, ensuring compliance with the detailed balance. The resulting set of conservation equations comprises mass, momentum, and energy for each…
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Taxonomy
TopicsAdvanced Thermodynamics and Statistical Mechanics
