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Abstract <p>The accurate computation of transport coefficients in the simulation of high-speed reacting gas flows is fundamentally important for analyzing heat transfer processes in various gas dynamics problems, such as the thermal protection calculations of reentry vehicles. The most physically correct models within the continuum approximation are multi-temperature and state-to-state approaches, which are constructed using the kinetic theory of transport and relaxation processes. However, these models possess significant computational complexity. For this reason, simplified one- and two-temperature approaches are commonly used, which are still computationally expensive for real-time modeling. One effective approach to applying such models is the regression of transport coefficients using machine learning. This work applies this approach to the modeling of viscosity and thermal conductivity coefficients for an 11-component ionized air mixture in the case of high-speed flow around Fire&#xa0;II reentry spacecraft.</p>

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Modeling of Transport Coefficients in Nonequilibrium High-Speed Flows Using Machine Learning

  • 期刊论文

  • 发表日期：

    2025年8月6日

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  • V. A. Istomin,
  • S. A. Pavlov

摘要

Abstract

The accurate computation of transport coefficients in the simulation of high-speed reacting gas flows is fundamentally important for analyzing heat transfer processes in various gas dynamics problems, such as the thermal protection calculations of reentry vehicles. The most physically correct models within the continuum approximation are multi-temperature and state-to-state approaches, which are constructed using the kinetic theory of transport and relaxation processes. However, these models possess significant computational complexity. For this reason, simplified one- and two-temperature approaches are commonly used, which are still computationally expensive for real-time modeling. One effective approach to applying such models is the regression of transport coefficients using machine learning. This work applies this approach to the modeling of viscosity and thermal conductivity coefficients for an 11-component ionized air mixture in the case of high-speed flow around Fire II reentry spacecraft.