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The Bhatnagar-Gross-Krook (BGK) equation is widely used as an efficient model for flow simulations in transitional Knudsen number regimes. The two main types of solvers based on the BGK model, the deterministic discrete velocity methods (DVM) and the stochastic particle-based BGK solvers, each come with specific disadvantages such as six-dimensional discretization or statistical noise. This work introduces a coupling in velocity space between both approaches, creating a flexible noise-reduced method capable of accelerating the resolution of multiscale problems. The method is verified on test cases, showing improved efficiency compared to the pure DVM or the pure particle-based solver.

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Combining Stochastic Particle BGK and Discrete Velocity Method for Efficient Multiscale Simulations

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Félix Garmirian,
  • Marcel Pfeiffer

摘要

The Bhatnagar-Gross-Krook (BGK) equation is widely used as an efficient model for flow simulations in transitional Knudsen number regimes. The two main types of solvers based on the BGK model, the deterministic discrete velocity methods (DVM) and the stochastic particle-based BGK solvers, each come with specific disadvantages such as six-dimensional discretization or statistical noise. This work introduces a coupling in velocity space between both approaches, creating a flexible noise-reduced method capable of accelerating the resolution of multiscale problems. The method is verified on test cases, showing improved efficiency compared to the pure DVM or the pure particle-based solver.