Abstract

The gas-kinetic scheme (GKS) and the unified gas-kinetic scheme (UGKS) are numerical methods based on the gas-kinetic theory, which have been widely used in the numerical simulations of high-speed and non-equilibrium flows. Both methods employ a multiscale flux function constructed from the integral solutions of kinetic equations to describe the local evolution process of particles’ free transport and collision. The accumulating effect of particles’ collision during transport process within a time step is used in the construction of the schemes, and the intrinsic simulating flow physics in the schemes depends on the ratio of the particle collision time and the time step, i.e., the so-called cell’s Knudsen number. With the initial distribution function reconstructed from the Chapman–Enskog expansion, the GKS can recover the Navier–Stokes solutions in the continuum regime at a small Knudsen number, and gain multi-dimensional properties by taking into account both normal and tangential flow variations in the flux function. By employing a discrete velocity distribution function, the UGKS can capture highly non-equilibrium physics, and is capable of simulating continuum and rarefied flow in all Knudsen number regimes. For high-speed non-equilibrium flow simulation, the real gas effects should be considered, and the computational efficiency and robustness of the schemes are the great challenges. Therefore, many efforts have been made to improve the validity and reliability of the GKS and UGKS in both the physical modeling and numerical techniques. In this paper, we give a review of the development of the GKS and UGKS in the past decades, such as physical modeling of a diatomic gas with molecular rotation and vibration at high temperature, plasma physics, computational techniques including implicit and multigrid acceleration, memory reduction methods, and wave–particle adaptation.

Highlights

  • High-speed flows are usually involved in aeronautical and aerospace engineering, such as in the re-entry of spacecraft, launch of rockets, and near-space vehicle cruising

  • Regardless of the quadrature rule in velocity space, the conservation law is always satisfied on the macroscopic level in the conserved discrete unified gas-kinetic scheme (DUGKS), which could benefit in the numerical calculations where conservation is of much importance, such as for highly nonequilibrium flow and plasma physics [48,104,125]

  • We have reviewed the development of the gas-kinetic scheme (GKS) and the unified gas-kinetic scheme (UGKS) in the aspects of physical modeling and numerical algorithms

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Summary

Introduction

High-speed flows are usually involved in aeronautical and aerospace engineering, such as in the re-entry of spacecraft, launch of rockets, and near-space vehicle cruising. The key ingredients of the UGKS are to follow the basic conservation laws of the macroscopic flow variables and the microscopic gas distribution function in a discretized space, and to construct a multiscale flux function from the integral solution of the kinetic equation, taking into account the accumulating effects of particles’ collision during the transport process in the scale of a numerical time-step. In the discontinuous shock region, the numerical relaxation time τ is large and the GKS becomes an upwind scheme to capture the discontinuity; in the smooth region at a small τ relative to the time step ∆t, the GKS recovers the central difference scheme with high-order accuracy Another distinguishable feature of the GKS is that both the normal and tangential gradients along the cell interface participate in the flux evaluation, which results in a multi-dimensional hydrodynamic NS solver [3]. The GKS shows excellent performance in the aspects of robustness and accuracy for simulating high-speed viscous flows

Multiple Temperature Model
Multi-Component and Reactive Flow
Implicit Scheme and Multigrid Acceleration
Unified Gas-Kinetic Scheme for High-Speed Flows
Diatomic Gas with Molecular Rotation and Vibration
Multi-Component Gas Mixture and Plasma
Computational Techniques
Implicit UGKS and Multigrid Acceleration
Parallel Strategy
Adaptive Mesh
Memory Reduction
Wave–Particle Adaptation
Conclusions
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