Special Topic of Shock/Boundary Layer Interation Mechanism and Control

An extension of hybrid kinetic WENO method

  • HE Kang ,
  • LI Xinliang ,
  • LIU Hongwei
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  • 1. State Key Laboratory of High Temperature Gas Dynamics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China;
    2. School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100049, China

Received date: 2021-06-03

  Revised date: 2021-09-23

  Online published: 2021-09-22

Supported by

National Key Research and Development Program of China (2019YFA0405300,2016YFA0401200);National Natural Science Foundation of China (91852203, 11472278)

Abstract

An extension of the high-order WENO methods based on the gas-kinetic theory is carried out. The hybrid kinetic WENO method proposed in Int.J.Numer.Meth. Fluids 79(6),290-305(2015) is further extended to the 7th-order and the 9th-order cases. Within the framework of the 5th-order hybrid kinetic WENO method, the computational accuracy and efficiency of different shock detection techniques are compared. The TVD Runge-Kutta method is used for temporal integration, and the hybrid kinetic WENO method is employed for spatial discretization. Both one-dimensional and two-dimensional numerical examples are presented to show that the extended hybrid kinetic methods have higher resolution and less numerical dissipation than the traditional flux vector splitting technique, and can also have good shock-capturing ability. It is also found that the shock detection technology proposed by Ohwada et al. has good shock-detection ability and computational efficiency.

Cite this article

HE Kang , LI Xinliang , LIU Hongwei . An extension of hybrid kinetic WENO method[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022 , 43(1) : 625909 -625909 . DOI: 10.7527/S1000-6893.2021.25909

References

[1] LIU X D, OSHER S, CHAN T. Weighted essentially non-oscillatory schemes[J]. Journal of Computational Physics, 1994, 115(1): 200-212.
[2] HARTEN A, ENGQUIST B, OSHER S, et al. Uniformly high order accurate essentially non-oscillatory schemes, III[J]. Journal of Computational Physics, 1987, 71(1):231-303.
[3] JIANG G S, SHU C W. Efficient implementation of weighted ENO schemes[J]. Journal of Computational Physics, 1996, 126(1): 202-228.
[4] WANG Z J, CHEN R F. Optimized weighted essentially non-oscillatory schemes for linear waves with discontinuity[J]. Journal of Computational Physics, 2001, 174(1): 381-404.
[5] HENRICK A K, ASLAM T D, POWERS J M. Mapped weighted essentially non-oscillatory schemes: Achieving optimal order near critical points[J]. Journal of Computational Physics, 2005, 207(2): 542-567.
[6] MARTÍN M P, TAYLOR E M, WU M, et al. A bandwidth-optimized WENO scheme for the effective direct numerical simulation of compressible turbulence[J]. Journal of Computational Physics, 2006, 220(1): 270-289.
[7] BORGES R, CARMONA M, COSTA B, et al. An improved weighted essentially non-oscillatory scheme for hyperbolic conservation laws[J]. Journal of Computational Physics, 2008, 227(6): 3191-3211.
[8] SHEN Y Q, ZHA G C. Improvement of weighted essentially non-oscillatory schemes near discontinuities[J]. Computers & Fluids, 2014, 96: 1-9.
[9] MA Y K, YAN Z G, ZHU H J. Improvement of multistep WENO scheme and its extension to higher orders of accuracy[J]. International Journal for Numerical Methods in Fluids, 2016, 82(12): 818-838.
[10] HONG Z, YE Z Y, MENG X Z. A mapping-function-free WENO-M scheme with low computational cost[J]. Journal of Computational Physics, 2020, 405: 109145.
[11] SURESH A, HUYNH H T. Accurate monotonicity-preserving schemes with Runge-Kutta time stepping[J]. Journal of Computational Physics, 1997, 136(1): 83-99.
[12] BALSARA D S, SHU C W. Monotonicity preserving weighted essentially non-oscillatory schemes with increasingly high order of accuracy[J]. Journal of Computational Physics, 2000, 160(2): 405-452.
[13] FU D X, MA Y W. Analysis of super compact finite difference method and application to simulation of vortex-shock interaction[J]. International Journal for Numerical Methods in Fluids, 2001, 36(7): 773-805.
[14] ADAMS N A, SHARIFF K. A high-resolution hybrid compact-ENO scheme for shock-turbulence interaction problems[J]. Journal of Computational Physics, 1996, 127(1): 27-51.
[15] COCKBURN B, SHU C W. The Runge-Kutta discontinuous Galerkin method for conservation laws V: multidimensional systems[J]. Journal of Computational Physics, 1998, 141(2): 199-224.
[16] QIU J X, SHU C W. Runge-Kutta discontinuous Galerkin method using WENO limiters[J]. SIAM Journal on Scientific Computing, 2005, 26(3): 907-929.
[17] XU K, PRENDERGAST K H. Numerical Navier-Stokes solutions from gas kinetic theory[J]. Journal of Computational Physics, 1994, 114(1): 9-17.
[18] XU K. Gas-kinetic schemes for unsteady compressible flow simulations[C]//29th Computational Fluid Dynamics, Annual Lecture Series, 1998.
[19] XU K. A gas-kinetic BGK scheme for the Navier-Stokes equations and its connection with artificial dissipation and Godunov method[J]. Journal of Computational Physics, 2001, 171(1): 289-335.
[20] DESHPANDE S. Kinetic theory based new upwind methods for inviscid compressible flows[C]//24th Aerospace Sciences Meeting, 1986.
[21] PULLIN D I. Direct simulation methods for compressible inviscid ideal-gas flow[J]. Journal of Computational Physics, 1980, 34(2): 231-244.
[22] MANDAL J C, DESHPANDE S M. Kinetic flux vector splitting for Euler equations[J]. Computers & Fluids, 1994, 23(2): 447-478.
[23] CHOU S Y, BAGANOFF D. Kinetic flux-vector splitting for the Navier-Stokes equations[J]. Journal of Computational Physics, 1997, 130(2): 217-230.
[24] CHEN Y B, JIANG S. Modified kinetic flux vector splitting schemes for compressible flows[J]. Journal of Computational Physics, 2009, 228(10): 3582-3604.
[25] XIONG S W, ZHONG C W, ZHUO C S, et al. Numerical simulation of compressible turbulent flow via improved gas-kinetic BGK scheme[J]. International Journal for Numerical Methods in Fluids, 2011, 67(12): 1833-1847.
[26] LIAO W, PENG Y, LUO L S. Gas-kinetic schemes for direct numerical simulations of compressible homogeneous turbulence[J]. Physical Review E, 2009, 80(4): 046702.
[27] SUN Z S, REN Y X, LARRICQ C, et al. A class of finite difference schemes with low dispersion and controllable dissipation for DNS of compressible turbulence[J]. Journal of Computational Physics, 2011, 230(12): 4616-4635.
[28] HE Z W, LI X L, LIANG X. Nonlinear spectral-like schemes for hybrid schemes[J]. Science China Physics, Mechanics and Astronomy, 2014, 57(4): 753-763.
[29] LIU H W. A hybrid kinetic WENO scheme for inviscid and viscous flows[J]. International Journal for Numerical Methods in Fluids, 2015, 79(6): 290-305.
[30] COCKBURN B, SHU C W. TVB Runge-Kutta local projection discontinuous Galerkin finite element method for conservation laws. II. General framework[J]. Mathematics of Computation, 1989, 52(186): 411-435.
[31] KRIVODONOVA L, XIN J, REMACLE J F, et al. Shock detection and limiting with discontinuous Galerkin methods for hyperbolic conservation laws[J]. Applied Numerical Mathematics, 2004, 48(3-4): 323-338.
[32] XU Z F, SHU C W. Anti-diffusive flux corrections for high order finite difference WENO schemes[J]. Journal of Computational Physics, 2005, 205(2): 458-485.
[33] SURESH A, HUYNH H T. Accurate monotonicity-preserving schemes with Runge-Kutta time stepping[J]. Journal of Computational Physics, 1997, 136(1): 83-99.
[34] HARTEN A. Adaptive multiresolution schemes for shock computations[J]. Journal of Computational Physics, 1994, 115(2): 319-338.
[35] LI G, QIU J X. Hybrid weighted essentially non-oscillatory schemes with different indicators[J]. Journal of Computational Physics, 2010, 229(21): 8105-8129.
[36] RAY D, HESTHAVEN J S. An artificial neural network as a troubled-cell indicator[J]. Journal of Computational Physics, 2018, 367: 166-191.
[37] RAY D, HESTHAVEN J S. Detecting troubled-cells on two-dimensional unstructured grids using a neural network[J]. Journal of Computational Physics, 2019, 397: 108845.
[38] ABGRALL R, HAN VEIGA M. Neural network-based limiter with transfer learning[J]. Communications on Applied Mathematics and Computation, 2020: 1-41.
[39] SUN Z. Convolution neural network shock detector for numerical solution of conservation laws[J]. Communications in Computational Physics, 2020, 28(5): 2075-2108.
[40] SHU C W, OSHER S. Efficient implementation of essentially non-oscillatory shock-capturing schemes[J]. Journal of Computational Physics, 1988, 77(2): 439-471.
[41] STEGER J L, WARMING R F. Flux vector splitting of the inviscid gasdynamic equations with application to finite-difference methods[J]. Journal of Computational Physics, 1981, 40(2): 263-293.
[42] OHWADA T, FUKATA S. Simple derivation of high-resolution schemes for compressible flows by kinetic approach[J]. Journal of Computational Physics, 2006, 211(2): 424-447.
[43] GUO Z L, LIU H W, LUO L S, et al. A comparative study of the LBE and GKS methods for 2D near incompressible laminar flows[J]. Journal of Computational Physics, 2008, 227(10): 4955-4976.
[44] TITAREV V A, TORO E F. Finite-volume WENO schemes for three-dimensional conservation laws[J]. Journal of Computational Physics, 2004, 201(1): 238-260.
[45] SCHULZ-RINNE C W, COLLINS J P, GLAZ H M. Numerical solution of the Riemann problem for two-dimensional gas dynamics[J]. SIAM Journal on Scientific Computing, 1993, 14(6): 1394-1414.
[46] WOODWARD P, COLELLA P. The numerical simulation of two-dimensional fluid flow with strong shocks[J]. Journal of Computational Physics, 1984, 54(1): 115-173.
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