A massively parallel multi-block hybrid compact-WENO scheme for compressible flows

被引:26
|
作者
Chao, J. [1 ]
Haselbacher, A. [1 ]
Balachandar, S. [1 ]
机构
[1] Univ Florida, Dept Mech & Aerosp Engn, Gainesville, FL 32611 USA
关键词
Compact methods; Hybrid methods; Shock-capturing methods; Finite-difference methods; Compressible flows; Parallel computing; HYPERBOLIC CONSERVATION-LAWS; FINITE-DIFFERENCE SCHEMES; SHOCK-TURBULENCE INTERACTION; EFFICIENT IMPLEMENTATION; RESOLUTION; MESHES; SIMULATIONS; FILTERS;
D O I
10.1016/j.jcp.2009.07.005
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
A new multi-block hybrid compact-WENO finite-difference method for the massively parallel computation of compressible flows is presented. In contrast to earlier methods, our approach breaks the global dependence of compact methods by using explicit finite-difference methods at block interfaces and is fully conservative. The resulting method is fifth and sixth-order accurate for the convective and diffusive fluxes, respectively. The impact of the explicit interface treatment on the stability and accuracy of the multi-block method is quantified for the advection and diffusion equations. Numerical errors increase slightly as the number of blocks is increased. It is also found that the maximum allowable time steps increase with the number of blocks. The method demonstrates excellent scalability on up to 1264 processors. (C) 2009 Elsevier Inc. All rights reserved.
引用
收藏
页码:7473 / 7491
页数:19
相关论文
共 50 条
  • [21] An Efficient Low-Dissipation Hybrid Central/WENO Scheme for Compressible Flows
    Li, Liang
    Wang, Hong-Bo
    Zhao, Guo-Yan
    Sun, Ming-Bo
    Xiong, Da-Peng
    Tang, Tao
    INTERNATIONAL JOURNAL OF COMPUTATIONAL FLUID DYNAMICS, 2020, 34 (10) : 705 - 730
  • [22] An Edge Detector Based on Artificial Neural Network with Application to Hybrid Compact-WENO Finite Difference Scheme
    Xiao Wen
    Wai Sun Don
    Zhen Gao
    Jan S. Hesthaven
    Journal of Scientific Computing, 2020, 83
  • [23] An Edge Detector Based on Artificial Neural Network with Application to Hybrid Compact-WENO Finite Difference Scheme
    Wen, Xiao
    Don, Wai Sun
    Gao, Zhen
    Hesthaven, Jan S.
    JOURNAL OF SCIENTIFIC COMPUTING, 2020, 83 (03)
  • [24] A hybrid WENO5IS-THINC reconstruction scheme for compressible multiphase flows
    Zhang, Wenbin
    Fleischmann, Nico
    Adami, Stefan
    Adams, Nikolaus A.
    JOURNAL OF COMPUTATIONAL PHYSICS, 2024, 498
  • [25] Conservative hybrid compact-WENO schemes for shock-turbulence interaction
    Pirozzoli, S
    JOURNAL OF COMPUTATIONAL PHYSICS, 2002, 178 (01) : 81 - 117
  • [26] Parallel multi-block computation of incompressible flows for industrial applications
    Byrde, O
    Cobut, D
    Reymond, JD
    Sawley, ML
    PARALLEL COMPUTATIONAL FLUID DYNAMICS: IMPLEMENTATIONS AND RESULTS USING PARALLEL COMPUTERS, 1996, : 447 - 454
  • [27] Tensor-train WENO scheme for compressible flows
    Danis, M. Engin
    Truong, Duc
    Boureima, Ismael
    Korobkin, Oleg
    Rasmussen, Kim o.
    Alexandrov, Boian S.
    JOURNAL OF COMPUTATIONAL PHYSICS, 2025, 529
  • [28] HYBRID COMPACT-WENO FINITE DIFFERENCE SCHEME WITH CONJUGATE FOURIER SHOCK DETECTION ALGORITHM FOR HYPERBOLIC CONSERVATION LAWS
    Don, Wai-Sun
    Gao, Zhen
    Li, Peng
    Wen, Xiao
    SIAM JOURNAL ON SCIENTIFIC COMPUTING, 2016, 38 (02): : A691 - A711
  • [29] An efficient hybrid WENO scheme with minimized dispersion and adaptive dissipation properties for compressible flows
    Zhang, Yin
    Zhu, Yujie
    Sun, Zhensheng
    Li, Siye
    Hu, Yu
    Xia, Xuefeng
    Zhang, Wei
    COMPUTERS & FLUIDS, 2024, 280
  • [30] A multi-block shock-fitting technique to solve steady and unsteady compressible flows
    Nasuti, F
    COMPUTATIONAL FLUID DYNAMICS 2002, 2003, : 217 - 222