A reconstruction method for finite volume flowfield solving based on incremental radial basis functions

被引:0
|
作者
Liu, Yilang [1 ]
Zhang, Weiwei [1 ]
Jiang, Yuewen [2 ]
Ye, Zhengyin [1 ]
机构
[1] College of Aeronautics, Northwestern Polytechnical University, Xi'an,710072, China
[2] Department of Engineering Science, University of Oxford, Oxford,OX1 3PJ, United Kingdom
基金
中国国家自然科学基金;
关键词
Wind tunnels - One dimensional - Computational efficiency - Interpolation - Radial basis function networks - Flow fields - Functions;
D O I
10.6052/0459-1879-14-028
中图分类号
学科分类号
摘要
A reconstruction method of flow field solving, based on incremental RBF (Radial Basis Functions) interpolation, has been developed in the paper. Since the fluctuation of flow parameters in the stencil cells used to reconstruct is small compared with the mean value in flow field reconstruction, direct RBF reconstruction will bring large numerical oscillations. The incremental RBF reconstruction developed in this paper effectively improves convergence and stability of the interpolation scheme. In first example, a simple one-dimensional model is used to illustrate the effective of this method when the fluctuation of the objective function is much smaller than the mean value. Furthermore, applicability and effectiveness of incremental RBF reconstruction method is proved by using four typical flow fields, namely, two-dimensional subsonic, transonic inviscid steady flow fields around NACA0012, the viscid unsteady flow around a stationary cylinder and a Mach 3 wind tunnel case with a step problem. Research shows that incremental RBF reconstruction method can smoothly capture steep shock and effectively improve the convergence and stability of flow solver with small numerical dissipation and high computational efficiency.
引用
收藏
页码:694 / 702
相关论文
共 50 条
  • [31] A method for simulation based optimization using radial basis functions
    Jakobsson, Stefan
    Patriksson, Michael
    Rudholm, Johan
    Wojciechowski, Adam
    OPTIMIZATION AND ENGINEERING, 2010, 11 (04) : 501 - 532
  • [32] A modified approximate method based on Gaussian radial basis functions
    Liu, Wei
    Wang, Baiyu
    ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS, 2019, 100 : 256 - 264
  • [33] Investigation of the use of radial basis functions in local collocation method for solving diffusion problems
    Chantasiriwan, S
    INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER, 2004, 31 (08) : 1095 - 1104
  • [34] A point interpolation meshless method based on radial basis functions
    Wang, JG
    Liu, GR
    INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, 2002, 54 (11) : 1623 - 1648
  • [35] Meshless method based on coupled radial and polynomial basis functions
    Zeng, Qing-Hong
    Lu, De-Tang
    Jisuan Wuli/Chinese Journal of Computational Physics, 2005, 22 (01): : 43 - 50
  • [36] Finite volume solvers for the shallow water equations using matrix radial basis function reconstruction
    Bonaventura, L.
    Miglio, E.
    Saleri, F.
    NUMERICAL MATHEMATICS AND ADVANCED APPLICATIONS, 2006, : 207 - +
  • [37] A MESHLESS LOCAL GALERKIN INTEGRAL EQUATION METHOD FOR SOLVING A TYPE OF DARBOUX PROBLEMS BASED ON RADIAL BASIS FUNCTIONS
    Assari, P.
    Asadi-Mehregan, F.
    Dehghan, M.
    ANZIAM JOURNAL, 2021, 63 (04): : 469 - 492
  • [38] A method for simulation based optimization using radial basis functions
    Stefan Jakobsson
    Michael Patriksson
    Johan Rudholm
    Adam Wojciechowski
    Optimization and Engineering, 2010, 11 : 501 - 532
  • [39] Finite element method for solving the Dirac eigenvalue problem with linear basis functions
    Almanasreh, Hasan
    JOURNAL OF COMPUTATIONAL PHYSICS, 2019, 376 : 1199 - 1211
  • [40] An high order finite co-volume scheme for denoising using radial basis functions
    Morigi, Serena
    Sgallari, Fiorella
    SCALE SPACE AND VARIATIONAL METHODS IN COMPUTER VISION, PROCEEDINGS, 2007, 4485 : 43 - +