笛卡尔网格下精确高效的壁面距离计算方法

孟爽; 周丹; 李雪亮; 毕林

doi:10.7638/kqdlxxb-2021.0359

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笛卡尔网格下精确高效的壁面距离计算方法

研究论文 | 更新时间：2026-05-28

- 笛卡尔网格下精确高效的壁面距离计算方法
- Accurate and efficient wall distance calculation method for Cartesian grids
- 空气动力学学报 2023年41卷第7期页码：93-101
- 作者机构：
  
  1. 中南大学轨道交通安全教育部重点实验室,长沙,410075
  2. 空气动力学国家重点实验室,绵阳,621000
  3. 中国空气动力研究与发展中心计算空气动力研究所,绵阳,621000
- 作者简介：
  
  孟爽（1994-），男，河南许昌人，博士研究生，研究方向：网格生成技术. E-mail：174211038@csu.edu.cn
  毕林*，研究员，研究方向：转捩、湍流. E-mail：bzbaby1010@163.com
- 基金信息：
  
  国家数值风洞工程（NNW2018-ZT1A02）;中南大学研究生自主探索创新项目（206021722）
- DOI：10.7638/kqdlxxb-2021.0359
  中图分类号： V211.3
- 纸质出版：2023
- 稿件说明：
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孟爽, 周丹, 李雪亮, 等. 笛卡尔网格下精确高效的壁面距离计算方法[J]. 空气动力学学报, 2023, 41(7): 93−101.

MENG Shuang, ZHOU Dan, LI Xueliang, et al. Accurate and efficient wall distance calculation method for Cartesian grids[J]. 2023, 41(7): 93-101.
孟爽, 周丹, 李雪亮, 等. 笛卡尔网格下精确高效的壁面距离计算方法[J]. 空气动力学学报, 2023, 41(7): 93−101. DOI： 10.7638/kqdlxxb-2021.0359

MENG Shuang, ZHOU Dan, LI Xueliang, et al. Accurate and efficient wall distance calculation method for Cartesian grids[J]. 2023, 41(7): 93-101. DOI： 10.7638/kqdlxxb-2021.0359.

摘要

对于笛卡尔网格方法，壁面距离是采用虚拟单元法精确处理物面边界的重要参数，同时也是网格自适应后制约流动计算效率的关键因素之一。针对现有壁面距离计算方法结果不精确、效率不高的问题，引入三角形参数化方法，将空间点到三角形物面离散网格的最小距离问题转换为约束条件下一维极值问题，仅需通过符号判断和少量加减乘运算，即可确定最小距离，计算精度和效率大幅提高；发展嵌套包围盒概念的KDT（K-dimensional tree）物面网格数据存储结构，优化KDT最近邻搜索算法中距物面较远数据点回溯过程，实现了最小距离对应的三角形的快速定位。运用球、导弹、DPW6等三维几何构型对上述方法考核验证结果表明，计算得到的壁面距离与解析值的误差在百万分之一以内，十亿量级网格规模下的单核计算效率接近已有文献中的并行计算效率。

Abstract

The wall distance of Cartesian grids is an essential parameter for the proper wall treatment using ghost cells and is also one of the critical factors governing the efficiency of the flow field simulation after mesh adaptation. This paper proposes a triangular parameterization method that converts the problem of computing the minimum distance between spatial points and discretized triangular meshes on the surface into a constrained one-dimensional extremum problem. This simplification only requires symbolic judgments and a small number of addition

subtraction and multiplication operations to obtain the minimal distance

yielding significant improvements in the accuracy and efficiency compared to existing methods. Meanwhile

a KDT (K-dimensional tree) data structure based on the nested enclosing box concept is developed to optimize the backtracking of data points far from the surface in the KDT nearest neighbor search algorithm. The application of this method to three-dimensional geometries such as spheres

missiles

and DPW6 demonstrates that the error between the computed wall distance and the resolved one is within one millionth. Moreover

the computational costs of using a single core for billion-scale grids are comparable to those of parallel computation using existing methods.

关键词

Keywords

references

CAPIZZANO F. Automatic generation of locally refined Cartesian meshes: data management and algorithms[J]. International Journal for Numerical Methods in Engineering, 2018, 113(5): 789-813. DOI:10.1002/nme.5636

XIN J J, CHEN Z L, SHI F, et al. A radial basis function–based ghost cell method for complex rigid or flexible moving boundary flows[J]. International Journal of Computational Methods, 2021, 18(1): 2050025. DOI:10.1142/s0219876220500255

HONG S, YOON D, HA S, et al. A ghost-cell immersed boundary method for unified simulations of flow over finite- and zero-thickness moving bodies at large CFL numbers[J]. Engineering Applications of Computational Fluid Mechanics, 2021, 15(1): 437-461. DOI:10.1080/19942060.2021.1880971

PETON N, LARDJANE N. An immersed boundary method for geometrical shock dynamics[J]. Journal of Computational Physics, 2020, 417: 109573. DOI:10.1016/j.jcp.2020.109573

AL-MAROUF M, SAMTANEY R. A versatile embedded boundary adaptive mesh method for compressible flow in complex geometry[J]. Journal of Computational Physics, 2017, 337: 339-378. DOI:10.1016/j.jcp.2017.02.044

SESHADRI P K, DE A. A novel sharp interface immersed boundary framework for viscous flow simulations at arbitrary Mach number involving complex and moving boundaries[J]. Computers Fluids, 2020, 206: 104579. DOI:10.1016/j.compfluid.2020.104579

PÉRON S, BENOIT C. Automatic off-body overset adaptive Cartesian mesh method based on an octree approach[J]. Journal of Computational Physics, 2013, 232(1): 153-173. DOI:10.1016/j.jcp.2012.07.029

GRIMBERG S, FARHAT C. Fast computation of the wall distance in unsteady Eulerian fluid-structure computations[J]. International Journal for Numerical Methods in Fluids, 2019, 89(4-5): 143-161. DOI:10.1002/fld.4686

TUCKER P G. Differential equation-based wall distance computation for DES and RANS[J]. Journal of Computational Physics, 2003, 190(1): 229-248. DOI:10.1016/S0021-9991(03)00272-9

XU J L, YAN C, FAN J J. Computations of wall distances by solving a transport equation[J]. Applied Mathematics and Mechanics, 2011, 32(2): 141-150. DOI:10.1007/s10483-011-1401-8

TUCKER P G, RUMSEY C L, SPALART P R, et al. Computations of wall distances based on differential equations[J]. AIAA Journal, 2005, 43(3): 539-549. DOI:10.2514/1.8626

TUCKER P G. Assessment of geometric multilevel convergence robustness and a wall distance method for flows with multiple internal boundaries[J]. Applied Mathematical Modelling, 1998, 22(4-5): 293-311. DOI:10.1016/S0307-904X(98)10007-0

WANG G, ZENG Z, YE Z Y. An efficient search algorithm for calculating minimum wall distance of unstructured mesh[J]. Journal of Northwestern Polytechnical University, 2014, 32(4): 511-516. (in Chinese)

GUO Z Z, HE Z Q, XIA C C, et al. KD tree method for efficient wall distance computation of mesh[J]. Journal of National University of Defense Technology, 2017, 39(4): 21-25. (in Chinese)

ROGET B, SITARAMAN J. Wall distance search algorithm using voxelized marching spheres[J]. Journal of Computational Physics, 2013, 241: 76-94. DOI:10.1016/j.jcp.2013.01.035

ZHAO Z, HE L, ZHANG J, et al. MPI/OpenMP hybrid parallel computation of wall distance for turbulence flow simulations[J]. Acta Aerodynamica Sinica, 2019, 37(6): 883-892. (in Chinese)

JONES M W. 3D distance from a point to a triangle[R]. Wales: Department of Computer Science, University of Wales Swansea, February 1995.

BOGER D A. Efficient method for calculating wall proximity[J]. AIAA Journal, 2001, 39(12): 2404-2406. DOI:10.2514/2.1251

BONET J, PERAIRE J. An alternating digital tree (ADT) algorithm for 3D geometric searching and intersection problems[J]. International Journal for Numerical Methods in Engineering, 1991, 31(1): 1-17. DOI:10.1002/nme.1620310102

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