AN EFFICIENT WALL-DISTANCE COMPUTATION IN RANS FLOW SIMULATION USING KD-TREE

C.W. Jeong; H. Lee; J.S. Park

doi:10.6112/kscfe.2026.31.1.065

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Original Article

AN EFFICIENT WALL-DISTANCE COMPUTATION IN RANS FLOW SIMULATION USING KD-TREE KD-트리를 활용한 RANS 유동 해석에서의 효율적인 벽면 거리 계산 기법

31 March 2026. pp. 65-78

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Abstract

In this study, a KD-tree-based wall-distance computation method is proposed to improve computational efficiency in compressible turbulent flow simulations on large unstructured grids. In Reynolds-averaged Navier-Stokes (RANS) turbulence modeling, accurate evaluation of the wall distance is essential for predicting near-wall turbulent behavior, while conventional Brute-force approaches suffer from rapidly increasing computational cost as the grid size grows. To improve computational efficiency, a two-step KD-tree search strategy is introduced to significantly reduce the number of wall elements involved in the distance calculation: candidate wall elements are first identified through a KD-tree-based proximity search, and the exact wall distance is then computed only for the selected candidates using a point-to-triangle geometric formulation. The proposed method is validated using the HB-2 configuration, the ONERA M6 wing, and the NASA CRM aircraft, demonstrating a substantial reduction in computational time compared to the Brute-force approach while maintaining reliable aerodynamic prediction accuracy.

Keywords

벽면 거리

KD-트리

전산유체역학

RANS 난류 모델

비정렬 격자

유한체적법

키워드

Wall distance

KD-tree

Computational fluid dynamics

RANS turbulence model

Unstructured grid

Finite volume method

References

2000, Spalart, P., “Trends in Turbulence Treatments,” AIAA Paper, No. 2000-2306.

10.2514/6.2000-2306

1996, Breuer M., Lakehal D. and Rodi W., “Flow around a Surface-Mounted Cubical Obstacle: Comparison of LES and RANS results,” Comput. Three-Dimens. Complex Flows, pp.22-30.

10.1007/978-3-322-89838-8_4

2002, Fares E. and Schroder W., “A Differential Equation to Determine the Wall Distance,” Int. J. for Numerical Methods in Fluids, Vol.39, pp.743-762.

10.1002/fld.348

2024, Zhang X., Diskin B. and Nielsen E. J., ”A Wall-Distance Method for Turbulence Modeling,” Proc. 12th Int. Conf. Comput. Fluid Dyn. (ICCFD12).

2013, Roget B., “Wall Distance Search Algorithm using Voxelized Marching Spheres,” J. Comput. Phys., pp.76-94.

10.1016/j.jcp.2013.01.035

2005, Tucker, P.G., “Computations of Wall Distances Based on Differential Equations,” AIAA J., Vol.43, No.3, pp.539-549.

10.2514/1.8626

1992, Spalart, P.R. and Allmaras, S.R., “ A One-Equation Tubulence Model for Aerodynamic Flows,” AIAA Paper 92-0439.

1994, Menter, F.R.,, “Two-Equation Eddy-Viscosity Turbulence Models for Engineering Applications,” AIAA J., Vol.32, No. 8, pp.1598-1605.

10.2514/3.12149

2022, Park, J.S., “pyBaram: Parallel compressible flow solver in high-performance Python for teaching and research,” Software X, Vol.20, 101272.

10.1016/j.softx.2022.101272

2012, Park, J.S. and Kim, C., “Multi-dimensional Limiting Process for Finite Volume Method on Unstructured Grid,” Comp. and fluid., Vol.65, pp.8-24.

10.1016/j.compfluid.2012.04.015

2024, Choi, S., Kim, D., Park, J. and Park, J.S., “Robust and accurate Roe-type Riemann solver with compact stencil: Rotated RoeM scheme,” J. Comput. Phys., Vol.505, No.15.

10.1016/j.jcp.2024.112913

2003, Eberly, D., Geometric Tools for Computer Graphics, Morgan Kaufmann, pp.374-382.

2020, Virtanen, P., Gommers, R., Oliphant, T.E., Haberland, M., Reddy, T., Cournapeau, D., et al., “SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python,” Nature Methods, Vol.17, No.3, pp.261-272.

1963,Gray, J.D. and Lindsay, E.E., “Force tests of standard hypervelocity ballistic models HB-1 and HB-2 at Mach 1.5 to 10,” AEDC-TDR-63-137.

1979, Schmitt, V. and Charpin, F., “Pressure distributions on the ONERA M6 wing at transonic Mach numbers,” AGARD Rep., No. AR-138.

2023, Hue, D., Sartor, F., Petropoulos, I. and Fournis, C.,, “DPW-7: Steady and Unsteady Computations of the Common Research Model at Different Reynolds Numbers,” AIAA J., Vol.60, No.6, pp.1857-1871.

10.2514/1.C037231

Information

Publisher :Korean Society for Computational Fluids Engineering
Publisher(Ko) :한국전산유체공학회
Journal Title :Journal of Computational Fluids Engineering
Journal Title(Ko) :한국전산유체공학회지
Volume : 31
No :1
Pages :65-78
Received Date : 2026-02-02
Revised Date : 2026-03-23
Accepted Date : 2026-03-24
DOI :https://doi.org/10.6112/kscfe.2026.31.1.065

Journal of Computational Fluids Engineering ISSN:1598-6071(Print) 3022-6252(Online) 한국전산유체공학회지

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