KD-트리를 활용한 RANS 유동 해석에서의 효율적인 벽면 거리 계산 기법

초록

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.