A Performance-Enhanced Cartesian Grid Generation Method: More Robust, Efficient, and Memory-Efficient

To address the limitations of poor robustness, low efficiency, and memory waste in traditional Cartesian grid generation, this paper proposes targeted improvements focusing on three key aspects: the surface mesh data structure, the Cartesian grid data structure, and the determination of the spatial relationship between the grid and the object surface. Specifically, a nested bounding box-based binary tree is established to store discrete triangles, enhancing the efficiency and accuracy of triangle data access. Secondly, a “member packaging and neighbor threading” Cartesian grid structure is constructed, improving generation speed while reducing memory consumption. Thirdly, a comprehensive Cartesian cell classification method is developed, integrating intersection checks, interior/exterior classification, and intersected cell center assessment; three strategies are further proposed to accelerate intersection checks for large-scale grids. The performance of Cartesian grid generation is compared with traditional methods and the commercial CFD code. The results demonstrate a grid generation efficiency of less than two seconds per million cells, significantly outperforming traditional methods, with a 20% reduction in memory consumption. Finally, an airflow simulation past a train illustrates the suitability of the generated grids for practical computational applications.