Exact and efficient wall distance pre-classification overset grid assembly for unstructured grids

Abstract

In this paper, we present a pre-classification overset grid assembly (OGA) based on the wall distance criterion. In contrast to the conventional OGA, where donor search is preceded by point classification, the proposed methodology first performs point classification based on the wall distance criterion, and then performs donor search on the reduced set of cells. To robustly profile the given grid components, a wall distance evaluation is performed exactly by considering the full geometry of the discretized surface while maintaining efficiency. The efficiency of the algorithm is achieved by redefining the methodology into a bounded form and establishing a conservative correlation between the vertex-based and element-based distances through a rigorous examination of geometric relations. From these observations, wall distance calculation, which requires a tremendous amount of computation, is divided into sequential steps of vertex-based approximate calculation and element-based exact calculation, dramatically reducing the computation load. To better adapt to parallel environments, load balancing is presented, and additionally, some voxelization techniques are developed to efficiently process the required geometric data. Numerous test cases are analyzed to demonstrate the efficiency and robustness of the proposed algorithm in various situations. The proposed algorithm consistently outperforms the conventional OGA, and also proves its robustness in the presence of high proximity objects, which highlights its suitability for large-scale multi-body problems.