RFF-meshing: a parallel and anisotropic quad-dominant mesh generation framework based on Riemann frame field

Compared to triangular meshes, quadrilateral meshes offer numerous advantages, such as enhanced fitting accuracy, the capacity to maintain geometric features, and alignment with specific problem orientations. However, there is currently no flawless method for the automatic generation of full quadrilateral meshes, which often leads to challenges such as subpar mesh quality and inadequate algorithm robustness when addressing regions with intricate boundaries. To overcome the limitations associated with full quadrilateral mesh generation, this paper proposes a parallel and anisotropic quad-dominant mesh generation framework based on Riemann frame fields (RFF). This framework (referred to as RFF-meshing) utilizes the triangulation of the geometric model as input and works following the four steps. Firstly, the geometric constraint boundaries are extracted through feature recognition. Secondly, RFF is computed on the surface using combinatorial alignment strategies. Thirdly, the background mesh is partitioned into sub-regions according to frame fields and feature edges. Finally, the anisotropic size field computation is conducted and the quadrilateral mesh is generated in parallel for all partitions. The proposed method has been evaluated with multiple computational fluid dynamics models with varying features, demonstrating its effectiveness and efficiency.