With the development of high-performance computers, the requirements of numerical simulations on the large scale of girds are increasing. However, the quality of ultra-large-scale grids is difficult to meet the demand.
This study proposes an improved high-quality parallel tetrahedral generation algorithm. Key methodological steps include: (1) Employing an enhanced dual graph weight-based domain decomposition method to optimize interface elements, ensure rapid partitioning, and prevent bad cell exposure at interfaces. (2) Developing the extendable node numbering method (ExNN) to achieve fully decoupled parallelization of curve, surface, and solid meshes, enabling boundary updates/partition merging with near-linear time complexity and zero communication overhead, and flexible load balancing in curve/surface parallel processes. (3) Advancing surface projection via parametric coordinates in recursion to ensure efficiency and avoid cell flipping.
The algorithm enhances the mesh quality of the partitioned interface and the generation efficiency of massively parallel tetrahedral meshes. With the improved decoupling of the parallel algorithm, an engineering verification of meshing complex geometric models with more than 1 billion elements in 2 minutes on a 1000-core high-performance computer is passed, demonstrating that the method possesses good parallel efficiency and scalability.
This work contributes critical innovations to large-scale parallel mesh generation: (1) The ExNN method achieves fully decoupled parallelization of curve, surface, and solid meshes, resolving high communication overhead and complexity. (2) The dual graph weight-based decomposition balances interface optimization and partitioning speed, while parametric coordinate projection ensures efficiency and cell stability. (3) ExNN-based fast partition merging (without template restrictions) preserves freedom for parallel refinement.
