Parallel tetrahedral mesh optimization method, system and equipment based on GPU (Graphics Processing Unit) and medium

By using GPU parallel processing technology and multi-threaded parallel execution of optimization operations, the problem of poor tetrahedral mesh shape in 3D geometric models was solved, achieving efficient mesh optimization and quality improvement.

CN120635366AActive Publication Date: 2025-09-12TAIHANG LABORATORY
4 Cites 2 Cited by

Patent Information

Application Number
CN202511093534.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-06
Publication Date
2025-09-12
Estimated Expiration
2045-08-06

AI Technical Summary

Technical Problem

When generating tetrahedral meshes of three-dimensional geometric models, existing technologies contain a large number of poorly shaped units, making it difficult to meet the mesh optimization efficiency and quality requirements of computational needs.

Method used

A GPU-based parallel tetrahedral mesh optimization method is adopted. Point smoothing, edge deletion, multi-face deletion, edge shrinkage and edge splitting operations are performed in parallel by multiple GPU threads. A parallel coloring strategy with priority determination and continuous array storage are combined to optimize the tetrahedral mesh.

Benefits of technology

It improves the speed and quality of mesh optimization, reduces the computational load of a single GPU thread, avoids control divergence problems, and improves computational efficiency and mesh quality.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention provides a GPU-based parallel tetrahedral mesh optimization method, system and device and a medium, and relates to the technical field of numerical simulation. Comprising the steps of generating a triangular mesh covering a surface for a three-dimensional geometric model; generating an initial tetrahedral mesh based on the triangular mesh; performing a round of point smoothing operation on each tetrahedron by using a plurality of GPU threads; carrying out iterative circulation of the combination optimization operation, and simultaneously carrying out a round of point smoothing, edge deletion and multi-surface deletion operation on a plurality of initial tetrahedrons by utilizing a plurality of GPU threads; and when the edge deletion operation and the multi-surface deletion operation cannot further improve the grid quality, simultaneously performing a round of edge contraction and edge segmentation operation on the plurality of initial tetrahedrons by utilizing the plurality of GPU threads. According to the method, the large-scale grid with reliable quality is quickly generated based on the low-quality input grid, and the grid optimization rate is improved, so that the requirement of large-scale unstructured grid optimization in a three-dimensional space is met.
Need to check novelty before this filing date? Find Prior Art