Streaming tetrahedral mesh optimization

Improving the quality of tetrahedral meshes is an important operation in many scientific computing applications. Meshes with badly shaped elements impact both the accuracy and convergence time of scientific applications. State-of-the-art mesh improvement techniques rely on sophisticated numerical optimization methods such as feasible Newton or conjugate gradient. Unfortunately, these methods cannot be practically applied to very large meshes due to their global nature. Our contribution in this paper is to describe a streaming framework for tetrahedral mesh optimization. This framework enables the optimization of meshes an order of magnitude larger than previously feasible ones and can effectively optimize meshes too large to fit in memory. Our results show that streaming is typically faster than the global optimization and results in comparable mesh quality. This leads us to conclude that streaming extends mesh optimization to a new class of mesh sizes without compromising the quality...

• Type: Journal
• Year: 2008
• Where: SMA
• Authors: Tian Xia, Eric Shaffer
• Keywords: Mesh, SMA 2008, Solid Modeling, Tetrahedral Mesh Optimization, Tetrahedral Meshes