论文标题
一种新型的高效拉格朗日模型,用于大规模多域模拟:平行上下文
A novel highly efficient Lagrangian model for massively multidomain simulations: parallel context
论文作者
论文摘要
弗洛雷斯(Florez)等人在先前的工作(Realimotion)中引入了一种模拟多域问题发展的新方法。 (2020)。在本文中,将介绍该模型的进一步发展。这里的主要重点是使用分布式内存方法与消息传递接口(MPI)库OpenMPI进行了强大的并行实现。原始的2D顺序方法包括一种修改的前跟踪方法,其中主要独创性不仅是域之间的接口,而且它们的内部也被啮合。基于网格上每个节点的拓扑程度跟踪接口,并且域的重构和拓扑变化是由在元素贴片上执行的选择性局部操作驱动的。在Florez等人中,顺序方法的准确性和性能非常有前途。 (2020)。在本文中,将通过曲率流进行多晶的曲率流动,即通过考虑晶粒生长(GG)机制来讨论和测试。给出了模型性能的结果,并讨论了文献中其他方法的比较。
A new method for the simulation of evolving multi-domains problems has been introduced in a previous work (RealIMotion), Florez et al. (2020). In this article further developments of the model will be presented. The main focus here is a robust parallel implementation using a distributed-memory approach with the Message Passing Interface (MPI) library OpenMPI. The original 2D sequential methodology consists in a modified front-tracking approach where the main originality is that not only interfaces between domains are discretized but their interiors are also meshed. The interfaces are tracked based on the topological degree of each node on the mesh and the remeshing and topological changes of the domains are driven by selective local operations performed over an element patch. The accuracy and the performance of the sequential method has proven very promising in Florez et al. (2020). In this article a parallel implementation will be discussed and tested in context of motion by curvature flow for polycrystals, i.e. by considering Grain Growth (GG) mechanism. Results of the performance of the model are given and comparisons with other approaches in the literature are discussed.