一种用于大规模多域模拟的新型高效拉格朗日模型：并行化背景
A novel highly efficient Lagrangian model for massively multidomain simulations: parallel context
AI总结：
本研究在已有RealIMotion多域模拟方法基础上，基于OpenMPI实现分布式内存并行化，针对多晶体晶粒长大场景测试验证，为大规模多域演化模拟提供高效并行方案。
摘要：
在先前的研究（RealIMotion，Florez等人，2020年）中，研究者提出了一种用于模拟演化型多域问题的新方法。本文将介绍该模型的进一步拓展成果，核心研究重点是采用分布式内存方案结合消息传递接口（MPI）库OpenMPI，实现鲁棒的并行化实现。\n原始的二维串行方法基于改进的前沿追踪法，其主要创新点在于不仅对域间界面进行离散化，还对域内部进行网格划分。界面通过网格上每个节点的拓扑度进行追踪，而域的网格重构与拓扑变化由在单元片上执行的选择性局部操作驱动。Florez等人（2020年）的研究已证明，该串行方法的精度与性能极具前景。\n本文针对多晶体曲率流驱动的运动场景（即考虑晶粒长大（GG）机制），对并行实现方案进行了讨论与测试。文中给出了该模型的性能结果，并与文献中其他方法的性能进行了对比讨论。
英文摘要：
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.
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