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A Parallel DSMC Implementation Including Adaptive Mesh Refinement And Cut-Cell Algorithms

机译:包含自适应网格细化和剪切单元算法的并行DSMC实现

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Parallel implementation of a three-dimensional direct simulation Monte Carlo (DSMC) code is outlined that runs on distributed memory systems using the Message-Passing Interface (MPI). The code employs an embedded 3-level Cartesian mesh, accompanied by a cut-cell algorithm to incorporate triangulated surface geometry into the adaptively refined Cartesian mesh. Two separate data structures are proposed in order to separate geometry data from cell and particle information. The geometry data structure requires little memory so that each partition in a parallel simulation can store large portions of the entire mesh. This has the potential to decrease inter-processor communication for particle movement and for adaptive mesh refinement in large parallel simulations. Parallel strategies are detailed for the main DSMC loop, adaptive mesh refinement, and the cut-cell routine. Parallel scalability is tested on a simple test case of free-stream flow. For a fixed problem size, linear scaling is demonstrated until the number of simulation particles per processor falls below approximately 625,000. However, when the problem size is increased with the number of processors (constant granularity), linear scaling is demonstrated up to 128 processors.
机译:概述了三维直接仿真蒙特卡罗(DSMC)代码的并行实现,该代码在使用消息传递接口(MPI)的分布式存储系统上运行。该代码采用了嵌入式3级笛卡尔网格,并辅以cut-cell算法,将三角化的曲面几何体合并到自适应精制的笛卡尔网格中。为了从细胞和粒子信息中分离出几何数据,提出了两个分离的数据结构。几何数据结构几乎不需要内存,因此并行模拟中的每个分区都可以存储整个网格的大部分。在大型并行仿真中,这有可能减少用于粒子移动和自适应网格细化的处理器间通信。详细说明了DSMC主循环,自适应网格细化和剪切单元例程的并行策略。并行可伸缩性是在一个简单的自由流测试案例中进行测试的。对于固定的问题大小,将演示线性缩放,直到每个处理器的模拟粒子数量下降到大约625,000以下。但是,当问题的大小随着处理器数量的增加而增加时(恒定的粒度),则线性扩展最多可显示128个处理器。

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