Magnetohydrodynamic with Adaptively Embedded Particle-in-Cell model: MHD-AEPIC
Magnetohydrodynamic with Adaptively Embedded Particle-in-Cell model: MHD-AEPIC
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DOI：
10.1016/j.jcp.2021.110656
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Y. Shou;V. Tenishev;Yuxi Chen;G. Tóth;N. Ganushkina
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文献类型：
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作者：
Y. Shou;V. Tenishev;Yuxi Chen;G. Tóth;N. Ganushkina
空间等离子体模拟发现，嵌入粒子单元(PIC)模型的磁流体动力学(MHD)的使用有所增加。这种组合的MHD-EPIC算法使用动力学PIC方法模拟一些感兴趣的区域，同时在域的其余部分采用MHD描述。MHD模型具有很高的计算效率，其流体描述适用于计算领域的大部分区域，从而使得大规模全球模拟成为可能，但在实际应用中，动力学效应起关键作用的区域可能在计算领域发生变化、出现、消失和移动。如果使用静态PIC区域，则需要比实际需要大得多的PIC区域，这会显著增加计算成本。为了解决这个问题，我们开发了一种能够动态改变应用PIC模型的计算区域的方法。我们使用BATS-R-US Hall MHD和自适应网格粒子模拟器(AMPS)作为半隐式PIC模型，用自适应嵌入PIC(MHD-AEPIC)算法实现了这种新的MHD。我们描述了该算法，并给出了一个两条合并磁链的测试案例，以验证算法的准确性。该实现使用内存的动态分配/释放和负载平衡来实现高效的并行执行。我们评估了MHD-AEPIC与MHD-EPIC的性能，以及该模型在大量计算核上的伸缩性。
Space plasma simulations have seen an increase in the use of magnetohydrodynamic (MHD) with embedded Particle-in-Cell (PIC) models. This combined MHD-EPIC algorithm simulates some regions of interest using the kinetic PIC method while employing the MHD description in the rest of the domain. The MHD models are highly efficient and their fluid descriptions are valid for most part of the computational domain, thus making large-scale global simulations feasible.However, in practical applications, the regions where the kinetic effects are critical can be changing, appearing, disappearing and moving in the computational domain. If a static PIC region is used, this requires a much larger PIC domain than actually needed, which can increase the computational cost dramatically.To address the problem, we have developed a new method that is able to dynamically change the region of the computational domain where a PIC model is applied. We have implemented this new MHD with Adaptively Embedded PIC (MHD-AEPIC) algorithm using the BATS-R-US Hall MHD and the Adaptive Mesh Particle Simulator (AMPS) as the semi-implicit PIC models. We describe the algorithm and present a test case of two merging flux ropes to demonstrate its accuracy. The implementation uses dynamic allocation/deallocation of memory and load balancing for efficient parallel execution. We evaluate the performance of MHD-AEPIC compared to MHD-EPIC and the scaling properties of the model to large number of computational cores.