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3D streamers simulation in a pin to plane configuration using massively parallel computing

Abstract : This paper concerns the 3D simulation of corona discharge using high performance computing (HPC) managed with the message passing interface (MPI) library. In the field of finite volume methods applied on non-adaptive mesh grids and in the case of a specific 3D dynamic benchmark test devoted to streamer studies, the great efficiency of the iterative R&B SOR and BiCGSTAB methods versus the direct MUMPS method was clearly demonstrated in solving the Poisson equation using HPC resources. The optimization of the parallelization and the resulting scalability was undertaken as a function of the HPC architecture for a number of mesh cells ranging from 8 to 512 million and a number of cores ranging from 20 to 1600. The R&B SOR method remains at least about four times faster than the BiCGSTAB method and requires significantly less memory for all tested situations. The R&B SOR method was then implemented in a 3D MPI parallelized code that solves the classical first order model of an atmospheric pressure corona discharge in air. The 3D code capabilities were tested by following the development of one, two and four coplanar streamers generated by initial plasma spots for 6 ns. The preliminary results obtained allowed us to follow in detail the formation of the tree structure of a corona discharge and the effects of the mutual interactions between the streamers in terms of streamer velocity, trajectory and diameter. The computing time for 64 million of mesh cells distributed over 1000 cores using the MPI procedures is about 30 min ns−1, regardless of the number of streamers.
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Contributor : Philippe Dessante <>
Submitted on : Friday, June 8, 2018 - 10:55:54 AM
Last modification on : Wednesday, September 16, 2020 - 5:51:21 PM



Philippe Dessante, M Plewa, O. Eichwald, O. Ducasse, Carolyn Jacobs, et al.. 3D streamers simulation in a pin to plane configuration using massively parallel computing. Journal of Physics D: Applied Physics, IOP Publishing, 2018, 51 (9), ⟨10.1088/1361-6463/aaa91b⟩. ⟨hal-01810755⟩



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