Sparse Grid reconstructions for Particle-In-Cell methods

C. Guillet, F. Deluzet, G. Fubiani, L. Garrigues, J. Narski
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引用次数: 5

Abstract

In this article, we propose and analyse Particle-In-Cell (PIC) methods embedding sparse grid reconstruction as those introduced in [1, 2]. The sparse grid reconstructions offer a significant improvement on the sta- tistical error of PIC schemes as well as a reduction in the complexity of the problem providing the electric field. Main results on the convergence of the electric field interpolant and conservation properties are provided in this pa- per. Besides, tailored sparse grid reconstructions, in the frame of the offset combination technique, are proposed to introduce PIC methods with improved efficiency. The methods are assessed numerically and compared to existing PIC schemes thanks to classical benchmarks with remarkable prospects for three dimensional computations.
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基于粒子单元法的稀疏网格重建
在本文中,我们提出并分析了嵌入稀疏网格重建的PIC方法,如[1,2]中所介绍的方法。稀疏网格重建不仅显著改善了PIC方案的统计误差,而且降低了提供电场问题的复杂性。本文给出了电场插值的收敛性和守恒性的主要结果。此外,在偏移组合技术的框架下,提出了定制的稀疏网格重建,以提高PIC方法的效率。这些方法进行了数值评估,并与现有的pics方案进行了比较,这要归功于具有显着前景的三维计算的经典基准。
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来源期刊
CiteScore
2.70
自引率
5.30%
发文量
27
审稿时长
6-12 weeks
期刊介绍: M2AN publishes original research papers of high scientific quality in two areas: Mathematical Modelling, and Numerical Analysis. Mathematical Modelling comprises the development and study of a mathematical formulation of a problem. Numerical Analysis comprises the formulation and study of a numerical approximation or solution approach to a mathematically formulated problem. Papers should be of interest to researchers and practitioners that value both rigorous theoretical analysis and solid evidence of computational relevance.
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