A deterministic verification strategy for electrostatic particle-in-cell algorithms in arbitrary spatial dimensions using the method of manufactured solutions

• Published in: Journal of computational physics Vol. 448; no. C; p. 110751
• Main Authors: Tranquilli, Paul, Ricketson, Lee, Chacón, Luis
• Format: Journal Article
• Language: English
• Published: Cambridge Elsevier Inc 01.01.2022; Elsevier Science Ltd; Elsevier
• Subjects: Algorithms; Computational physics; Convergence; Distribution functions; Finite element method; Kinetic plasmas; Method of manufactured solutions; Particle in cell technique; Particle-in-cell methods; Plasmas (physics); Verification; Weight reduction; Method of manufactured solutions; Particle-in-cell methods; Kinetic plasmas
• ISSN: 0021-9991; 1090-2716; 1090-2716
• DOI: 10.1016/j.jcp.2021.110751

•The proposed verification scheme requires only errors on grid quantities.•Rigorously prove convergence of the Vlasov solution is implied by convergence of the electric field or charge distribution.•Confirm by analysis and experiment differing convergence rates, with respect to particle number, of various grid quantities. As simulations of kinetic plasmas continue to increase in scope and complexity, a rigorous and straightforward method for verifying particle-in-cell (PIC) implementations is necessary to ensure their correctness. In this paper, we present a deterministic method for the rigorous verification of multidimensional, multispecies, electrostatic particle-in-cell codes based on the method of manufactured solutions. Specifically, we prove that rigorous verification is possible through the exclusive examination of errors of grid quantities (i.e., moments and/or fields), allowing for a very light-weight and non-intrusive implementation in existing PIC codes. This is a marked improvement over earlier PIC verification approaches (only demonstrated with one species in 1D-1V), which rely on the comparison of cumulative distribution functions, and are invasive on the code base, introduce additional stochastic noise, are significantly more computationally expensive, and lack rigorous convergence properties. Interestingly, we show that different grid quantities feature different rates of convergence with the number of particles and mesh size, impacting the verification process. These theoretical results are confirmed numerically with a multi-species 2D-2V particle-in-cell code, including the ability of the method to detect order reduction due to an incorrect implementation.