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Higher-Order Particle Representation for Particle-in-Cell Simulations

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Published copy (DOI)

Author(s)

  • Dominic Brown
  • Matthew T. Bettencourt
  • Steven A. Wright
  • Satheesh Maheswaran
  • John P. Jones
  • Stephen Jarvis

Department/unit(s)

Publication details

JournalJournal of Computational Physics
DateAccepted/In press - 1 Mar 2021
DateE-pub ahead of print (current) - 8 Mar 2021
Volume435
Number of pages22
Early online date8/03/21
Original languageEnglish

Abstract

In this paper we present an alternative approach to the representation of simulation particles for unstructured electro- static and electromagnetic PIC simulations. In our modified PIC algorithm we represent particles as having a smooth shape function limited by some specified finite radius, r0. A unique feature of our approach is the representation of this shape by surrounding simulation particles with a set of virtual particles with delta shape, with fixed offsets and weights derived from Gaussian quadrature rules and the value of r0. As the virtual particles are purely computational, they provide the additional benefit of increasing the arithmetic intensity of traditionally memory bound particle kernels. The modified algorithm is implemented within Sandia National Laboratories’ unstructured EMPIRE-PIC code, for electrostatic and electromagnetic simulations, using periodic boundary conditions. We show results for a representative set of benchmark problems, including electron orbit, a transverse electromagnetic wave propagating through a plasma, numerical heating, and a plasma slab expansion. Good error reduction across all of the chosen problems is achieved as the particles are made progressively smoother, with the optimal particle radius appearing to be problem-dependent.

Bibliographical note

© 2021 Published by Elsevier Inc. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.

    Research areas

  • Particle-in-Cell, High-order, Unstructured, Particle Representation, Shape Function

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