Numerical heating in particle-in-cell simulations with Monte Carlo binary collisions
Abstract
The binary Monte Carlo (MC) collision algorithm is a standard and robust method to include binary Coulomb collision effects in particle-in-cell (PIC) simulations of plasmas. Here we show that the coupling between PIC and MC algorithms can give rise to (nonphysical) numerical heating of the system that significantly exceeds that observed when these algorithms operate independently. We argue that this deleterious effect results from an inconsistency between the particle motion associated with MC collisions and the work performed by the collective electromagnetic field on the PIC grid. This inconsistency manifests as the (artificial) stochastic production of electromagnetic energy, which ultimately heats the plasma particles. Here, the MC-induced numerical heating can significantly impact the evolution of the simulated system for long simulation times (≳103 collision periods, for typical numerical parameters). We describe the source of the MC-induced numerical heating analytically and discuss strategies to minimize it.
- Authors:
-
[1];
[1]
- SLAC National Accelerator Lab., Menlo Park, CA (United States)
- Univ. of California, Los Angeles, CA (United States)
- Publication Date:
- Research Org.:
- SLAC National Accelerator Lab., Menlo Park, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Fusion Energy Sciences (FES); U.S. DOE Early Career Research Program; National Science Foundation (NSF)
- OSTI Identifier:
- 1768238
- Grant/Contract Number:
- AC02-76SF00515; LFR-17-449059; SC0019010; NA0003842; FWP 100331; FWP 100237; FWP 100182; ACI-1339893
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Physical Review E
- Additional Journal Information:
- Journal Volume: 103; Journal Issue: 1; Journal ID: ISSN 2470-0045
- Publisher:
- American Physical Society (APS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 70 PLASMA PHYSICS AND FUSION TECHNOLOGY; High-energy-density plasmas; Plasma transport; Monte Carlo methods; Particle-in-cell methods
Citation Formats
Alves, E. P., Mori, W. B., and Fiuza, F. Numerical heating in particle-in-cell simulations with Monte Carlo binary collisions. United States: N. p., 2021.
Web. doi:10.1103/physreve.103.013306.
Alves, E. P., Mori, W. B., & Fiuza, F. Numerical heating in particle-in-cell simulations with Monte Carlo binary collisions. United States. https://doi.org/10.1103/physreve.103.013306
Alves, E. P., Mori, W. B., and Fiuza, F. Mon .
"Numerical heating in particle-in-cell simulations with Monte Carlo binary collisions". United States. https://doi.org/10.1103/physreve.103.013306. https://www.osti.gov/servlets/purl/1768238.
@article{osti_1768238,
title = {Numerical heating in particle-in-cell simulations with Monte Carlo binary collisions},
author = {Alves, E. P. and Mori, W. B. and Fiuza, F.},
abstractNote = {The binary Monte Carlo (MC) collision algorithm is a standard and robust method to include binary Coulomb collision effects in particle-in-cell (PIC) simulations of plasmas. Here we show that the coupling between PIC and MC algorithms can give rise to (nonphysical) numerical heating of the system that significantly exceeds that observed when these algorithms operate independently. We argue that this deleterious effect results from an inconsistency between the particle motion associated with MC collisions and the work performed by the collective electromagnetic field on the PIC grid. This inconsistency manifests as the (artificial) stochastic production of electromagnetic energy, which ultimately heats the plasma particles. Here, the MC-induced numerical heating can significantly impact the evolution of the simulated system for long simulation times (≳103 collision periods, for typical numerical parameters). We describe the source of the MC-induced numerical heating analytically and discuss strategies to minimize it.},
doi = {10.1103/physreve.103.013306},
journal = {Physical Review E},
number = 1,
volume = 103,
place = {United States},
year = {Mon Jan 11 00:00:00 EST 2021},
month = {Mon Jan 11 00:00:00 EST 2021}
}
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