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Implementation of neutralizing fields for particle–particle simulations using like charges

Published online by Cambridge University Press:  07 July 2021

Yinjian Zhao Open the ORCID record for Yinjian Zhao [Opens in a new window] ,
Chen Cui Open the ORCID record for Chen Cui [Opens in a new window] ,
Yanan Zhang  and
Yuan Hu Open the ORCID record for Yuan Hu [Opens in a new window]
Yinjian Zhao
Affiliation:
University of Southern California, Los Angeles, CA90007, USA Lawrence Berkeley National Laboratory, Berkeley, CA94720, USA
Chen Cui
Affiliation:
University of Southern California, Los Angeles, CA90007, USA
Yanan Zhang
Affiliation:
Arizona State University, Tempe, AZ85281, USA
Yuan Hu*
Affiliation:
State Key Laboratory of High Temperature Gas Dynamics, Institute of Mechanics, Chinese Academy of Sciences, Beijing100190, PR China
*
†Email address for correspondence: [email protected]
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Abstract

The particle–particle (PP) model has a growing number of applications in plasma simulations, because of its high accuracy of solving Coulomb collisions. One of the main issues restricting the practical use of the PP model is its large computational cost, which is now becoming acceptable thanks to state-of-art parallel computing techniques. Another issue is the singularity that occurs when two particles are too close. The most effective approach of avoiding the singularity would be to simulate particles with only like charges plus a neutralizing field, such that the short-range collisions are equivalent to those of using unlike charges. In this paper, we introduce a way of adding the neutralizing field by using the analytical solution of the electric field in the domain filled with uniformly distributed charges, for applications with homogeneous and quasi-neutral plasmas under a reflective boundary condition. Two most common Cartesian domain geometries, cubic and spherical, are considered. The model is verified by comparing simulation results with an analytical solution of an electron–ion temperature relaxation problem, and a corresponding simulation using unlike charges. In addition, it is found that a PP simulation using like charges can achieve a significant speed-up of 100 compared with a corresponding simulation using unlike charges, due to the capability of using larger time steps while maintaining the same energy conservation.

Keywords

plasma simulationstrongly coupled plasmasplasma dynamics
Type
Research Article
Information
Journal of Plasma Physics , Volume 87 , Issue 3 , June 2021 , 905870315
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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References

REFERENCES

Benedict, L.X., Glosli, J.N., Richards, D.F., Streitz, F.H., Hau-Riege, S.P., London, R.A., Graziani, F.R., Murillo, M.S. & Benage, J.F. 2009 Molecular dynamics simulations of electron-ion temperature equilibration in an $\boldsymbol {S}\boldsymbol {f}_6$ plasma. Phys. Rev. Lett. 102, 205004.CrossRefGoogle Scholar
Benedict, L.X., Surh, M.P., Castor, J.I., Khairallah, S.A., Whitley, H.D., Richards, D.F., Glosli, J.N., Murillo, M.S., Scullard, C.R., Grabowski, P.E., et al. 2012 Molecular dynamics simulations and generalized Lenard–Balescu calculations of electron-ion temperature equilibration in plasmas. Phys. Rev. E 86, 046406.CrossRefGoogle ScholarPubMed
Birdsall, C.K. & Langdon, A.B. 1991 Plasma physics via computer simulation. IOP.CrossRefGoogle Scholar
Bobrov, A.A., Bronin, S.Y., Klyarfeld, A.B., Zelener, B.B. & Zelener, B.V. 2020 Molecular dynamics calculation of thermal conductivity and shear viscosity in two-component fully ionized strongly coupled plasma. Phys. Plasmas 27 (1), 010701.CrossRefGoogle Scholar
Bobrov, A.A., Bronin, S.Y., Zelener, B.B., Zelener, B.V., Manykin, E.A. & Khikhlukha, D.R. 2011 Collisional recombination coefficient in an ultracold plasma: calculation by the molecular dynamics method. J. Exp. Theor. Phys. 112 (3), 527.CrossRefGoogle Scholar
Bobrov, A.A., Bunkov, A.M., Bronin, S.Y., Klyarfeld, A.B., Zelener, B.B. & Zelener, B.V. 2019 Conductivity and diffusion coefficients in fully ionized strongly coupled plasma: method of molecular dynamics. Phys. Plasmas 26 (8), 082102.CrossRefGoogle Scholar
Bobrov, A.A., Vorob'ev, V.S. & Zelener, B.V. 2018 Transfer coefficients in ultracold strongly coupled plasma. Phys. Plasmas 25 (3), 033513.CrossRefGoogle Scholar
Cohen, R.S., Spitzer, L. & Routly, P.M. 1950 The electrical conductivity of an ionized gas. Phys. Rev. 80, 230–238.CrossRefGoogle Scholar
Daligault, J. & Dimonte, G. 2009 Correlation effects on the temperature-relaxation rates in dense plasmas. Phys. Rev. E 79, 056403.CrossRefGoogle ScholarPubMed
Dimonte, G. & Daligault, J. 2008 Molecular-dynamics simulations of electron-ion temperature relaxation in a classical coulomb plasma. Phys. Rev. Lett. 101 (13), 135001.CrossRefGoogle Scholar
Eberly, D.H. 2006 3D Game Engine Design: A Practical Approach to Real-Time Computer Graphics. Morgan Kaufmann.CrossRefGoogle Scholar
Faussurier, G. & Blancard, C. 2017 Fast temperature relaxation model in dense plasmas. Phys. Plasmas 24 (1), 012705.CrossRefGoogle Scholar
Glosli, J.N., Graziani, F.R., More, R.M., Murillo, M.S., Streitz, F.H., Surh, M.P., Benedict, L.X., Hau-Riege, S., Langdon, A.B. & London, R.A. 2008 Molecular dynamics simulations of temperature equilibration in dense hydrogen. Phys. Rev. E 78, 025401.CrossRefGoogle ScholarPubMed
Hockney, R.W. & Eastwood, J.W. 1981 Computer Simulation Using Particles. McGraw-Hill.Google Scholar
Landau, L. 1937 Sov. Phys. JETP 7, 203.Google Scholar
Liu, Y., Hu, C. & Zhao, C. 2011 Efficient parallel implementation of Ewald summation in molecular dynamics simulations on multi-core platforms. Comput. Phys. Commun. 182 (5), 1111–1119.CrossRefGoogle Scholar
Nanbu, K. 1997 Theory of cumulative small-angle collisions in plasmas. Phys. Rev. E 55 (4), 4642–4652.CrossRefGoogle Scholar
Ramazanov, T.S. & Kodanova, S.K. 2001 Coulomb logarithm of a nonideal plasma. Phys. Plasmas 8 (11), 5049–5050.CrossRefGoogle Scholar
Spitzer, L. 1962 Physics of Fully Ionized Gases. John Wiley & Sons.Google Scholar
Thompson, W.B. & Hubbard, J. 1960 Long-range forces and the diffusion coefficients of a plasma. Rev. Mod. Phys. 32, 714–718.CrossRefGoogle Scholar
Verlet, L. 1967 Computer “experiments” on classical fluids. I. Thermodynamical properties of Lennard–Jones molecules. Phys. Rev. 159, 98–103.CrossRefGoogle Scholar
Zhao, Y. 2017 Investigation of effective impact parameters in electron–ion temperature relaxation via particle–particle coulombic molecular dynamics. Phys. Lett. A 381 (35), 2944–2948.CrossRefGoogle Scholar
Zhao, Y. 2018 a A binary collision monte carlo model for electron–ion temperature relaxation. Phys. Plasmas 25 (3), 032707.CrossRefGoogle Scholar
Zhao, Y. 2018 b A binary collision monte carlo model for temperature relaxation in multicomponent plasmas. AIP Adv. 8 (7), 075016.CrossRefGoogle Scholar
Zhao, Y. 2018 c Three-dimensional particle–particle simulations: dependence of relaxation time on plasma parameter. Phys. Plasmas 25 (5), 052112.CrossRefGoogle Scholar
Zhao, Y., Lehe, R., Myers, A., Thévenet, M., Huebl, A., Schroeder, C.B. & Vay, J.-L. 2020 Modeling of emittance growth due to coulomb collisions in plasma-based accelerators. Phys. Plasmas 27 (11), 113105.CrossRefGoogle Scholar
Zhao, Y. & Wang, J. 2019 A particle–particle simulation model for droplet acceleration in colloid thrusters. In The 36th International Electric Propulsion Conference, IEPC-2019-526, University of Vienna.Google Scholar
Zhao, Y., Wang, J. & Usui, H. 2018 Simulations of ion thruster beam neutralization using a particle–particle model. 34 (5), 1109–1115.Google Scholar