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Journal articles on the topic 'Particle-in-cell simulation'

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Author: Grafiati

Published: 4 June 2021

Last updated: 21 February 2023

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1

Jahangir, Alam S. M., Guo Qing Hu, and Ling Ke Yu. "Simulation of Red Particles in Blood Cell." Applied Mechanics and Materials 477-478 (December 2013): 330–34. http://dx.doi.org/10.4028/www.scientific.net/amm.477-478.330.

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Red blood cell (RBC) particle detection and counting with characteristics in blood cell systems has been done by computer simulation. A simulation region, including plasma, red blood cells (RBCs) and platelets, was modeled by an assembly of discrete particles. The proposed method has detected the red particle from blood cell systems through different simulations of MATLAB and GAMBIT & FLUENT. After the detection, the number of red particles in a sampled cell has been counted and the characteristics about the red particles for analyzing the Birth-Death growth of each red particle have been found.

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2

Friedman, A., S. E. Parker, S. L. Ray, and C. K. Birdsall. "Multi-scale particle-in-cell plasma simulation." Journal of Computational Physics 91, no. 1 (November 1990): 252. http://dx.doi.org/10.1016/0021-9991(90)90027-x.

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3

Friedman, A., S. E. Parker, S. L. Ray, and C. K. Birdsall. "Multi-scale particle-in-cell plasma simulation." Journal of Computational Physics 96, no. 1 (September 1991): 54–70. http://dx.doi.org/10.1016/0021-9991(91)90265-m.

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4

Langdon, A. Bruce. "Evolution of Particle-in-Cell Plasma Simulation." IEEE Transactions on Plasma Science 42, no. 5 (May 2014): 1317–20. http://dx.doi.org/10.1109/tps.2014.2314615.

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5

Peratt, A. L., W. Peter, and C. M. Snell. "3-Dimensional Particle-in-Cell Simulations of Spiral Galaxies." Symposium - International Astronomical Union 140 (1990): 143–50. http://dx.doi.org/10.1017/s007418090018979x.

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The advent of 3-dimensional, electromagnetic, and fully relativistic particle simulations allows a detailed study of a magnetized plasma galaxy model. When two such models are simulated, an interaction yielding results resembling observational data from double radio sources, including the emission of synchrotron radiation, are obtained. Simulation derived morphologies, radiation intensities, frequency spectra and isophote patterns are produced by the model which can be directly compared to observational data. Long time simulation runs (~109 years) show the evolution of barred spiral galaxies with large scale bisymmetric magnetic field distributions having 100μG field strengths.

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6

Konior, Wojciech. "Particle-In-Cell Electrostatic Numerical Algorithm." Transactions on Aerospace Research 2017, no. 3 (September 1, 2017): 24–45. http://dx.doi.org/10.2478/tar-2017-0020.

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Abstract Existing global models of interaction between the solar wind (SW) and the local interstellar medium (LISM) describe the heliosphere that arises as a result of this interaction. There is a strong motivation to develop a kinetic model using the Particle-in-Cell (PIC) method to describe phenomena which appear in the heliosphere. This is however a long term scientific goal. This paper describes an electrostatic Particle-in-Cell numerical model developed in the Institute of Aviation in Warsaw, which includes mechanical and charge exchange collisions between particles in the probabilistic manner using Direct Simulation Monte Carlo method. This is the first step into developing simulations of the heliosphere incorporating kinetic effects in collisionless plasmas. In this paper we focus only on presenting the work, which have been done on the numerical PIC algorithm.

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7

Liu Dagang, 刘大刚, 周俊 Zhou Jun, and 杨超 Yang Chao. "Electromagnetic field algorithms in particle-in-cell simulation." High Power Laser and Particle Beams 22, no. 6 (2010): 1306–10. http://dx.doi.org/10.3788/hplpb20102206.1306.

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8

Somu, Pradeep, and R. Moulick. "Particle-in-Cell simulation of an Ion Gun." Journal of Physics: Conference Series 1531 (May 2020): 012009. http://dx.doi.org/10.1088/1742-6596/1531/1/012009.

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9

Liu, Hui, Boying Wu, Daren Yu, Yong Cao, and Ping Duan. "Particle-in-cell simulation of a Hall thruster." Journal of Physics D: Applied Physics 43, no. 16 (April 8, 2010): 165202. http://dx.doi.org/10.1088/0022-3727/43/16/165202.

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10

Bai-Lin Qin and P. D. Pedrow. "Particle-in-cell simulation of bipolar dc corona." IEEE Transactions on Dielectrics and Electrical Insulation 1, no. 6 (1994): 1104–18. http://dx.doi.org/10.1109/94.368652.

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11

Soria, C., F. Pontiga, and A. Castellanos. "Particle-in-Cell Simulation of Electrical Gas Discharges." Journal of Computational Physics 171, no. 1 (July 2001): 47–78. http://dx.doi.org/10.1006/jcph.2001.6763.

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12

Zenitani, Seiji, and Tsunehiko N. Kato. "Multiple Boris integrators for particle-in-cell simulation." Computer Physics Communications 247 (February 2020): 106954. http://dx.doi.org/10.1016/j.cpc.2019.106954.

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13

Taccogna, F., S. Longo, M. Capitelli, and R. Schneider. "Particle-in-Cell Simulation of Stationary Plasma Thruster." Contributions to Plasma Physics 47, no. 8-9 (December 2007): 635–56. http://dx.doi.org/10.1002/ctpp.200710074.

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14

Chen, Yang, Junyi Cheng, and Scott E. Parker. "Evolution of the marker distribution in gyrokinetic δf particle-in-cell simulations." Physics of Plasmas 29, no. 7 (July 2022): 073901. http://dx.doi.org/10.1063/5.0097207.

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The evolution of the particle weight in a [Formula: see text] particle-in-cell simulation depends on the marker distribution that can evolve in a turbulent field due to turbulent diffusion. When Monte Carlo methods are used to implement the test particle collision operator, or when the particle motion is not strictly Hamiltonian in a collisionless simulation, the marker distribution will evolve along the particle trajectory and, in general, cannot be known exactly. A two-dimensional numerical marker distribution is proposed as an approximation. It is shown to be advantageous over other common methods for evaluating the marker distribution in long-time turbulence simulations. A generalized two-weight [Formula: see text]-method is proposed to mitigate the marker evolution problem.

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15

Ejiri, Haruki, Takashi Fujii, Akiko Kumada, and Kunihiko Hidaka. "Particle-In-Cell Simulation for Breakdown Phenomena in Vacuum." IEEJ Transactions on Fundamentals and Materials 140, no. 6 (June 1, 2020): 318–24. http://dx.doi.org/10.1541/ieejfms.140.318.

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16

Zenitani, Seiji, and Takayuki Umeda. "On the Boris solver in particle-in-cell simulation." Physics of Plasmas 25, no. 11 (November 2018): 112110. http://dx.doi.org/10.1063/1.5051077.

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17

Radmilović-Radjenović, M., J. K. Lee, F. Iza, and G. Y. Park. "Particle-in-cell simulation of gas breakdown in microgaps." Journal of Physics D: Applied Physics 38, no. 6 (March 4, 2005): 950–54. http://dx.doi.org/10.1088/0022-3727/38/6/027.

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18

Boytsov, A. Yu, and A. A. Bulychev. "Ef: Software for Nonrelativistic Beam Simulation by Particle-in-Cell Algorithm." EPJ Web of Conferences 177 (2018): 07002. http://dx.doi.org/10.1051/epjconf/201817707002.

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Understanding of particle dynamics is crucial in construction of electron guns, ion sources and other types of nonrelativistic beam devices. Apart from external guiding and focusing systems, a prominent role in evolution of such low-energy beams is played by particle-particle interaction. Numerical simulations taking into account these effects are typically accomplished by a well-known particle-in-cell method. In practice, for convenient work a simulation program should not only implement this method, but also support parallelization, provide integration with CAD systems and allow access to details of the simulation algorithm. To address the formulated requirements, development of a new open source code - Ef - has been started. It's current features and main functionality are presented. Comparison with several analytical models demonstrates good agreement between the numerical results and the theory. Further development plans are discussed.

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19

Qu, Ziyin, Minchen Li, Fernando De Goes, and Chenfanfu Jiang. "The power particle-in-cell method." ACM Transactions on Graphics 41, no. 4 (July 2022): 1–13. http://dx.doi.org/10.1145/3528223.3530066.

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This paper introduces a new weighting scheme for particle-grid transfers that generates hybrid Lagrangian/Eulerian fluid simulations with uniform particle distributions and precise volume control. At its core, our approach reformulates the construction of Power Particles [de Goes et al. 2015] by computing volume-constrained density kernels. We employ these optimized kernels as particle domains within the Generalized Interpolation Material Point method (GIMP) in order to incorporate Power Particles into the Particle-In-Cell framework, hence the name the Power Particle-In-Cell method. We address the construction of volume-constrained density kernels as a regularized optimal transportation problem and describe an iterative solver based on localized Gaussian convolutions that leads to a significant performance speedup compared to [de Goes et al. 2015]. We also present novel extensions for handling free surfaces and solid obstacles that bypass the need for cell clipping and ghost particles. We demonstrate the advantages of our transfer weights by improving hybrid schemes for fluid simulation such as the Fluid Implicit Particle (FLIP) method and the Affine Particle-In-Cell (APIC) method with volume preservation and robustness to varying particle-per-cell ratio, while retaining low numerical dissipation, conserving linear and angular momenta, and avoiding particle reseeding or post-process relaxations.

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20

NISHIDA, Kento, Xuehan GUO, Ritoku HORIUCHI, and Yasushi ONO. "Particle-In-Cell Simulation of Field-Reversed Configuration with Adaptive Particle Management." Plasma and Fusion Research 13 (May 22, 2018): 3401060. http://dx.doi.org/10.1585/pfr.13.3401060.

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21

Brown, Dominic A. S., Matthew T. Bettencourt, Steven A. Wright, Satheesh Maheswaran, John P. Jones, and Stephen A. Jarvis. "Higher-order particle representation for particle-in-cell simulations." Journal of Computational Physics 435 (June 2021): 110255. http://dx.doi.org/10.1016/j.jcp.2021.110255.

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22

Ashida, Yasumasa, Ikkoh Funaki, Hiroshi Yamakawa, Hideyuki Usui, Yoshihiro Kajimura, and Hirotsugu Kojima. "Two-Dimensional Particle-In-Cell Simulation of Magnetic Sails." Journal of Propulsion and Power 30, no. 1 (January 2014): 233–45. http://dx.doi.org/10.2514/1.b34692.

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23

Lönnroth, J. S., J. A. Heikkinen, K. M. Rantamäki, and S. J. Karttunen. "Particle-in-cell simulation of ion Bernstein wave excitation." Physics of Plasmas 9, no. 7 (July 2002): 2926–39. http://dx.doi.org/10.1063/1.1477451.

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24

Umeda, Takayuki, Maha Ashour-Abdalla, David Schriver, Robert L. Richard, and Ferdinand V. Coroniti. "Particle-in-cell simulation of Maxwellian ring velocity distribution." Journal of Geophysical Research: Space Physics 112, A4 (April 2007): n/a. http://dx.doi.org/10.1029/2006ja012124.

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25

Lüskow, Karl Felix, Patrick R. C. Neumann, Gunnar Bandelow, Julia Duras, Daniel Kahnfeld, Stefan Kemnitz, Paul Matthias, Konstantin Matyash, and Ralf Schneider. "Particle-in-cell simulation of the cathodic arc thruster." Physics of Plasmas 25, no. 1 (January 2018): 013508. http://dx.doi.org/10.1063/1.5012584.

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26

Jiang, W., T. Sakagami, K. Masugata, and K. Yatsui. "Particle‐in‐cell simulation of spherical plasma focus diode." Physics of Plasmas 2, no. 1 (January 1995): 325–31. http://dx.doi.org/10.1063/1.871109.

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27

Saito, S., S. Peter Gary, and Y. Narita. "Wavenumber spectrum of whistler turbulence: Particle-in-cell simulation." Physics of Plasmas 17, no. 12 (December 2010): 122316. http://dx.doi.org/10.1063/1.3526602.

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28

Kumar, Pawan, Stefano Markidis, Giovanni Lapenta, Karl Meerbergen, and Dirk Roose. "High Performance Solvers for Implicit Particle in Cell Simulation." Procedia Computer Science 18 (2013): 2251–58. http://dx.doi.org/10.1016/j.procs.2013.05.396.

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29

Bastrakov, S., R. Donchenko, A. Gonoskov, E. Efimenko, A. Malyshev, I. Meyerov, and I. Surmin. "Particle-in-cell plasma simulation on heterogeneous cluster systems." Journal of Computational Science 3, no. 6 (November 2012): 474–79. http://dx.doi.org/10.1016/j.jocs.2012.08.012.

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30

Joyce, Glenn, Martin Lampe, Steven P. Slinker, and Wallace M. Manheimer. "Electrostatic Particle-in-Cell Simulation Technique for Quasineutral Plasma." Journal of Computational Physics 138, no. 2 (December 1997): 540–62. http://dx.doi.org/10.1006/jcph.1997.5833.

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31

Lee, W. W., T. G. Jenkins, and S. Ethier. "A generalized weight-based particle-in-cell simulation scheme." Computer Physics Communications 182, no. 3 (March 2011): 564–69. http://dx.doi.org/10.1016/j.cpc.2010.10.013.

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32

Matyash, K., R. Schneider, F. Taccogna, A. Hatayama, S. Longo, M. Capitelli, D. Tskhakaya, and F. X. Bronold. "Particle in Cell Simulation of Low Temperature Laboratory Plasmas." Contributions to Plasma Physics 47, no. 8-9 (December 2007): 595–634. http://dx.doi.org/10.1002/ctpp.200710073.

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33

Qin Feng, 秦风, 常安碧 Chang Anbi, 丁恩燕 Ding Enyan, and 罗敏 Luo Min. "Particle-in-cell simulation of pseudospark switch based on particle-in-cell plus Monte-Carlo collision method." High Power Laser and Particle Beams 22, no. 2 (2010): 447–51. http://dx.doi.org/10.3788/hplpb20102202.0447.

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34

Joyce, Glenn, Jonathan Krall, and Steven Slinker. "ELBA (electron beams in accelerators) particle simulation code." Laser and Particle Beams 12, no. 2 (June 1994): 273–82. http://dx.doi.org/10.1017/s0263034600007734.

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ELBA is a three-dimensional, particle-in-cell, simulation code that has been developed to study the propagation and transport of relativistic charged particle beams. The code is particularly suited to the simulation of relativistic electron beams propagating through collisionless or slightly collisional plasmas or through external electric or magnetic fields. Particle motion is followed via a coordinate “window” in the laboratory frame that moves at the speed of light. This scheme allows us to model only the immediate vicinity of the beam. Because no information can move in the forward direction in these coordinates, particle and field data can be handled in a simple way that allows for very large scale simulations. A mapping scheme has been implemented that, with corrections to Maxwell's equations, allows the inclusion of bends in the simulation system.

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35

Yu, Jinqing, Xiaolin Jin, Weimin Zhou, Bin Li, and Yuqiu Gu. "High-Order Interpolation Algorithms for Charge Conservation in Particle-in-Cell Simulations." Communications in Computational Physics 13, no. 4 (April 2013): 1134–50. http://dx.doi.org/10.4208/cicp.290811.050412a.

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AbstractHigh-order interpolation algorithms for charge conservation in Particle-in-Cell (PIC) simulations are presented. The methods are valid for the case that a particle trajectory is a zigzag line. The second-order and third-order algorithms which can be applied to any even-order and odd-order are discussed in this paper, respectively. Several test simulations are performed to demonstrate their validity in two-dimensional PIC code. Compared with the simulation results of one-order, high-order algorithms have advantages in computation precision and enlarging the grid scales which reduces the CPU time.

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36

Werner, Gregory R., Thomas G. Jenkins, Andrew M. Chap, and John R. Cary. "Speeding up simulations by slowing down particles: Speed-limited particle-in-cell simulation." Physics of Plasmas 25, no. 12 (December 2018): 123512. http://dx.doi.org/10.1063/1.5061683.

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37

Li, Xiaofei, Dong Li, Haikun Yue, Zhe Zhang, Kaifeng Liu, Mingwu Fan, and Dezhi Chen. "A High Precision Particle-Moving Algorithm for Particle-in-Cell Simulation of Plasma." IEEE Transactions on Magnetics 51, no. 3 (March 2015): 1–4. http://dx.doi.org/10.1109/tmag.2014.2361607.

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38

Qiang, Ji, and Xiaoye Li. "Particle-field decomposition and domain decomposition in parallel particle-in-cell beam dynamics simulation." Computer Physics Communications 181, no. 12 (December 2010): 2024–34. http://dx.doi.org/10.1016/j.cpc.2010.08.021.

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39

An, Xiangyan, Min Chen, Zheng-Ming Sheng, and Jie Zhang. "Modeling of Bound Electron Effects in Particle-in-Cell Simulation." Communications in Computational Physics 32, no. 2 (June 2022): 583–94. http://dx.doi.org/10.4208/cicp.oa-2021-0258.

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40

Yang Chao, 杨超, 刘大刚 Liu Dagang, 周俊 Zhou Jun, 夏蒙重 Xia Mengzhong, 杨宇鹏 Yang Yupeng, and 徐旭光 Xu Xuguang. "Implementation of anode grid model in particle-in-cell simulation." High Power Laser and Particle Beams 22, no. 9 (2010): 2097–102. http://dx.doi.org/10.3788/hplpb20102209.2097.

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41

Nikishkin, I. I., and R. I. Kholodov. "Particle-in-cell Simulation of Processes in the Electron Gas." Journal of Nano- and Electronic Physics 13, no. 5 (2021): 05022–1. http://dx.doi.org/10.21272/jnep.13(5).05022.

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42

Soria-Hoyo, C., F. Pontiga, and A. Castellanos. "Particle-in-cell simulation of Trichel pulses in pure oxygen." Journal of Physics D: Applied Physics 40, no. 15 (July 13, 2007): 4552–60. http://dx.doi.org/10.1088/0022-3727/40/15/027.

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43

Kempf, A., P. Kilian, and F. Spanier. "Energy loss in intergalactic pair beams: Particle-in-cell simulation." Astronomy & Astrophysics 585 (January 2016): A132. http://dx.doi.org/10.1051/0004-6361/201527521.

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44

Lapenta, G., F. Iinoya, and J. U. Brackbill. "Particle-in-cell simulation of glow discharges in complex geometries." IEEE Transactions on Plasma Science 23, no. 4 (1995): 769–79. http://dx.doi.org/10.1109/27.467999.

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45

Wang, Q. Y., and P. D. Pedrow. "Particle-in-cell simulation of a radioactive probe in wind." IEEE Transactions on Electrical Insulation 27, no. 2 (April 1992): 342–51. http://dx.doi.org/10.1109/14.135605.

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46

Parker, Scott E. "Nearest-Grid-Point Interpolation in Gyrokinetic Particle-in-Cell Simulation." Journal of Computational Physics 178, no. 2 (May 2002): 520–32. http://dx.doi.org/10.1006/jcph.2002.7039.

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47

Usui, H., H. Matsumoto, F. Yamashita, A. Yamamoto, and Y. Omura. "Antenna analysis in magnetized plasma via particle-in-cell simulation." Advances in Space Research 34, no. 11 (January 2004): 2433–36. http://dx.doi.org/10.1016/j.asr.2003.08.073.

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48

Yu, Chunkai, Zhongwei Yang, Xinliang Gao, Quanming Lu, and Jian Zheng. "Electron Acceleration by Moderate-Mach-number Low-β Shocks: Particle-in-Cell Simulations." Astrophysical Journal 930, no. 2 (May 1, 2022): 155. http://dx.doi.org/10.3847/1538-4357/ac67df.

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Abstract Particle acceleration is ubiquitous at shock waves, occurring on scales ranging from supernova remnants in the universe to coronal-mass-ejection-driven shocks and planetary bow shocks in the heliosphere. The most promising mechanism responsible for the almost universally observed power-law spectra is diffusive shock acceleration (DSA). However, how electrons are preaccelerated by different shocks to the energy required by the DSA theory is still unclear. In this paper, we perform two-dimensional particle-in-cell plasma simulations to investigate how the magnetic field orientations, with respect to simulation planes, affect electron preacceleration in moderate-Mach-number low- β shocks. Simulation results show that instabilities can be different as the simulation planes capture different trajectories of particles. For magnetic fields perpendicular to the simulation plane, electron cyclotron drift instability dominates in the foot. Electrons can be trapped by the electrostatic wave and undergo shock-surfing acceleration. For magnetic fields lying in the simulation plane, whistler waves produced by modified two-stream instability dominate in the foot and scatter the electrons. In both cases, electrons undergo multistage acceleration in the foot, shock surface, and immediate downstream, during which process shock-surfing acceleration takes place as part of the preacceleration mechanism in moderate-Mach-number quasi-perpendicular shocks.

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49

Mizuno, Yusuke, Shun Takahashi, Kota Fukuda, and Shigeru Obayashi. "Direct Numerical Simulation of Gas–Particle Flows with Particle–Wall Collisions Using the Immersed Boundary Method." Applied Sciences 8, no. 12 (November 26, 2018): 2387. http://dx.doi.org/10.3390/app8122387.

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We investigated particulate flows by coupling simulations of the three-dimensional incompressible Navier–Stokes equation with the immersed boundary method (IBM). The results obtained from the two-way coupled simulation were compared with those of the one-way simulation, which is generally applied for clarifying the particle kinematics in industry. In the present flow simulation, the IBM was solved using a ghost–cell approach and the particles and walls were defined by a level set function. Using proposed algorithms, particle–particle and particle–wall collisions were implemented simply; the subsequent coupling simulations were conducted stably. Additionally, the wake structures of the moving, colliding and rebounding particles were comprehensively compared with previous numerical and experimental results. In simulations of 50, 100, 200 and 500 particles, particle–wall collisions were more frequent in the one–way scheme than in the two-way scheme. This difference was linked to differences in losses in energy and momentum.

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50

Li, T. M., C. Li, W. J. Ding, and P. F. Chen. "Particle-in-cell Simulation of ³He Enrichment in Solar Energetic Particle Events." Astrophysical Journal 922, no. 1 (November 1, 2021): 50. http://dx.doi.org/10.3847/1538-4357/ac2a40.

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Abstract 3He enrichment is one distinctive feature of impulsive solar energetic particle events. This study is designed to investigate the process of plasma wave–particle resonance, which plays a key role in selectively accelerating heavy ions. We apply a 1.5 dimensional particle-in-cell simulation to model the electron-beam–plasma interaction that generates electron and ion cyclotron waves, namely proton and 4He cyclotron waves, whose dispersions are dependent on the magnetization parameter α = ω pe/Ωce and the temperature ratio τ = T e /T p . The background particles, e.g., 3He and 4He, resonate with the excited cyclotron waves and experience selective heating or acceleration. Specifically, the resonant modes of 3He ions lead to a more effective acceleration rate compared to those of the 4He ions. The simulation results provide a potential solution for understanding the abundance of heavy ions in the solar wind.

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