Relevant bibliographies by topics / Particle-in-cell simulation

Academic literature on the topic 'Particle-in-cell simulation'

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Published: 4 June 2021
Last updated: 21 February 2023

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

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Particle-in-cell simulation"

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Przebinda, Viktor. "Vertical optimization of particle in cell simulation." Diss., Connect to online resource, 2005. http://wwwlib.umi.com/cr/colorado/fullcit?p1425790.

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Fox, Justin M. 1981. "Parallelization of particle-in-cell simulation modeling Hall-effect thrusters." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/28905.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2005.
Includes bibliographical references (p. 136-139).
MIT's fully kinetic particle-in-cell Hall thruster simulation is adapted for use on parallel clusters of computers. Significant computational savings are thus realized with a predicted linear speed up efficiency for certain large-scale simulations. The MIT PIC code is further enhanced and updated with the accuracy of the potential solver, in particular, investigated in detail. With parallelization complete, the simulation is used for two novel investigations. The first examines the effect of the Hall parameter profile on simulation results. It is concluded that a constant Hall parameter throughout the entire simulation region does not fully capture the correct physics. In fact, it is found empirically that a Hall parameter structure which is instead peaked in the region of the acceleration chamber obtains much better agreement with experiment. These changes are incorporated into the evolving MIT PIC simulation. The second investigation involves the simulation of a high power, central-cathode thruster currently under development. This thruster presents a unique opportunity to study the efficiency of parallelization on a large scale, high power thruster. Through use of this thruster, we also gain the ability to explicitly simulate the cathode since the thruster was designed with an axial cathode configuration.
by Justin M. Fox.
S.M.
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Van, der Straaten Trudy. "A particle-in-cell simulation of a DC magnetron discharge." Thesis, The University of Sydney, 1996. https://hdl.handle.net/2123/27510.

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Magnetron discharges have applications in the materials processing industry for the fabrication of thin films via sputter deposition. Despite their wide use, the underlying physics of the discharge is not well understood. In particular, the transport mechanisms that enable the electrons to migrate across magnetic field lines at low pressures have not been conclusively established. The research reported in this thesis is directed toward understanding this problem further.
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Beidler, Penh Koetwongjun 1974. "Two dimensional particle-in-cell simulation model for Hall type thrusters." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/9726.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1998.
Includes bibliographical references (p. 79-80).
In this master's thesis, a two-dimensional model of a Hall type thruster, was developed, to include secondary electron emission at the wall, ion recombination at the wall, diffuse reflection for neutrals bouncing off of the wall, wall potential calculation based on the collected wall charge and a steady state non-uniform magnetic field found in Hall thruster configurations. The model used a non-collisional, two dimensional in regular space and three dimensional in phase space, particle-in-cell (PIC) formulation for simulation of the plasma, while a separate model accounted for particle collisions, using Argon-electron elastic, excitation and ionization cross-sections. The collision model used an electron-neutral collision frequency on the same order as the electron plasma frequency, which made the neutral density to be on the order of 1025m- 3 Such a large neutral density implied that ion-neutral interactions, typically neglected in Hall thrusters, must also be taken into account. However, in this simulation they were neglected. Proceeding forward, the simulation size was 50x20 Debye lengths. Cell size was half of the plasma Debye length, in both dimensions. Time step was based on the condition that the electron gyroradius be ten times the Debye length, for a given electron temperature of 10 eV and maximum magnetic field of 0.8 Tesla, which made the electron density to be on the order of 10-2 0m - 3 . Neutral particle injection rate assumed a particle temperature of 1000K. Electron injection rate from the cathode equaled the electron collection rate at the anode. Ion and neutral mass were set to 1000 times that of the electron mass, in an attempt to accelerate plasma phenomena. Simulation of the model proceeded for 50000 iterations or 7.11 x 10- 9 seconds, which was equivalent to three ion passes through the simulation. Results analysis consisted of studying simulation output at different points in time. It was concluded that the simulation here does not simulate an actual Hall thruster, but introduces some computer models for it.
by Penh Koetwongjun Beidler.
S.M.
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Chae, Gyoo-Soo. "Numerical Simulation of Ion Waves in Dusty Plasmas." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/29165.

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There has been a great deal of interest in investigating numerous unique types of electrostatic and electromagnetic waves and instabilities in dusty plasmas. Dusty plasmas are characterized by the presence of micrometer or submicrometer size dust grains immersed in a partially or fully ionized plasma. In this study, a two-dimensional numerical model is presented to study waves and instabilities in dusty plasmas. Fundamental differences exist between dusty plasmas and electron-ion plasmas because of dust charging processes. Therefore, a primary goal of this study is to consider the unique effects of dust charging on collective effects in dusty plasmas. The background plasma electrons and ions here are treated as two interpenerating fluids whose densities vary by dust charging. The dust is treated with a Particle-In-Cell PIC model in which the dust charge varies with time according to the standard dust charging model. Fourier spectral methods with a predictor-corrector time advance are used to temporally evolve the background plasma electron and ion equations. The dust charge fluctuation mode and the damping of lower hybrid oscillations due to dust charging, as well as plasma instabilities associated with dust expansion into a magnetized background plasma are investigated using our numerical model. Also, an ion acoustic streaming instability in unmagnetized dusty plasmas due to dust charging is investigated. The numerical simulation results show good agreement with theoretical predictions and provide further insight into dust charging effects on wave modes and instabilities in dusty plasmas.
Ph. D.
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Pierru, Julien. "Development of a Parallel Electrostatic PIC Code for Modeling Electric Propulsion." Thesis, Virginia Tech, 2005. http://hdl.handle.net/10919/34597.

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This thesis presents the parallel version of Coliseum, the Air Force Research Laboratory plasma simulation framework. The parallel code was designed to run large simulations on the world fastest supercomputers as well as home mode clusters. Plasma simulations are extremely computationally intensive as they require tracking millions of particles and solving field equations over large domains. This new parallel version will allow Coliseum to run simulations of spacecraft-plasma interactions in domain large enough to reproduce space conditions. The parallel code ran on two of the world fastest supercomputers, the NASA JPL Cosmos supercomputer ranked 37th on the TOP500 list and Virginia Tech's System X, ranked 7th. DRACO, the Virginia Tech PIC module to Coliseum, was modified with parallel algorithms to create a full parallel PIC code. A parallel solver was added to DRACO. It uses a Gauss-Seidel method with SOR acceleration on a Red-Black checkerboard scheme. Timing results were obtained on JPL Cosmos supercomputer to determine the efficiency of the parallel code. Although the communication overhead limits the code's parallel efficiency, the speed up obtained greatly decreases the time required to run the simulations. A speed up of 51 was reached on 128 processors. The parallel code was also used to simulate the plume expansion of an ion thruster array composed of three NSTAR thrusters. Results showed that the multiple beams merge to form a single plume similar to the plume created by a single ion thruster.
Master of Science
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Vanderburgh, Richard N. "One-Dimensional Kinetic Particle-In-Cell Simulations of Various Plasma Distributions." Wright State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1610313011646245.

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Tran, Binh Phuoc. "Modeling of Ion Thruster Discharge Chamber Using 3D Particle-In-Cell Monte-Carlo-Collision Method." Thesis, Virginia Tech, 2005. http://hdl.handle.net/10919/33510.

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This thesis is aimed toward developing a method to simulate ion thruster discharge chambers in a full three dimensional environment and to study the effect of discharge chamber size on ion thruster performance. The study focuses solely on ring-cusped thrusters that make use of Xenon for propellant and discharge cathode assembly for mean of propellant ionization. Commercial software is used in both the setup and analysis phases. Numerical simulation is handled by 3D Particle-In-Cell Monte-Carlo-Collision method. Simulation results are analyzed and compared with other works. It is concluded that the simulation methodology is validated and can be used to simulate different cases. Therefore, different simulation cases of varying chamber sizes are done and the results are used to develop a performance curve. This plot suggests that the most efficient case is the 30 cm thruster. The result further validates the simulation process since the operating parameters used for all of the cases are taken from a 30 cm thruster experiment. One of the obvious applications for such a simulation process is to determine a set of the most efficient operating parameters for a certain size thruster before actual fabrication and laboratory testing.
Master of Science
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Jin, Hanbing. "Particle-in-Cell Simulation of Electromagnetic Pulse Generated by High-power Laser-target Interaction." Thesis, KTH, Fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231339.

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Tatomirescu, Emilian-Dragos. "Accélération laser-plasma à ultra haute intensité - modélisation numérique." Thesis, Bordeaux, 2019. http://www.theses.fr/2019BORD0013/document.

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Avec les dernières augmentations de l'intensité maximale de laser réalisable grâce à de courtes impulsions à haute puissance (gamme femtoseconde) un intérêt a surgi dans les sources de plasma laser potentiels. Les lasers sont utilisés en radiographie proton, allumage rapide, hadronthérapie, la production de radioisotopes et de laboratoire astrophysique. Au cours de l'interaction laser-cible, les ions sont accélérés par des processus physiques différents, en fonction de la zone de la cible. Tous ces mécanismes ont un point commun: les ions sont accélérés par des champs électriques intenses, qui se produisent en raison de la séparation de forte charge induite par l'interaction de l'impulsion laser avec la cible, directement ou indirectement. Deux principales sources distinctes pour le déplacement de charge peuvent être mis en évidence. Le premier est le gradient de charge provoquée par l'action directe de la force ponderomotive de laser sur les électrons dans la surface avant de la cible, qui est la prémisse pour le processus d'accélération des radiations de pression (RPA). Une deuxième source peut être identifiée comme provenant du rayonnement laser qui est transformée en énergie cinétique d'une population d'électrons relativistes chaud (~ quelques MeV). Les électrons chauds se déplacent et font recirculer à travers la cible et forment un nuage d'électrons relativistes à la sortie de la cible dans le vide. Ce nuage, qui se prolonge pour plusieurs longueurs de Debye, crée un champ électrique extrêmement intense longitudinal, la plupart du temps dirigé le long de la surface normale, ce qui, par conséquent, est la cause de l'accélération d'ions efficace, qui conduit à l'accélération cible normale gaine (TNSA) processus . Le mécanisme TNSA permet d'utiliser des géométries différentes cibles afin de parvenir à une meilleure focalisation des faisceaux de particules de l'ordre de plusieurs dizaines de microns, avec des densités d'énergie élevées. Les électrons chauds sont produits par l'irradiation d'une feuille solide avec une impulsion laser intense; ces électrons sont transportés à travers la cible, la formation d'un champ électrostatique fort, normal à la surface cible. Protons et les ions chargés positivement de la surface arrière de la cible sont accélérés par ce domaine jusqu'à ce que la charge de l'électron est compensée. La densité d'électrons chauds et la température dans le vide arrière dépendent des propriétés géométriques et de composition cibles tels que la courbure de la cible, les structures de mise au point d'impulsion et de microstructure pour l'accélération de protons améliorée. Au cours de ma première année, j'ai étudié les effets de la géométrie de la cible sur le proton et l'ion énergie et la distribution angulaire afin d'optimiser les faisceaux de particules laser accéléré au moyen de deux dimensions (2D) particule-in-cell (PIC) simulations de l'interaction de l'ultra-court impulsions laser avec plusieurs cibles microstructurées. Également au cours de cette année, je l'ai étudié la théorie derrière les modèles utilisés
With the latest increases in maximum laser intensity achievable through short pulses at high power (femtosecond range) an interest has arisen in potential laser plasma sources. Lasers are used in proton radiography, rapid ignition, hadrontherapy, production of radioisotopes and astrophysical laboratory. During the laser-target interaction, the ions are accelerated by different physical processes, depending on the area of ​​the target. All these mechanisms have one thing in common: the ions are accelerated by intense electric fields, which occur due to the separation of high charge induced by the interaction of the laser pulse with the target, directly or indirectly. Two main distinct sources for charge displacement can be identified. The first is the charge gradient caused by the direct action of the laser ponderomotive force on the electrons in the front surface of the target, which is the premise for the pressure ramping acceleration (RPA) process. A second source can be identified as coming from the laser radiation which is transformed into kinetic energy of a hot relativistic electron population (~ a few MeV). The hot electrons move and recirculate through the target and form a cloud of relativistic electrons at the exit of the target in a vacuum. This cloud, which extends for several lengths of Debye, creates an extremely intense longitudinal electric field, mostly directed along the normal surface, which is therefore the cause of effective ion acceleration, which leads to the normal target sheath acceleration (TNSA) process. The TNSA mechanism makes it possible to use different target geometries in order to obtain a better focusing of the beams of particles on the order of several tens of microns, with high energy densities. Hot electrons are produced by irradiating a solid sheet with an intense laser pulse; these electrons are transported through the target, forming a strong electrostatic field, normal to the target surface. Protons and positively charged ions from the back surface of the target are accelerated by this domain until the charge of the electron is compensated. The density of hot electrons and the temperature in the back vacuum depend on the target geometric and compositional properties such as target curvature, pulse and microstructure tuning structures for enhanced proton acceleration. In my first year I studied the effects of target geometry on the proton and energy ion and angular distribution in order to optimize the accelerated laser particle beams by means of two-dimensional (2D) particle -in-cell (PIC) simulations of the interaction of ultra-short laser pulses with several microstructured targets. Also during this year, I studied the theory behind the models used
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Books on the topic "Particle-in-cell simulation"

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Chen, Maozhang. Numerical simulation of Tollmien-Schlichting waves by use of a modified vortex particle-in-cell method. London: Imperial College of Science and Technology, Dept. of Aeronautics, 1985.

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Birch, Paul Colin. Particle-in-cell simulations of the lunar wake. [s.l.]: typescript, 2001.

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Rantamäki, Karin. Particle-in-cell simulations of the near-field of a lower hybrid grill. Espoo [Finland]: VTT Technical Research Centre of Finland, 2003.

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Dieckmann, Mark Eric. A survey of elementary plasma instabilities and ECH wave noise properties relevant to plasma sounding by means of particle in cell simulations. [s.l.]: typescript, 1999.

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Wang, Qingyuan. Particle-in-cell simulation of a radioactive potential probe in wind. 1991.

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Qin, Bai-Lin. High voltage dc bipolar corona via particle-in-cell simulation. 1993.

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Book chapters on the topic "Particle-in-cell simulation"

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Robinson, Alex P. L. "Particle-in-Cell and Hybrid Simulation." In Laser-Plasma Interactions and Applications, 397–408. Heidelberg: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00038-1_15.

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Cui, Wanzhao, Yun Li, Hongtai Zhang, and Jing Yang. "Electromagnetic Particle-in-Cell Method." In Simulation Method of Multipactor and Its Application in Satellite Microwave Components, 79–136. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003189794-3.

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Peratt, Anthony L. "Particle-in-Cell Simulation of Cosmic Plasma." In Physics of the Plasma Universe, 311–30. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7819-5_10.

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Peratt, Anthony L. "Particle-in-Cell Simulation of Cosmic Plasma." In Physics of the Plasma Universe, 285–303. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2780-9_8.

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Guidotti, Nicolas, Pedro Ceyrat, João Barreto, José Monteiro, Rodrigo Rodrigues, Ricardo Fonseca, Xavier Martorell, and Antonio J. Peña. "Particle-In-Cell Simulation Using Asynchronous Tasking." In Euro-Par 2021: Parallel Processing, 482–98. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85665-6_30.

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Dorobisz, Andrzej, Michał Kotwica, Jacek Niemiec, Oleh Kobzar, Artem Bohdan, and Kazimierz Wiatr. "The Impact of Particle Sorting on Particle-In-Cell Simulation Performance." In Parallel Processing and Applied Mathematics, 156–65. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78024-5_15.

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Zeng, Zhengzhong, Yuchang Qiu, and Edmund Kuffel. "A Particle-in-Cell Simulation of Plasma Opening Switch." In Gaseous Dielectrics IX, 155–60. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-0583-9_21.

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Ejiri, Haruki, Akiko Kumada, and Kunihiko Hidaka. "Particle-In-Cell Simulation for Breakdown Initiating Process in Vacuum." In Lecture Notes in Electrical Engineering, 347–58. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31680-8_36.

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Taccogna, Francesco, Savino Longo, Mario Capitelli, and Ralf Schneider. "Fully Kinetic Particle-in-Cell Simulation of a Hall Thruster." In Computational Science - ICCS 2004, 588–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25944-2_76.

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Larin, Anton, Sergey Bastrakov, Aleksei Bashinov, Evgeny Efimenko, Igor Surmin, Arkady Gonoskov, and Iosif Meyerov. "Load Balancing for Particle-in-Cell Plasma Simulation on Multicore Systems." In Parallel Processing and Applied Mathematics, 145–55. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78024-5_14.

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Conference papers on the topic "Particle-in-cell simulation"

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Lim, Jeremy, Ricky Ang, Wen Jun Ding, Xiao Xiong, Ching Eng Png, Lin Wu, Do Thi Bich Hue, Michel Bosman, and Zackaria Mahfoud. "Particle-in-Cell Simulation of Plasmons." In 2020 IEEE International Conference on Plasma Science (ICOPS). IEEE, 2020. http://dx.doi.org/10.1109/icops37625.2020.9717348.

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Koh, W. S., S. H. Chen, and L. K. Ang. "Particle-in-cell simulation of plasmonic nanoparticle." In 2011 IEEE 38th International Conference on Plasma Sciences (ICOPS). IEEE, 2011. http://dx.doi.org/10.1109/plasma.2011.5993225.

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Langdon, A. Bruce. "Evolution of Particle-in-Cell plasma simulation." In 2013 IEEE 40th International Conference on Plasma Sciences (ICOPS). IEEE, 2013. http://dx.doi.org/10.1109/plasma.2013.6633426.

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Matyash, Konstantin. "Particle in cell simulation of plasma thrusters." In 2013 IEEE 40th International Conference on Plasma Sciences (ICOPS). IEEE, 2013. http://dx.doi.org/10.1109/plasma.2013.6635220.

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Matyash, K., and R. Schneider. "Particle in cell simulation of plasma thrusters." In 2013 IEEE Pulsed Power and Plasma Science Conference (PPPS 2013). IEEE, 2013. http://dx.doi.org/10.1109/ppc.2013.6627708.

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Max Chung, Shen Shou. "Particle-in-Cell Simulation on Spark Gap." In 2020 IEEE International Conference on Plasma Science (ICOPS). IEEE, 2020. http://dx.doi.org/10.1109/icops37625.2020.9717588.

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Kwan, Thomas J. T., Cheungkun Huang, Dmitry Shchegolkov, and Evgenya Simakov. "Particle-in-cell simulation of dielectric wall accelerators." In 2013 IEEE 40th International Conference on Plasma Sciences (ICOPS). IEEE, 2013. http://dx.doi.org/10.1109/plasma.2013.6635129.

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Akarsu, Erol, Kivanc Dincer, Tomasz Haupt, and Geoffrey C. Fox. "Particle-in-cell simulation codes in High Performance Fortran." In the 1996 ACM/IEEE conference. New York, New York, USA: ACM Press, 1996. http://dx.doi.org/10.1145/369028.369108.

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Singh, Rajanish Kumar, and M. Thottappan. "Particle-in-cell (PIC) simulation of a 250GHz gyrotron." In 2016 Progress in Electromagnetic Research Symposium (PIERS). IEEE, 2016. http://dx.doi.org/10.1109/piers.2016.7735641.

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Nakanotani, Masaru, Shuichi Matsukiyo, and Tohru Hada. "Full Particle-in-Cell Simulation of Two Colliding Shocks." In Proceedings of the 12th Asia Pacific Physics Conference (APPC12). Journal of the Physical Society of Japan, 2014. http://dx.doi.org/10.7566/jpscp.1.015103.

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Reports on the topic "Particle-in-cell simulation"

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Averkin, Sergey. Particle In Cell Simulation of Nanotube Growth. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1468984.

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Joyce, Glenn, Martin Lampe, Steven P. Slinker, and Wallace M. Manheimer. Electrostatic Particle-in-Cell Simulation Technique for Quasineutral Plasma. Fort Belvoir, VA: Defense Technical Information Center, March 1997. http://dx.doi.org/10.21236/ada323507.

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Chacon, Luis. Fully implicit particle-in-cell algorithms for kinetic simulation of plasmas. Office of Scientific and Technical Information (OSTI), February 2013. http://dx.doi.org/10.2172/1063911.

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Peter H Stoltz. Final Report for "Simulation Tools for Parallel Microwave Particle in Cell Modeling". Office of Scientific and Technical Information (OSTI), September 2008. http://dx.doi.org/10.2172/938509.

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Gibbons, Matthew Richard. Low frequency, electrodynamic simulation of kinetic plasmas with the DArwin Direct Implicit Particle-In-Cell (DADIPIC) method. Office of Scientific and Technical Information (OSTI), June 1995. http://dx.doi.org/10.2172/130663.

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Dipp, T. M. Particle-In-Cell (PIC) code simulation results and comparison with theory scaling laws for photoelectron-generated radiation. Office of Scientific and Technical Information (OSTI), December 1993. http://dx.doi.org/10.2172/10129595.

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Byers, J. A., T. J. Williams, B. I. Cohen, and A. M. Dimits. Three-dimensional gyrokinetic particle-in-cell simulation of plasmas on a massively parallel computer: Final report on LDRD Core Competency Project, FY 1991--FY 1993. Office of Scientific and Technical Information (OSTI), April 1994. http://dx.doi.org/10.2172/10157900.

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J.L.V. Lewandowski. Particle-in-cell Simulations with Kinetic Electrons. Office of Scientific and Technical Information (OSTI), February 2004. http://dx.doi.org/10.2172/821523.

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J.L.V. Lewandowski. Optimized Loading for Particle-in-cell Gyrokinetic Simulations. Office of Scientific and Technical Information (OSTI), May 2004. http://dx.doi.org/10.2172/827943.

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Gladd, N. T. A C ++ Formulation for Particle-In-Cell Simulations. Fort Belvoir, VA: Defense Technical Information Center, September 1995. http://dx.doi.org/10.21236/ada302566.

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