scholarly journals Enhanced Beam Diagnostics with Existing BPPMs via GPU-powered Multi-Particle Simulation

2021 ◽  
Author(s):  
En-Chuan Huang ◽  
Charles Taylor ◽  
Petr Anisimov
Keyword(s):  
Particle Simulation ◽  
Beam Diagnostics

Gyro-Kinetic Particle Simulation of m=1 Internal Kink Mode in the Presence of Density Gradient.

1999 ◽  
Vol 75 (10) ◽  
pp. 1188-1194 ◽  
Author(s):  
Taro MATSUMOTO ◽  
Shinji TOKUDA ◽  
Yasuaki KISHIMOTO ◽  
Tomonori TAKIZUKA ◽  
Hiroshi NAITOU
Keyword(s):  
Density Gradient ◽  
Particle Simulation ◽  
Kink Mode

scholarly journals Gyrokinetic Particle Simulation Using the Orbit Averaged Electron Drift-Kinetic Equation.

1999 ◽  
Vol 75 (2) ◽  
pp. 131-142 ◽  
Author(s):  
Yasuhiro IDOMURA ◽  
Shinji TOKUDA ◽  
Masahiro WAKATANI
Keyword(s):  
Kinetic Equation ◽  
Particle Simulation ◽  
Electron Drift

scholarly journals Particle simulation of plasmons

Nanophotonics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 3303-3313 ◽  
Author(s):  
Wen Jun Ding ◽  
Jeremy Zhen Jie Lim ◽  
Hue Thi Bich Do ◽  
Xiao Xiong ◽  
Zackaria Mahfoud ◽  
...  
Keyword(s):  
Simulation Method ◽  
Electron Excitation ◽  
Particle Simulation ◽  
Quantum Limit ◽  
Free Electrons ◽  
Theoretical Approaches ◽  
Electromagnetic Responses ◽  
Electric And Magnetic Fields ◽  
Particle Simulations ◽  
Collective Oscillations

AbstractParticle simulation has been widely used in studying plasmas. The technique follows the motion of a large assembly of charged particles in their self-consistent electric and magnetic fields. Plasmons, collective oscillations of the free electrons in conducting media such as metals, are connected to plasmas by very similar physics, in particular, the notion of collective charge oscillations. In many cases of interest, plasmons are theoretically characterized by solving the classical Maxwell’s equations, where the electromagnetic responses can be described by bulk permittivity. That approach pays more attention to fields rather than motion of electrons. In this work, however, we apply the particle simulation method to model the kinetics of plasmons, by updating both particle position and momentum (Newton–Lorentz equation) and electromagnetic fields (Ampere and Faraday laws) that are connected by current. Particle simulation of plasmons can offer insights and information that supplement those gained by traditional experimental and theoretical approaches. Specifically, we present two case studies to show its capabilities of modeling single-electron excitation of plasmons, tracing instantaneous movements of electrons to elucidate the physical dynamics of plasmons, and revealing electron spill-out effects of ultrasmall nanoparticles approaching the quantum limit. These preliminary demonstrations open the door to realistic particle simulations of plasmons.

Particle simulation of streamer discharges on surface of DC transmission line in presence of raindrops

2021 ◽  
Vol 129 (18) ◽  
pp. 183306
Author(s):  
Xin-chun Zhang ◽  
Feng Wang ◽  
Nan-nan Liu ◽  
An-qi Li ◽  
Wei-li Fan
Keyword(s):  
Transmission Line ◽  
Particle Simulation ◽  
Streamer Discharges

scholarly journals A Robust SMC-PHD Filter for Multi-Target Tracking with Unknown Heavy-Tailed Measurement Noise

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3611
Author(s):  
Yang Gong ◽  
Chen Cui
Keyword(s):  
Target Tracking ◽  
Sequential Monte Carlo ◽  
Measurement Noise ◽  
Particle Simulation ◽  
Target Number ◽  
Student T Distribution ◽  
Multi Target Tracking ◽  
Heavy Tailed ◽  
Scale Matrix ◽  
T Distribution

In multi-target tracking, the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter is a practical algorithm. Influenced by outliers under unknown heavy-tailed measurement noise, the SMC-PHD filter suffers severe performance degradation. In this paper, a robust SMC-PHD (RSMC-PHD) filter is proposed. In the proposed filter, Student-t distribution is introduced to describe the unknown heavy-tailed measurement noise where the degrees of freedom (DOF) and the scale matrix of the Student-t distribution are respectively modeled as a Gamma distribution and an inverse Wishart distribution. Furthermore, the variational Bayesian (VB) technique is employed to infer the unknown DOF and scale matrix parameters while the recursion estimation framework of the RSMC-PHD filter is derived. In addition, considering that the introduced Student- t distribution might lead to an overestimation of the target number, a strategy is applied to modify the updated weight of each particle. Simulation results demonstrate that the proposed filter is effective with unknown heavy-tailed measurement noise.

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