scholarly journals Variational formulation of E & M particle simulation algorithms in cylindrical geometry using an angular modal decomposition

2016 ◽  
Author(s):  
A. B. Stamm ◽  
B. A. Shadwick
Keyword(s):  
Variational Formulation ◽  
Cylindrical Geometry ◽  
Particle Simulation ◽  
Modal Decomposition ◽  
Simulation Algorithms

scholarly journals Variational formulation of macro-particle plasma simulation algorithms

2014 ◽  
Vol 21 (5) ◽  
pp. 055708 ◽  
Author(s):  
B. A. Shadwick ◽  
A. B. Stamm ◽  
E. G. Evstatiev
Keyword(s):  
Variational Formulation ◽  
Plasma Simulation ◽  
Simulation Algorithms

scholarly journals Time-Step Considerations in Particle Simulation Algorithms for Coulomb Collisions in Plasmas

2010 ◽  
Vol 38 (9) ◽  
pp. 2394-2406 ◽  
Author(s):  
Bruce I. Cohen ◽  
Andris M. Dimits ◽  
Alex Friedman ◽  
Russel E. Caflisch
Keyword(s):  
Particle Simulation ◽  
Time Step ◽  
Coulomb Collisions ◽  
Simulation Algorithms

Bridge Resistance Updating Based on the General Particle Simulation Algorithms of Complex Bayesian Formulas

2021 ◽  
Vol 43 ◽  
pp. 111-122
Author(s):  
Xue Ping Fan ◽  
Sen Wang ◽  
Yue Fei Liu
Keyword(s):  
Expectation Maximization ◽  
Probability Model ◽  
Distribution Functions ◽  
Bridge Engineering ◽  
Particle Simulation ◽  
Posteriori Probability ◽  
Resistance Degradation ◽  
Simulation Algorithms ◽  
Existing Bridges ◽  
Bridge Resistance

The existing bridges are subjected to time-variant loading and resistance degradation processes. How to update resistance probability distribution functions with resistance degradation model and proof load effects has become one of the research hotspots in bridge engineering field. To solve with the above issue, this paper proposed the general particle simulation algorithms of complex Bayesian formulas for bridge resistance updating. Firstly, the complex Bayesian formulas for updating resistance probability model are built. For overcoming the difficulty for the analytic calculation of complex Bayesian formulas, the general particle simulation methods are provided to obtain the particles of complex Bayesian formulas; then, with the improved expectation maximization optimization algorithm obtained with the combination of K-MEANS algorithm and Expectation Maximization (EM) algorithm, the above simulated particles can be used to estimate the posteriori probability density functions of resistance probability model; finally, a numerical example is provided to illustrate the feasibility and application of the proposed algorithms.

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
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