Does the Slim‐Disk Model Correctly Consider Photon‐trapping Effects?

2002 ◽  
Vol 574 (1) ◽  
pp. 315-324 ◽  
Author(s):  
Ken Ohsuga ◽  
Shin Mineshige ◽  
Masao Mori ◽  
Masayuki Umemura
Keyword(s):  
Disk Model ◽  
Photon Trapping ◽  
Trapping Effects

scholarly journals Modeling of photon trapping effects in high-density divertor plasmas

2020 ◽  
Vol 247 ◽  
pp. 106949
Author(s):  
J. Rosato ◽  
M. Koubiti ◽  
Y. Marandet ◽  
R. Stamm
Keyword(s):  
High Density ◽  
Photon Trapping ◽  
Trapping Effects

Dicke photon trapping effects in molecular beam-foil experiments

1978 ◽  
Vol 65 (3) ◽  
pp. 196-198 ◽  
Author(s):  
S. Ruschin ◽  
Y. Ben-Aryeh ◽  
D. Nir
Keyword(s):  
Molecular Beam ◽  
Photon Trapping ◽  
Beam Foil ◽  
Trapping Effects

Photon Trapping Effects in DEMO Divertor Plasma

2016 ◽  
Vol 56 (6-8) ◽  
pp. 657-662 ◽  
Author(s):  
K. Hoshino ◽  
K. Sawada ◽  
R. Idei ◽  
S. Tokunaga ◽  
N. Asakura ◽  
...  
Keyword(s):  
Photon Trapping ◽  
Trapping Effects

scholarly journals INS/gravity gradient aided navigation based on gravitation field particle filter

Open Physics ◽  
2019 ◽  
Vol 17 (1) ◽  
pp. 709-718
Author(s):  
Fanming Liu ◽  
Fangming Li ◽  
Xin Jing
Keyword(s):  
Particle Filter ◽  
Inertial Navigation System ◽  
Attractive Force ◽  
Gravity Gradient ◽  
Experimental Conditions ◽  
Gravitation Field ◽  
Disk Model ◽  
Swarm Optimization ◽  
Repulsion Force ◽  
Position Estimate

Abstract Swarm intelligence method is an effective way to improve the particle degradation and sample depletion of the traditional particle filter. This paper proposes a particle filer based on the gravitation field algorithm (GF-PF), and the gravitation field algorithm is introduced into the resampling process to improve particle degradation and sample depletion. The gravitation field algorithm simulates the solar nebular disk model, and introduces the virtual central attractive force and virtual rotation repulsion force between particles. The particles are moves rapidly to the high-likelihood region under action of the virtual central attractive force. The virtual rotation repulsion force makes the particles keep a certain distance from each other. These operations improve estimation performance, avoid overlapping of particles and maintain the diversity of particles. The proposed method is applied into INS/gravity gradient aided navigation, by combining the sea experimental data of an inertial navigation system. Compared with the particle swarm optimization particle filter(PSO-PF) and artificial physics optimized particle filter (APO-PF), the GF-PF has higher position estimate accuracy and faster convergence speed with the same experimental conditions.

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