Moment approach to the bootstrap current in nonaxisymmetric toroidal plasmas using δf Monte Carlo methods

被引：6
|
作者：
Matsuyama, A. [1 ]
Isaev, M. Yu. [2 ]
Watanabe, K. Y. [3 ]
Hanatani, K. [4 ]
Suzuki, Y. [3 ]
Nakajima, N. [3 ]
Cooper, W. A. [5 ]
Tran, T. M. [5 ]
机构：
[1] Kyoto Univ, Grad Sch Energy Sci, Kyoto 6110011, Japan
[2] RRC Kurchatov Inst, Nucl Fus Inst, Moscow 123182, Russia
[3] Natl Inst Nat Sci, Natl Inst Fus Sci, Toki, Gifu 5095292, Japan
[4] Kyoto Univ, Inst Adv Energy, Kyoto 6110011, Japan
[5] Ecole Polytech Fed Lausanne, Ctr Rech Phys Plasmas, Assoc Euratom Suisse, CH-1015 Lausanne, Switzerland
来源：
PHYSICS OF PLASMAS | 2009年 / 16卷 / 05期
基金：
瑞士国家科学基金会;
关键词：
method of moments; Monte Carlo methods; plasma collision processes; plasma kinetic theory; plasma simulation; plasma toroidal confinement; plasma transport processes; stellarators; LARGE HELICAL DEVICE; NEOCLASSICAL TRANSPORT; COEFFICIENTS; SYSTEMS; COLLISIONALITY; CONFIGURATIONS; STELLARATORS; CONFINEMENT; IMPURITIES; SIMULATION;
D O I：
10.1063/1.3121223
中图分类号：
O35 [流体力学]; O53 [等离子体物理学];
学科分类号：
070204 ; 080103 ; 080704 ;
摘要：
To evaluate the bootstrap current in nonaxisymmetric toroidal plasmas quantitatively, a delta f Monte Carlo method is incorporated into the moment approach. From the drift-kinetic equation with the pitch-angle scattering collision operator, the bootstrap current and neoclassical conductivity coefficients are calculated. The neoclassical viscosity is evaluated from these two monoenergetic transport coefficients. Numerical results obtained by the delta f Monte Carlo method for a model heliotron are in reasonable agreement with asymptotic formulae and with the results obtained by the variational principle.
引用
收藏
页数：9
相关论文
共 50 条
  • [21] An atomistic simulation of solid state sintering using Monte Carlo methods
    Sutton, RA
    Schaffer, GB
    MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING, 2002, 335 (1-2): : 253 - 259
  • [22] An Approach for Analyzing Queuing Systems using Markov Chain Monte Carlo Methods: A Traffic Flow Case Study
    Wong, Xin Ci
    Ahmed, Syed Khaleel
    Zulkifli, Fadhilah
    Ramasamy, Agileswari K.
    2009 IEEE STUDENT CONFERENCE ON RESEARCH AND DEVELOPMENT: SCORED 2009, PROCEEDINGS, 2009, : 41 - 44
  • [23] Sequential Dynamic Leadership Inference Using Bayesian Monte Carlo Methods
    Li, Qing
    Ahmad, Bashar, I
    Godsill, Simon J.
    IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2021, 57 (04) : 2039 - 2052
  • [24] Monte Carlo Methods for the Ferromagnetic Potts Model Using Factor Graph Duality
    Molkaraie, Mehdi
    Gomez, Vicenc
    IEEE TRANSACTIONS ON INFORMATION THEORY, 2018, 64 (12) : 7449 - 7464
  • [25] A probabilistic occupant evacuation model for fire emergencies using Monte Carlo methods
    Zhang, Xia
    Li, Xiao
    Hadjisophocleous, George
    FIRE SAFETY JOURNAL, 2013, 58 : 15 - 24
  • [26] Two improvements in risk analysis for project cost using Monte Carlo methods
    Guo, Y
    Liu, EL
    NEW TRENDS OF INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT IN NEW CENTURY, 2001, : 679 - 683
  • [27] Reliability analysis of the stress intensity factor using multilevel Monte Carlo methods
    Hamdia, Khader M.
    Ghasemi, Hamid
    PROBABILISTIC ENGINEERING MECHANICS, 2023, 74
  • [28] Propagating probability distributions of stand variables using sequential Monte Carlo methods
    Gove, Jeffrey H.
    FORESTRY, 2009, 82 (04): : 403 - 418
  • [29] The Mathematical Simulation for the Photocatalytic Fatigue of Polymer Nanocomposites Using the Monte Carlo Methods
    Orekhov, Andrey V.
    Artemev, Yurii M.
    Pavilaynen, Galina V.
    MATHEMATICS, 2022, 10 (09)
  • [30] Numerical approach to predict particle breakage in dense flows by coupling multiphase particle-in-cell and Monte Carlo methods
    Zhang, Wei
    You, Changfu
    POWDER TECHNOLOGY, 2015, 283 : 128 - 136
12345