Published March 2007
| Version v1
Journal article
Construction of neoclassical transport database for large helical device plasma applying neural network method
- 1. Hokkaido Univ., Graduate School of Engineering, Sapporo, Hokkaido (Japan)
- 2. Kyoto Univ., Dept. of Nuclear Engineering, Kyoto (Japan)
Description
A neoclassical transport database for the large helical device (LHD) plasma, DCOM/NNW, is constructed using the neural network method. Monoenergetic neoclassical transport coefficients evaluated by the Monte Carlo code, DCOM, are used as training data of the neural network. The databases for two typical magnetic field configurations in LHD, namely, standard and inward-shifted configurations, are constructed and transport coefficients for thermal plasma are evaluated. The plasma parameter dependencies and the ambipolar radial electric field are investigated. (author)
Additional details
Publishing Information
- Journal Title
- Japanese Journal of Applied Physics. Part 1, Regular Papers, Brief Communications and Review Papers
- Journal Volume
- 46
- Journal Issue
- 3A
- Journal Page Range
- p. 1157-1167
- ISSN
- 0021-4922
INIS
- Country of Publication
- Japan
- Country of Input or Organization
- Japan
- INIS RN
- 38065896
- Subject category
- S70: PLASMA PHYSICS AND FUSION TECHNOLOGY;
- Descriptors DEI
- COLLISIONLESS PLASMA; COLLISIONS; DATA BASE MANAGEMENT; DATA COMPILATION; DIFFUSION; ELECTRIC FIELDS; ELECTRON TEMPERATURE; ION TEMPERATURE; LHD DEVICE; MAGNETIC FIELDS; NEOCLASSICAL TRANSPORT THEORY; NEURAL NETWORKS; PLASMA DENSITY; PLASMA RADIAL PROFILES; PLASMA SIMULATION; THERMAL DIFFUSIVITY
- Descriptors DEC
- CHARGED-PARTICLE TRANSPORT THEORY; CLOSED PLASMA DEVICES; DATA; INFORMATION; MANAGEMENT; PHYSICAL PROPERTIES; PLASMA; SIMULATION; THERMODYNAMIC PROPERTIES; THERMONUCLEAR DEVICES; TRANSPORT THEORY
Optional Information
- Notes
- 25 refs., 17 figs.