Published June 2015
| Version v1
Journal article
Application of least square support vector machine in core power distribution reconstruction
Creators
- 1. Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu (China)
- 2. Department of Engineering Physics, Tsinghua University, Beijing (China)
Description
The application of the least square support vector machine (LS-SVM) to core axial power distribution reconstruction was researched, and 18-node powers were reconstructed from six-level in-core detector signals. Axial power distributions of 7740 cases of ACP-100 modular reactor were used to verify the accuracy of the LS-SVM reconstruction method. The results show that the LS-SVM method performs much better than the alternating conditional expectation (ACE) method and the LS-SVM method has good robustness. (authors)
Additional details
Publishing Information
- Journal Title
- Atomic Energy Science and Technology
- Journal Volume
- 49
- Journal Issue
- 6
- Journal Page Range
- p. 1026-1031
- ISSN
- 1000-6931
INIS
- Country of Publication
- China
- Country of Input or Organization
- China
- INIS RN
- 48072544
- Subject category
- S21: SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS;
- Descriptors DEI
- ACCURACY; CHINA; LEAST SQUARE FIT; NEUTRON DETECTION; NEUTRON FLUX; POWER DISTRIBUTION; PWR TYPE REACTORS; REACTOR CORES; SIGNALS; VECTORS
- Descriptors DEC
- ASIA; DETECTION; ENRICHED URANIUM REACTORS; MATHEMATICAL SOLUTIONS; MAXIMUM-LIKELIHOOD FIT; NUMERICAL SOLUTION; POWER REACTORS; RADIATION DETECTION; RADIATION FLUX; REACTOR COMPONENTS; REACTORS; TENSORS; THERMAL REACTORS; WATER COOLED REACTORS; WATER MODERATED REACTORS
Optional Information
- Notes
- 3 figs., 1 tab., 6 refs.