Published 2019
| Version v1
Book
Tool based on artificial neural networks for PWR loading pattern optimization
- 1. Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG (Brazil). Departamento de Engenharia Nuclear
Description
A methodology for fuel loading patterns based on deep learning techniques has been proposed. A computational tool was implemented using artificial neural networks (ANN), covering the safety criteria of keff < = 1.09 and peak-to-average power ratio < = 1.6. The preliminary results show the capability of the developed ANN to find a pattern of loading the assemblies with the constraints imposed. (author)
Additional details
Publishing Information
- Publisher
- ABEN
- Imprint Place
- Rio de Janeiro, RJ (Brazil)
- ISBN
- 978-85-99141-08-3
- Imprint Title
- Proceedings of the INAC 2019: international nuclear atlantic conference. Nuclear new horizons: fueling our future
- Imprint Pagination
- 6019 p.
- Journal Page Range
- p. 4757-4769
Conference
- Title
- international nuclear atlantic conference; 21. meeting on nuclear reactor physics and thermal hydraulics - ENFIR; 14. meeting on nuclear applications - ENAN; 6. meeting on nuclear industry - ENIN; 1. international workshop on thorium - ITHOR-WS
- Acronym
- INAC 2019
- Dates
- 21-25 Oct 2019
- Place
- Santos, SP (Brazil)
INIS
- Country of Publication
- Brazil
- Country of Input or Organization
- Brazil
- INIS RN
- 51007284
- Subject category
- S97: MATHEMATICAL METHODS AND COMPUTING;
- Resource subtype / Literary indicator
- Conference
- Descriptors DEI
- ARTIFICIAL INTELLIGENCE; COMPUTER CODES; NEURAL NETWORKS; OPTIMIZATION; PEAKS; POWER DISTRIBUTION; PWR TYPE REACTORS; REACTOR CORES; REACTOR FUELING; SPATIAL DISTRIBUTION
- Descriptors DEC
- DISTRIBUTION; ENRICHED URANIUM REACTORS; POWER REACTORS; REACTOR COMPONENTS; REACTORS; THERMAL REACTORS; WATER COOLED REACTORS; WATER MODERATED REACTORS
Optional Information
- Notes
- R03-032