A novel method to improve dose assessment due to severe NPP accidents based on field measurements and particle swarm optimization
- 1. Comissão Nacional de Energia Nuclear – IEN/CNEN, Rua Helio de Almeida, 75 Ilha do Fundão, 21941- 906 Rio de Janeiro (Brazil)
- 2. Universidade Federal do Rio de Janeiro – PEN/COPPE/UFRJ, Ilha do Fundão, 21941-901, Centro de Tecnologia, Rio de Janeiro (Brazil)
Description
Highlights: •A new method to improve dose assessment during NPP accidents have been developed. •Estimated dose distribution is corrected by means of field measurements and particle swarm optimization. •Corrections are made directly on the dose distribution map. •Optimized geometric transformations are used to correct the dose distribution map. •Improvements in accuracy of dose estimation was achieved. -- Abstract: Severe nuclear power plant (NPP) accidents are those which involve significant core degradation and lead the plant to conditions more severe than a design basis accident. Under such conditions the accident progression might become unpredictable and the source term estimation, imprecise by orders of magnitude. The consequence is a dose assessment very far from the reality and a deficient decision making support. This work presents a novel approach to improve accuracy of dose estimation, based on field measurements and particle swarm optimization (PSO) algorithm. The main idea is to determine a correction matrix, which once applied to the originally estimated (incorrect) dose distribution map, generates a corrected one, which better fits to the field measurements. The proposed correction matrix is the result of a concatenation of geometric transformations and an amplification/attenuation factor, aimed to fit the shape of the original map and radiation intensities in order to match the field measurements. Finding the optimum transformations (correction matrix) is, however a complex nonlinear optimization problem, which has been successfully solved by using a PSO algorithm. Results demonstrate that PSO was able to find good correction transformations, which can be used to better project future dose distributions and, consequently, improve decision making support.
Availability note (English)
Available from http://dx.doi.org/10.1016/j.anucene.2017.06.027Additional details
Identifiers
- DOI
- 10.1016/j.anucene.2017.06.027;
- PII
- S0306-4549(17)30169-X;
Publishing Information
- Journal Title
- Annals of Nuclear Energy (Oxford)
- Journal Volume
- 110
- Journal Issue
- Complete
- Journal Page Range
- p. 148-159
- ISSN
- 0306-4549
- CODEN
- ANENDJ
INIS
- Country of Publication
- United Kingdom
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 49045417
- Subject category
- S97: MATHEMATICAL METHODS AND COMPUTING; S29: ENERGY PLANNING, POLICY AND ECONOMY;
- Descriptors DEI
- BASES; CORRECTIONS; DECISION MAKING; DESIGN-BASIS ACCIDENTS; MAPS; MATRICES; NONLINEAR PROBLEMS; NUCLEAR POWER PLANTS; OPTIMIZATION; PARTICLES; RADIATION DOSE DISTRIBUTIONS; TRANSFORMATIONS
- Descriptors DEC
- ACCIDENTS; NUCLEAR FACILITIES; POWER PLANTS; THERMAL POWER PLANTS
Optional Information
- Copyright
- Copyright (c) 2017 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.