Optimization of U–Th fuel in heavy water moderated thermal breeder reactors using multivariate regression analysis and genetic algorithms
Creators
Description
Highlights: • A new method useful for the parametric analysis and optimization of reactor core designs. • This uses the strengths of genetic algorithms (GA), and regression splines. • The method is applied to the core fuel pin cell of a PHWR design. • Tools like java, R, and codes like Serpent, Matlab are used in this research. - Abstract: An analysis and optimization of a set of neutronics parameters of a thorium-fueled pressurized heavy water reactor core fuel has been performed. The analysis covers a detailed pin-cell analysis of a seed-blanket configuration, where the seed is composed of natural uranium, and the blanket is composed of thorium. Genetic algorithms (GA) is used to optimize the input parameters to meet a specific set of objectives related to: infinite multiplication factor, initial breeding ratio, and specific nuclide's effective microscopic cross-section. The core input parameters are the pitch-to-diameter ratio, and blanket material composition. Recursive partitioning of decision trees (rpart) multivariate regression model is used to perform a predictive analysis of the samples generated from the GA module. Reactor designs are usually complex and a simulation needs a significantly large amount time to execute, hence implementation of GA or any other global optimization techniques is not feasible, therefore we present a new method of using rpart in conjunction with GA. Due to using rpart, we do not necessarily need to run the neutronics simulation for all the inputs generated from the GA module rather, run the simulations for a predefined set of inputs, build a regression fit to the input and the output parameters, and then use this fit to predict the output parameters for the inputs generated by GA. The rpart model is implemented as a library using R programming language. The results suggest that the initial breeding ratio tends to increase due to a harder neutron spectrum, however a softer neutron spectrum is desired to limit the parasitic absorption of Pa-233. The neutronics model, design and analysis have been done using Serpent 1.1.19 Monte Carlo code
Availability note (English)
Available from http://dx.doi.org/10.1016/j.anucene.2015.07.006Additional details
Identifiers
- DOI
- 10.1016/j.anucene.2015.07.006;
- PII
- S0306-4549(15)00364-3;
Publishing Information
- Journal Title
- Annals of Nuclear Energy (Oxford)
- Journal Volume
- 85
- Journal Page Range
- p. 885-892
- ISSN
- 0306-4549
- CODEN
- ANENDJ
INIS
- Country of Publication
- United Kingdom
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 47019456
- Subject category
- S21: SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS;
- Descriptors DEI
- ALGORITHMS; BREEDER REACTORS; BREEDING RATIO; CROSS SECTIONS; DECISION TREE ANALYSIS; FUEL PINS; MONTE CARLO METHOD; MULTIPLICATION FACTORS; MULTIVARIATE ANALYSIS; NATURAL URANIUM; NEUTRON SPECTRA; OPTIMIZATION; PARAMETRIC ANALYSIS; PHWR TYPE REACTORS; PROTACTINIUM 233; REACTOR CORES; REGRESSION ANALYSIS; THORIUM
- Descriptors DEC
- ACTINIDE NUCLEI; ACTINIDES; BETA DECAY RADIOISOTOPES; BETA-MINUS DECAY RADIOISOTOPES; CALCULATION METHODS; CONVERSION RATIO; DAYS LIVING RADIOISOTOPES; DIMENSIONLESS NUMBERS; ELEMENTS; FUEL ELEMENTS; HEAVY NUCLEI; HEAVY WATER COOLED REACTORS; HEAVY WATER MODERATED REACTORS; ISOTOPES; MATHEMATICAL LOGIC; MATHEMATICS; METALS; NUCLEI; ODD-EVEN NUCLEI; PROTACTINIUM ISOTOPES; RADIOISOTOPES; REACTOR COMPONENTS; REACTORS; SPECTRA; STATISTICS; URANIUM
Optional Information
- Copyright
- Copyright (c) 2015 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.