Optimization of U-Th fuel in heavy water moderated thermal breeder reactors using multivariate regression analysis and genetic algorithms
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2015 Elsevier Ltd. All rights reserved. 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.
