Multiobjective genetic algorithm strategies for burnable poison design of pressurized water reactor

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Abstract

The design of burnable poisons (BPs) can compensate for excess reactivity at the beginning of lifetime of nuclear reactors and flatten power distribution, which is especially important for long-cycle nuclear reactors. The design of BPs requires the optimization of material type, purity, layout, axial division of the BPs, so that the reactivity introduction at the beginning of life, poisons residues at the end of life, fuel utilization and power flattening can be comprehensively optimized, which is a multi-input multiobjective optimization problem. At present, the traditional optimization design mainly relies on the subjective experience and judgment of designers, which is complicated and time-consuming. Therefore, the efficiency and reliability of BPs design urgently need to be improved. In this article, the mathematical model of multiobjective optimization based on genetic algorithm (GA) was established for BPs design in pressurized water reactor (PWR) fuel assembly. Then, optimization program was developed by combining parallel multiobjective GA with Monte Carlo particle transport code Reactor Monte Carlo as the neutronics and depletion solver. The optimization method and program were applied to the BPs design in two-dimensional and three-dimensional fuel assembly of PWR. The optimization schemes of BPs searched by GA were similar to the schemes by the manual search of designers for two-dimensional assembly in the previous research. The developed optimization method and program were proved to be effective for BP designs of PWR assembly, which do not require the manual experience of designers for searching the optimization scheme. This article provided useful methods and tools for BPs design of nuclear reactors.

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