Abstract
In order to maximize both the life cycle and efficiency of a reactor core, it is essential to find the optimum loading pattern. In the case of research reactors, a loading pattern can also be optimized for flux at an irradiation site. Therefore, the development of a general-use methodology for core loading optimization would be very valuable. In this paper, general-use multiobjective core reloading pattern optimization is performed using modified genetic algorithms (MGA). The developed strategy can be applied for the constrained optimization of research and power reactor cores. For an optimal reactor core reloading design strategy, an intelligent technique GA is coupled with the Monte Carlo (MC) code SuperMC developed by the FDS team in China for nuclear reactor physics calculations. An optimal loading pattern can be depicted as a configuration that has the maximum keff and maximum thermal fluxes in the core of the given fuel inventory keeping in view the safety constraints such as limitation on power peaking factor. The optimized loading patterns for Pakistan Research Reactor-1 (PARR-1) have been recommended using the implemented strategy by considering the constraint optimization, i.e., to maximize the keff or maximum thermal neutron flux while maintaining low power peaking factor. It has been observed that the developed intelligent strategy performs these tasks with a reasonable computational cost.
Highlights
Nuclear fuel management is one of the most significant considerations when managing smaller research reactors
Description of the PARR-1 Core. e reactor core considered in this study is Pakistan Research Reactor-1 (PARR1), the swimming pool-type material test research reactor (MTR)
PARR-1 has a full power of a 10 MW, utilizing the low-enriched uranium fuel (LEU) of uranium silicide (U3Si2-Al) fuel contained less than 20% of enriched 235U
Summary
Nuclear fuel management is one of the most significant considerations when managing smaller research reactors. Naive calculations for core loading optimization techniques can be prohibitively expensive due to the computational cost of simulating each arrangement of the reactor core. Extensive studies have been done to optimize the in-core fuel loading pattern during the last few decades, resulting in numerous perspective processes. An improvement of new coding procedure for GA is capable of searching automatically for the optimal fuel loading patterns most suitable for the research reactor [15]. E multiobjective GA (MOGA) used three integer-based arrays to perform the multiobjective optimization of fuel loading patterns of PWR. The effective multiplication factor keff and the neutron flux distributions are considered the most foundational evaluation quantities. In the present study, loading patterns for PARR-1 have been proposed by maximizing the effective multiplication factor and neutron thermal flux while keeping low power peak. (ii) e positions of control fuel elements (CFEs) in the core are fixed, but they can swap their position among the CFEs
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