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- W1947011607 abstract "A genetic algorithm (GA)–based optimizer (GAOPT) has been developed for in-core fuel management of advanced gas-cooled reactors (AGRs) at HINKLEY B and HARTLEPOOL, which employ on-load and off-load refueling, respectively. The optimizer has been linked to the reactor analysis code PANTHER for the automated evaluation of loading patterns in a two-dimensional geometry, which is collapsed from the three-dimensional reactor model. GAOPT uses a directed stochastic (Monte Carlo) algorithm to generate initial population members, within predetermined constraints, for use in GAs, which apply the standard genetic operators: selection by tournament, crossover, and mutation. The GAOPT is able to generate and optimize loading patterns for successive reactor cycles (multicycle) within acceptable CPU times even on single-processor systems. The algorithm allows radial shuffling of fuel assemblies in a multicycle refueling optimization, which is constructed to aid long-term core management planning decisions. This paper presents the application of the GA-based optimization to two AGR stations, which apply different in-core management operational rules. Results obtained from the testing of GAOPT are discussed." @default.
- W1947011607 created "2016-06-24" @default.
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- W1947011607 date "2003-02-01" @default.
- W1947011607 modified "2023-09-27" @default.
- W1947011607 title "Multicycle Optimization of Advanced Gas-Cooled Reactor Loading Patterns Using Genetic Algorithms" @default.
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- W1947011607 doi "https://doi.org/10.13182/nt03-a3355" @default.
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