Abstract
MOOGLE is a new genetic algorithm based methodology for the 3D design of nuclear fuel assemblies. MOOGLE uses common fuel rod types as the decision variable to develop a suite of 3D fuel assemblies to provide optimized solutions to the design problem. Pressurized water reactor (PWR) fuel assemblies were optimized using Integral Fuel Burnable Absorber (IFBA) and gadolinium (Gd2O3) as burnable poisons to compare how burnable poison choice affects optimization results. Boiling water reactor (BWR) fuel bundles were also optimized using three unique fuel rod palettes to study how the size of the design space affects optimization results. Burnable poison analysis showed that utilizing IFBA and Gd2O3 as burnable poisons produced the best and widest range of optimized solutions. BWR fuel bundle optimization results indicate that the inclusion of additional fuel rod types produced a wider solution space but did not improve optimization results for regions explored using fewer unique fuel rods. These tests demonstrate MOOGLE’s ability to analyze the trade-offs between the inclusion of different fuel elements and their effects on assembly performance.
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