MOOGLE: A Multi-Objective Optimization tool for three-dimensional nuclear fuel assembly design

Brian Andersen, David J. Kropaczek

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish
Article number104518
JournalProgress in Nuclear Energy
Volume155
DOIs
StatePublished - Jan 2023

Funding

This manuscript has been co-authored by an employee of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725.

Keywords

  • Fuel assembly design
  • Fuel lattices
  • Genetic algorithm
  • Multi-Objective Optimization

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