Optimization of BWR fuel lattice enrichment and gadolinia distribution using genetic algorithms and knowledge

摘要

An optimization methodology based on the Genetic Algorithms (GA) method was developed for the design of radial enrichment and gadolinia distributions for boiling water reactor (BWR) fuel lattices. The optimization algorithm was linked to the HELIOS code to evaluate the neutronic parameters included in the objective function. The goal is to search for a fuel lattice with the lowest average enrichment, which satisfy a reactivity target, a local power peaking factor (PPF), lower than a limit value, and an average gadolinia concentration target. The methodology was applied to the design of a 10 x 10 fuel lattice, which can be used in fuel assemblies currently used in the two BWRs operating at Mexico. The optimization process showed an excellent performance because it found forty lattice designs in which the worst one has a better neutronic performance than the reference lattice design. The main contribution of this study is the development of an efficient procedure for BWR fuel lattice design, using GA with an objective function (OF) which saves computing time because it does not require lattice burnup calculations.