Application of genetic algorithms to optimize fuel loading pattern and use of burnable absorbers to minimize power peaking and control excess reactivity in gas cooled fast reactor

Abstract

This study aims to optimize the fuel loading pattern in gas-cooled fast reactors (GFRs) using genetic algorithms (GA) to reduce the power peaking factor, and in the GA-optimized model, several burnable absorbers (BA), namely Gd₂O₃, Er₂O₃, AmO₂, ZrB₂, HfO₂, Ta₂O₅, Dy₂O₃, Eu₂O₃, and Lu₂O₃ were deployed to control excess reactivity and further enhance power distribution. The GA-optimized fuel loading pattern significantly lowered the power peaking factor without compromising the effective multiplication factor. All GA + BA models, except for the Am and ZrB₂ models, successfully minimized both power peaking and excess reactivity. The Eu and Lu models demonstrated best result in reducing these parameters, though the Eu model’s shorter cycle length and larger reactivity swing make it less favorable. Gd, Er, Hf, Ta, and Dy models also displayed satisfactory performance in lowering both PPF and excess reactivity, and maintained criticality for over 2000 days with moderate reactivity swings. Neutronics analysis revealed that all models achieved harder neutron spectra and maintained uniform radial neutron flux distribution. Additionally, all models achieved satisfactory values for the effective delayed neutron fraction and fuel temperature coefficient, except Ta and Eu model. Overall, the Lu model emerged as the most favorable burnable absorber, as it achieved lower power peaking factor, reduced excess reactivity, good cycle length, moderate reactivity swing, satisfactory neutronics performance, and favorable beta effective and Doppler constant values.