Application of genetic algorithms in optimization of SFR nuclear reactor design

Abstract This work presents a demonstrational application of genetic algorithms (GAs) to solve sample optimization problems in the generation IV nuclear reactor core design. The new software was developed implementing novel GAs, and it applied show their capabilities by presenting an example solution two selected check whether GAs can be used successfully engineering as tool. 3600 MWth oxide core, which based on OECD/NEA sodium-cooled fast (SFR) benchmark, reference design [1]. first problem fuel isotopic inventory terms minimizing volume share long-lived actinides, while maximizing effective neutron multiplication factor. second task boron shield distribution around minimize sodium void reactivity effect (SVRE). Neutron transport depletion simulations were performed using Monte Carlo code SERPENT2. simulation resulted optimized mixture composition for parameters, demonstrates functionality algorithm. results efficiency universality multidimensional engineering.