Application of Genetic Algorithm methodologies in fuel bundle burnup optimization of Pressurized Heavy Water Reactor

Highlights: • We study and compare Genetic Algorithms (GA) in the fuel bundle burnup optimization of an Indian Pressurized Heavy Water Reactor (PHWR) of 220 MWe. • Two Genetic Algorithm methodologies namely, Penalty Functions based GA and Multi Objective GA are considered. • For the selected problem, Multi Objective GA performs better than Penalty Functions based GA. • In the present study, Multi Objective GA outperforms Penalty Functions based GA in convergence speed and better diversity in solutions. - Abstract: The work carried out as a part of application and comparison of GA techniques in nuclear reactor environment is presented in the study. The nuclear fuel management optimization problem selected for the study aims at arriving appropriate reference discharge burnup values for the two burnup zones of 220 MWe Pressurized Heavy Water Reactor (PHWR) core. Two Genetic Algorithm methodologies namely, Penalty Functions based GA and Multi Objective GA are applied in this study. The study reveals, for the selected problem of PHWR fuel bundle burnup optimization, Multi Objective GA is more suitable than Penalty Functions based GA in the two aspects considered: by way of producing diverse feasible solutions and the convergence speed being better, i.e. it is capable of generating more number of feasible solutions, from earlier generations. It is observed that for the selected problem, the Multi Objective GA is 25.0% faster than Penalty Functions based GA with respect to CPU time, for generating 80% of the population with feasible solutions. When average computational time of fixed generations are considered, Penalty Functions based GA is 44.5% faster than Multi Objective GA. In the overall performance, the convergence speed of Multi Objective GA surpasses the computational time advantage of Penalty Functions based GA. The ability of Multi Objective GA in producing more diverse feasible solutions is a desired feature of the problem selected, that helps the reactor operator in getting more choices when deciding the appropriate discharge burnups of the core zones