A review on optimization methods for nuclear reactor fuel reloading analysis

Abstract

Nuclear reactor fuel reloading optimization is a hot issue in the field of nuclear engineering. With the reasonable rearrangement of fuel assemblies in a reactor core, the nuclear fuel cycle cost could be reduced while safely operating. However, the problems of fuel reloading optimization prove to be complicated with a huge solution space. Normally, the core physics calculation programs and meta-heuristic algorithms are always combined to solve this problem, which we call it traditional calculation method. Among them, the core physics calculation method is used to quantitatively evaluate the core configurations, while the meta-heuristic algorithms would update them by certain calculation process. With the iterative calculation of these two sub-processes, the best core configuration of fuel assemblies would be finally output. In the actual process of fuel reloading optimization, the selection of meta-heuristic algorithms turns out to be significant, which almost decides the quality of optimization solutions. Therefore, in this review, we mainly concentrate on the research status of meta-heuristic algorithms in fuel reloading optimization. The meta-heuristic algorithms are systematically divided into four types which are individual optimization algorithm, evolutionary algorithm, swarm intelligence algorithm and hybrid optimization algorithm. At the same time, the related research and some typical entities of these four are summarized. Finally, based on our recent work, the development of AI-based calculation models is also stated, including the development of the novel calculation method and the triangle-filter convolution neural network. The results indicate that the hybrid algorithms have better comprehensive optimization ability, with regard to optimization quality and convergence speed. In addition, comparing to traditional calculation method, the application of AI-based calculation method could substantially reduce the calculation time while obtaining feasible solutions.