A novel multi objective Loading Pattern Optimization by Gravitational Search Algorithm (GSA) for WWER1000 core

Abstract

In this paper, a novel multi objective optimization algorithm, Gravitational Search Algorithm (GSA), is developed in order to implement in the Loading Pattern Optimization (LPO) of a nuclear reactor core. In recent decades several metaheuristic algorithms or computational intelligence methods have been expanded to optimize reactor core loading pattern. Regarding the coupled behavior of Neutronic and Thermal-Hydraulic (NTH) dynamics in a nuclear reactor core, proper loading pattern of fuel assemblies (FAs) depends on NTH aspects, simultaneously. Thus, obtaining optimal arrangement of FAs, in a core to meet special objective functions is a complex problem. Gravitational Search Algorithm (GSA) is constructed based on the law of Gravity and the notion of mass interactions, using the theory of Newtonian physics and searcher agents are the collection of masses. In this work, for multi objective optimization, the NTH aspects are included in fitness function. Neutronic goals include increasing multiplication factor (Keff), decreasing of power picking factor (PPF) and flattening of the power density, also thermal–hydraulic (TH) goals include increasing critical heat flux (CHF) and decreasing average of fuel centers temperature. Therefore, at the first step, GSA method is compared with other metaheuristic algorithms on Shekel's Foxholes problem. In the second step for finding the best core pattern and implementation of the objectives listed, GSA algorithm has been performed for case of WWER1000 reactor. For the NTH calculations, PARCS (Purdue advanced reactor core simulator) and COBRA-EN codes are implemented, respectively. The results demonstrate that GSA algorithm have promising performance and can propose for other optimization problems of nuclear engineering field.