Implementation of Grey Wolf Optimization (GWO) algorithm to multi-objective loading pattern optimization of a PWR reactor. (1st December 2020)

• Record Type: Journal Article
• Authors: Naserbegi, A.; Aghaie, M.; Zolfaghari, A.
• Is Part Of: Annals of nuclear energy. Volume 148(2020)
• Publication Date: 2020-12-01
• Subjects: GWO -- WWER-1000 -- Multi-objective LPO -- Neutronic -- Thermal-hydraulic; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2020.107703
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: The Grey Wolf Optimizer algorithm is introduced for LPO of a PWR. Neutronic and thermal-hydraulic calculations for a PWR core is performed. Effective parameters are included in a multi objective fitness function. Rastrigin and Griewank problems are solved with GWO for capability demonstration. The GWO results are compared with GA, GSA and capabilities are indicated in LPO. Abstract: Loading pattern optimization (LPO) is a main issue of in-core fuel management. In this work, the Grey Wolf Optimizer (GWO) algorithm based on behavior of gray wolves for hunting is introduced for the multi-objective LPO of a WWER-1000 core. Besides the GWO, the Genetic Algorithm (GA) and Gravitational Search Algorithm (GSA) have been applied and the performances of these algorithms are compared in challenging test functions (Griewank and Rastrigin) and LPO. The basic goal of this paper is to optimize the loading pattern with Neutronic and Thermal-Hydraulic (NTH) parameters, simultaneously. Considering these goals, a fitness function is defined for increasing k eff and flattening of PPF s. Thermal-hydraulic (TH) goals with specified weight are considered in this fitness (including critical heat flux (CHF) and fuel temperature). To calculate the required parameters of the core, two nuclear computational codes are used. The neutronic calculations are done by PARCS and COBRA-EN code is implemented for Thermal-hydraulic. These codes have been coupled with the GWO, GA and GSA algorithms with proper procedures. The results demonstrate the usefulness of the GWO and confirm that the GWO has appropriate adaptability for loading pattern optimization.