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Self-adaptive global best harmony search algorithm applied to reactor core fuel management optimization

ANNALS OF NUCLEAR ENERGY 2013年 62卷 86-102页

作者： Poursalehi, N. Zolfaghari, A. Minuchehr, A. Valavi, K. Shahid Beheshti Univ Dept Nucl Engn Tehran Iran Islamic Azad Univ Sci & Res Branch Dept Nucl Engn Tehran Iran

The aim of this work is to apply the new developed optimization algorithm, Self-adaptive Global best Harmony Search (SGHS), for PWRs fuel management optimization. SGHS algorithm has some modifications in comparison with basic Harmony Search (HS) and Global-best Harmony Search (GHS) algorithms such as dynamically change of parameters. For the demonstration of SGHS ability to find an optimal configuration of fuel assemblies, basic Harmony Search (HS) and Global-best Harmony Search (GHS) algorithms also have been developed and investigated. For this purpose, Self-adaptive Global best Harmony Search Nodal Expansion package (SGHSNE) has been developed implementing HS, GHS and SGHS optimization algorithms for the fuel management operation of nuclear reactor cores. This package uses developed average current nodal expansion code which solves the multi group diffusion equation by employment of first and second orders of Nodal Expansion Method (NEM) for two dimensional, hexagonal and rectangular geometries, respectively, by one node per a FA. Loading pattern optimization was performed using SGHSNE package for some test cases to present the SGHS algorithm capability in converging to near optimal loading pattern. Results indicate that the convergence rate and reliability of the SGHS method are quite promising and practically, SGHS improves the quality of loading pattern optimization results relative to HS and GHS algorithms. As a result, it has the potential to be used in the other nuclear engineering optimization problems. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Loading pattern optimization Self-adaptive global best harmony search algorithm Nodal expansion method rectangular and hexagonal geometries

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