Heuristic rules embedded genetic algorithm to solve VVER loading pattern optimization problem
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Date
2006
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Azerbaijan National Academy of Sciences Institute of Radiation Problems ve Turkish Atomic Energy Authority
Abstract
Loading Pattern (LP) optimization is one of the most important aspects of the operation of nuclear reactors. A genetic algorithm (GA) code GARCO (Genetic Algorithm Reactor Optimization Code) has been developed with embedded heuristic techniques to perform optimization calculations for in-core fuel management tasks. GARCO is a practical tool that includes a unique methodology applicable for all types of Pressurized Water Reactor (PWR) cores having different geometries with an unlimited number of FA types in the inventoiy. GARCO was developed by modifying the classical representation of the genotype. Both the genotype representation and the basic algorithm have been modified to incorporate the in-core fuel management heuristics rules so as to obtain the best results in a shorter time. GARCO has three modes. Mode 1 optimizes the locations of the fuel assemblies (FAs) in the nuclear reactor core, Mode 2 optimizes the placement of the burnable poisons (BPs) in a selected LP, and Mode 3 optimizes simultaneously both the LP and the BP placement in the core. This study describes the basic algorithm for Mode 1. The GARCO code is applied to the WER-1000 reactor hexagonal geometry core in this study. The “Moby-Dick” is used as reactor physics code to deplete FAs in the core. It was developed to analyze the WER reactors by SKODA Inc. To use these rules for creating the initial population with GA operators, the worth definition application is developed. Each FA has a worth value for each location. This worth is between 0 and 1. If worth of any FA for a location is larger than 0.5, this FA in this location is a good choice. When creating the initial population of LPs, a subroutine provides a percent of individuals, which have genes with higher than the 0.5 worth. The percentage of the population to be created without using worth definition is defined in the GARCO input. And also age concept has been developed to accelerate the GA calculation process in reaching the optimum solutions. The computing time is divided into ages. It can be stated that the classical GA has only one age. It is assumed that in each age the operators work with a group of genes instead of with all of the genes. These groups are created according to in-core fuel management heuristic rules.
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Keywords
Heuristic rules, Sezgisel kurallar, Genetic algorithm, Genetik algoritma, VVER loading pattern, VVER yükleme düzeni, Optimization problem, Optimizasyon problemi
Citation
Alim, F. ve Ivanov, K. (2006). Heuristic rules embedded genetic algorithm to solve VVER loading pattern optimization problem. The Fourth Eurasian Conference on Nuclear Science and Its Application : Presentations, (s. 112-119). 31 October-3 November 2006. Baku, Azerbaijan.