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PWR Nuclear Power Plants Fuel Management Optimization

机译:压水堆核电站的燃料管理优化

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The objective of this paper is to develop a new hybrid mutation in genetic algorithm (GA) for designing the loading pattern (LP) in pressurized water reactors. Because of huge number of possible combinations for the fuel assemblies (FA's) loading in a core, finding the optimum solution is truly a complex problem. In common genetic algorithm the mutation and crossover techniques are used to optimize an objective function but in this paper a new hybrid mutation is presented. In this study flattening of power inside a reactor core is chosen as an objective function. To obtain optimal FA arrangement, a core reload package code, MAKNOGA, based on well established MAKGA code is developed. This code is applicable for all types of PWR core having different geometries and designs with an unlimited number of FA types. The result is well improved in comparison with pattern proposed by designer.
机译:本文的目的是在遗传算法(GA)中开发一种新的杂交突变,以设计压水堆中的装载模式(LP)。由于堆芯中燃料组件(FA)装载的可能组合数量众多,因此找到最佳解决方案确实是一个复杂的问题。在常见的遗传算法中,使用变异和交叉技术来优化目标函数,但本文提出了一种新的杂交变异。在这项研究中,选择电抗器堆芯内部的功率平坦化作为目标函数。为了获得最佳的FA布置,基于完善的MAKGA代码,开发了核心重装软件包代码MAKNOGA。该代码适用于具有不同几何形状和设计且具有无限数量的FA类型的所有类型的PWR磁芯。与设计人员提出的模式相比,结果得到了很好的改善。

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