Abstract Genetic algorithms for nuclear reactor fuel load and reload optimization problems
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Genetic algorithms for nuclear reactor fuel load and reload optimization problems

机译：核反应堆燃料装载和再装载优化问题的遗传算法

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AbstractApproaches are examined in the present paper to the application of genetic algorithms for optimization of initial reactor load and subsequent reloading and reshuffling of fuel assemblies in the nuclear reactor core. The issues associated with selection of the optimization criterion, which was chosen to be the nuclear fuel burnup depth, are discussed. The burnup depth is estimated after the fuel assembly is unloaded from the core, i.e. after residence in the reactor core during 3 fuel irradiation campaigns.An important aspect determining the efficiency of the use of the genetic algorithm in the problem under examination is that the neutronics calculation of the reactor core is to be performed in sufficient details allowing "feeling" the change in the location of the fuel assemblies relative to each other. The use of low-precision instrument results in the uselessness of the proposed approach to the optimization of reactor core loading. The opposite extreme, i.e. the excessive degree of details, is associated with significant increase of expended computer CPU time. In the presented paper, the TRIGEX application software package was used in the analysis of neutronics characteristics of the reactor core providing acceptable degree of details and capable to demonstrate sensitivity of the results to the changes in the reactor load arrangement.The genetic algorithm incorporates the use of at least two basic procedures—selection and mutation. One of the most important issues in the application of the genetic algorithm is the definition of the basic concepts, namely the concepts ofmutation, crossing,andspecimen. The answers to these questions as applicable to the problem under discussion are provided in the present paper. In addition, the main recommendations for the organization of crossing and mutation procedures are also given.The efficiency of use of the developed model of the genetic algorithm is demonstrated by the test example of a BN type reactor. The results of the test run demonstrated that the use of the proposed approach allows searching for optimal reactor load mapping for each separate core reshuffling operation. The main objective of the performed study was to demonstrate the applicability and efficiency of the new up-to-date approach to solving the problem of fuel loading into a nuclear reactor.
机译： 摘要 在本文中，我们研究了遗传算法在优化初始反应堆负荷以及随后对核反应堆堆芯中的燃料组件进行重装和改组方面的应用方法。讨论了与选择最佳标准相关的问题，该标准被选为核燃料燃尽深度。燃耗深度是在从核芯上卸下燃料组件后（即在3次燃料辐照活动期间留在反应堆核芯中之后）估算的。 确定正在研究的问题中使用遗传算法的效率的一个重要方面是，反应堆堆芯的中子学计算应足够详细地进行，以“感觉”到燃料组件位置的变化相对于彼此。低精度仪器的使用导致了所提出的用于优化反应堆堆芯负载的方法的无用性。相反的极端情况，即过多的细节程度，与所花费的计算机CPU时间的显着增加有关。在本文中，使用TRIGEX应用软件包对反应堆堆芯的中子学特性进行了分析，提供了可接受的详细程度，并能够证明结果对反应堆负荷布置变化的敏感性。 遗传算法结合了至少两个基本过程的使用-选择和突变。遗传算法应用中最重要的问题之一是基本概念的定义，即变异，杂交，和 。本文提供了适用于所讨论问题的这些问题的答案。此外，还提供了有关杂交和突变程序组织的主要建议。 通过BN型反应堆的测试实例证明了遗传算法的模型。测试运行的结果表明，使用建议的方法可以为每个单独的堆芯改组操作搜索最佳反应堆负荷图。进行研究的主要目的是证明新的最新方法可以解决核反应堆中的燃料装载问题。

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