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Evaluation of applying axial variation of enrichment distribution method and radial variation of enrichment distribution in WER/1000 reactor using a Hopfield neural network to optimize fuel management

机译:利用Hopfield神经网络优化燃料管理在WER / 1000反应堆中应用浓度分布轴向变化和浓度分布径向变化的评价

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In this present work the analysis technique was developed to find the optimum core configuration by applying neural network. This work investigates an appropriate way to solve the problem of optimizing fuel management in WER/1000 reactor. To automate this procedure, a computer program has been developed. This program suggests an optimal core configuration which is determined to establish safety constraints. The suggested solution is based on the use of coupled programs, which one of them is the nuclear code, for making a database and modeling the core, and another one is Hopfield Neural Network Artificial (HNNA). The first stage of computational procedure consists of creating the cross section database and calculating neutronic parameters by using WIMSD4 and CITATION codes. The second one, consists of finding the optimum core loading pattern by applying the primary fuel assemblies of the WER/1000 reactor core, using the HNNA method that based on minimizing power peaking factor (PPF) and maximizing the effective multiplication factor (keff). In the third second one, we apply a heuristic method to flat the flux core and decreasing the power peaking factor of the core. It consists of finding the best axial and radial variation of enrichment distribution to reach an optimum core loading pattern, by using HNNA and the cross section database. Finally, we compared obtained results of these methods to obtained results of the primary core, Suggested pattern of the Russian contractor. In total, the results show that applying the HNNA led us to the appropriate PPF and keff. Therefore, we achieved to a set of two basic parameters PPF and keff as effective factors on satisfying the safety constraints of WER/1000 reactor core. It should be mentioned to say that the obtained results of HNNA suggested pattern is promising. Therefore, these methods ultimately eventuated to find the optimum configuration for WER/1000 reactor core.
机译:在本工作中,开发了分析技术以通过应用神经网络找到最佳的堆芯配置。这项工作研究了解决WER / 1000反应堆燃料优化管理问题的适当方法。为了使该过程自动化,已经开发了计算机程序。该程序建议确定最佳的核心配置,以建立安全约束。建议的解决方案基于耦合程序的使用,其中一个是核代码,用于建立数据库和建模核心,另一个是Hopfield神经网络仿真(HNNA)。计算过程的第一阶段包括创建横截面数据库并使用WIMSD4和CITATION代码计算中子学参数。第二个方法是通过应用WER / 1000反应堆堆芯的主要燃料组件,使用基于最小化功率峰值因子(PPF)和最大化有效倍增因子(keff)的HNNA方法来找到最佳堆芯装载模式。在第三篇第二篇中,我们应用启发式方法来使磁通磁芯扁平化并降低磁芯的功率峰值因数。它包括通过使用HNNA和横截面数据库来找到最佳的富集分布轴向和径向变化,以达到最佳的岩心加载模式。最后,我们将这些方法获得的结果与主要核心(俄罗斯承包商的建议模式)获得的结果进行了比较。总体而言,结果表明,应用HNNA使我们获得了适当的PPF和keff。因此,我们获得了两个基本参数PPF和keff作为满足WER / 1000反应堆堆芯安全约束的有效因素。应当说,HNNA建议模式的结果是有希望的。因此,最终这些方法最终找到了WER / 1000反应堆堆芯的最佳配置。

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