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Multi objective loading pattern optimization of PWRs with Fuzzy logic controller based Gravitational Search Algorithm

机译:基于引力搜索算法的模糊逻辑控制器的压水堆多目标负荷模式优化

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The multi objective Loading Pattern Optimization (LPO) is one of the most important concerns for the incore design of nuclear reactors. Hence, different techniques have been presented for optimization of incore patterns for nuclear reactors, this paper presents a new optimization technique, which uses Fuzzy Logic Controller (FLC) for solving multi-objective optimization problems. In this work, using the FLC, the gravity constant of the Gravitational Search Algorithm (GSA) is controlled to reach better optimization results and convergence rate. A well-designed loading pattern of fuel assemblies in a reactor core depends on Neutronics and Thermal-Hydraulics (NTH) aspects, simultaneously. In this way, for multi-objective optimization, the NTH parameters are included in the fitness function. Neutronic goals are focused on multiplication factor, power peaking factor, and power density and for TH, fuel temperature and critical heat flux are considered. In the present investigation, for evaluating the Fuzzy Gravitational Search Algorithm (FGSA), four cases have been studied. At the first step, to demonstrate the performance of proposed algorithm, the Ackley and Shekel Foxholes functions have been studied. In the next step, the FGSA algorithm with a multi-objective fitness function has been applied for two PWR reactors. For the NTH calculations, valid codes have been executed in searching iterations. The results reveal that convergence rate of the FGSA method is quite promising. Also, the FGSA improves the quality of multi objective LPO in average and could be accounted as a trustworthy method. (C) 2017 Elsevier B.V. All rights reserved.
机译:多目标加载模式优化(LPO)是核反应堆堆芯设计最重要的问题之一。因此,已经提出了不同的技术来优化核反应堆的堆芯模式,本文提出了一种新的优化技术,该技术使用模糊逻辑控制器(FLC)解决多目标优化问题。在这项工作中,使用FLC,可以控制重力搜索算法(GSA)的重力常数,以获得更好的优化结果和收敛速度。设计良好的反应堆堆芯燃料组件装载模式同时取决于中子学和热工学(NTH)方面。以这种方式,对于多目标优化,NTH参数包含在适应度函数中。中子学的目标集中在倍增系数,功率峰值系数和功率密度上,而对于TH,则考虑了燃料温度和临界热通量。在本研究中,为评估模糊引力搜索算法(FGSA),已研究了四种情况。第一步,为了证明所提出算法的性能,对Ackley和Shekel Foxholes函数进行了研究。下一步,具有多目标适应度函数的FGSA算法已应用于两个PWR反应堆。对于NTH计算,已经在搜索迭代中执行了有效代码。结果表明,FGSA方法的收敛速度是很有希望的。同样,FGSA平均提高了多目标LPO的质量,可以认为是值得信赖的方法。 (C)2017 Elsevier B.V.保留所有权利。

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