Abstract Measurements from in-core Self Powered Neutron Detectors in the nuclear reactor serve as input to the Online Flux Mapping System which is used for effective monitoring and regulation of the nuclear reactor. It is important to optimize the positioning of in-core detectors given that placement at all desired locations is not pragmatic. In Advanced Heavy Water Reactor (AHWR), which uses thorium-based fuel and boiling light water as coolant, the neutronics and thermal hydraulics are strongly coupled. However, neutronically different regions of the core may be loosely coupled. The knowledge of the 3-D core neutron flux distribution is an important safety feature of online monitoring in AHWR. This paper proposes a data-driven approach based on in-core detector measurements to arrive at the number and optimal locations for in-core detectors in AHWR. K -means clustering algorithm has been applied to in-core detector measurements of AHWR to identify the linear relationship among the detectors and to group them into clusters. Detectors within the clusters are highly correlated with each other and one detector is sufficient to represent others in a cluster, resulting in optimization of the number and locations of in-core detectors. The proposed in-core detector layout has been tested for its effectiveness in flux reconstruction using a Flux Mapping Algorithm in test cases representing typical operational scenarios of AHWR.

中文翻译：

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使用聚类方法优化 AHWR 中的磁通映射堆芯探测器位置

摘要 核反应堆堆芯自供电中子探测器的测量结果作为在线通量测绘系统的输入，用于对核反应堆进行有效监测和调节。鉴于放置在所有所需位置并不实用，因此优化堆芯探测器的定位非常重要。在使用钍基燃料和沸腾的轻水作为冷却剂的先进重水反应堆 (AHWR) 中，中子学和热工水力是强耦合的。然而，核心的中子不同区域可能是松散耦合的。3-D 堆芯中子通量分布的知识是 AHWR 在线监测的一个重要安全特征。本文提出了一种基于堆内探测器测量的数据驱动方法，以得出 AHWR 中堆内探测器的数量和最佳位置。K均值聚类算法已应用于AHWR的核心探测器测量，以识别探测器之间的线性关系并将它们分组。集群内的检测器彼此高度相关，一个检测器足以代表集群中的其他检测器，从而优化核心检测器的数量和位置。已使用通量映射算法在代表 AHWR 典型操作场景的测试案例中测试了建议的堆芯探测器布局在通量重建方面的有效性。K均值聚类算法已应用于AHWR的核心探测器测量，以识别探测器之间的线性关系并将它们分组。集群内的检测器彼此高度相关，一个检测器足以代表集群中的其他检测器，从而优化核心检测器的数量和位置。已使用通量映射算法在代表 AHWR 典型操作场景的测试案例中测试了建议的堆芯探测器布局在通量重建方面的有效性。K均值聚类算法已应用于AHWR的核心探测器测量，以识别探测器之间的线性关系并将它们分组。集群内的检测器彼此高度相关，一个检测器足以代表集群中的其他检测器，从而优化核心检测器的数量和位置。已使用通量映射算法在代表 AHWR 典型操作场景的测试案例中测试了建议的堆芯探测器布局在通量重建方面的有效性。