In this article, we propose a methodology based on cubature Kalman filtering (CKF) for simultaneous estimation of two important variables of nuclear reactors, viz. the neutron flux and the total core reactivity, from signals of delayed response self-powered neutron detectors (SPNDs). Moreover, by using the linearity of our model, the Rao-Blackwellized CKF (RBCKF) algorithm is developed for our estimation problem, which offers benefits in terms of smaller computational load in comparison to standard CKF. Furthermore, a scheme which is termed as adaptive-RBCKF (A-RBCKF) has been designed for adaptation of process noise covariance. A comparative study of the two state estimators, that is, RBCKF and A-RBCKF, through simulations under different transient scenarios demonstrates the efficacy of A-RBCKF over RBCKF.

中文翻译：

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核反应堆中子通量和反应性的同时估计

在本文中，我们提出了一种基于容积卡尔曼滤波 (CKF) 的方法，用于同时估计核反应堆的两个重要变量，即。中子通量和总堆芯反应性，来自延迟响应自供电中子探测器 (SPND) 的信号。此外，通过使用我们模型的线性，我们为我们的估计问题开发了 Rao-Blackwellized CKF (RBCKF) 算法，与标准 CKF 相比，它在计算量更小的方面提供了好处。此外，已经设计了一种称为自适应 RBCKF (A-RBCKF) 的方案来适应过程噪声协方差。通过在不同瞬态场景下的模拟，对两个状态估计器，即 RBCKF 和 A-RBCKF 的比较研究证明了 A-RBCKF 优于 RBCKF。