Abstract Extended Kalman filter (EKF) and cubature Kalman filter (CKF) are proposed to estimate the state parameters of an external neutron source driven sub-critical reactor, including power level, reactivity, external neutron source, six-groups of delayed neutrons precursor densities, equivalent fuel temperature, average coolant temperature and nuclear densities of iodine, xenon, promethium, samarium nuclides. Parameter settings and matters needed attention in EKF and CKF are also analyzed, especially the relationship between model prediction covariance matrix and measurement covariance matrix. In order to effectively identify the maneuvering of the external neutron source and reactivity on the uncertainty of the prediction model, two adaptive algorithms are proposed to adjust the covariance matrix of the prediction model online. The results show that these two adaptive algorithms can effectively detect various maneuvering such as neutron source variation and reactivity insertion, and realize the optimal estimation of the reactor state using EKF method. However, CKF has divergence and non-convergence. EKF achieves good results in all parameters estimation.

中文翻译：

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使用自适应卡尔曼滤波器的外部中子源驱动的亚临界堆芯状态估计

摘要 提出了扩展卡尔曼滤波器（EKF）和容积卡尔曼滤波器（CKF）来估计外中子源驱动亚临界反应堆的状态参数，包括功率电平、反应性、外中子源、六组延迟中子前驱体密度。 、等效燃料温度、平均冷却剂温度和碘、氙、钷、钐核素的核密度。还分析了EKF和CKF中的参数设置和注意事项，特别是模型预测协方差矩阵和测量协方差矩阵之间的关系。为了有效识别外部中子源的操纵和反应性对预测模型的不确定性，提出了两种自适应算法在线调整预测模型的协方差矩阵。结果表明，这两种自适应算法能够有效检测中子源变化和反应性插入等各种机动，并利用EKF方法实现反应堆状态的最优估计。但是，CKF 具有发散性和非收敛性。EKF 在所有参数估计中都取得了很好的效果。