Abstract A RELAP5-3D/LSTM model was created to analyze the failures of two drywell cooling fans at a nuclear power plant. A total of four fan coil units (FCUs) each comprised of a water-cooled heat exchanger and a centrifugal fan located in the drywell provide cooling via a closed nitrogen loop to the primary containment of the boiling water reactor. A Reactor Excursion and Leak Analysis Program (RELAP5-3D) thermal hydraulic model was created to simulate the steady-state normal operation of the FCUs. Historical data from the plant Process Information (PI) system was synchronized in time for a total of 33 Plant Management Information System (PMIS) tags per FCU representing: (1) the temperatures at various locations within the drywell, (2) inlet, outlet, and dewpoint temperatures at the FCUs, (3) reactor power, and (4) water coolant flowrate and temperature. Because the inlet temperature sensor for the two fans that failed did not provide consistent data prior to the failures, a long short-term memory (LSTM) recurrent neural network was trained to predict the FCU inlet temperature history based upon the states of the other valid PMIS points. RELAP5-3D simulations were performed using the measured FCU inlet temperatures, as well as the LSTM-generated temperatures, and the resulting FCU outlet temperatures were compared. The simulation results using the measured and predicted FCU inlet temperature were shown to be within 7.35% and 5.16%, respectively, of the values reported by the PI system. Thus, a viable approach has been demonstrated to predict the expected FCU outlet temperature. By comparing real-time measurements of FCU outlet temperature with predictions such as those presented here, off-normal operation can be readily detected. The use of RELAP5-3D with the LSTM results was successfully implemented to prototype a physics-based anomaly detection model for the drywell FCUs.

中文翻译：

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用于分析干井冷却风扇故障的 RELAP5-3D/LSTM 模型

摘要 创建了一个 RELAP5-3D/LSTM 模型来分析核电站两个干井冷却风扇的故障。总共四个风机盘管单元 (FCU)，每个单元由一个水冷热交换器和一个位于干井中的离心风机组成，通过封闭的氮气回路为沸水反应堆的主安全壳提供冷却。创建反应堆偏移和泄漏分析程序 (RELAP5-3D) 热工水力模型来模拟 FCU 的稳态正常运行。来自工厂过程信息 (PI) 系统的历史数据及时同步，每个 FCU 共有 33 个工厂管理信息系统 (PMIS) 标签，表示：(1) 干井内不同位置的温度，(2) 入口、出口和 FCU 的露点温度，(3) 反应堆功率，(4) 冷却水流量和温度。由于两个发生故障的风扇的入口温度传感器在发生故障之前没有提供一致的数据，因此训练了一个长短期记忆 (LSTM) 递归神经网络，以根据另一个有效风扇的状态预测 FCU 入口温度历史记录。 PMIS 积分。使用测量的 FCU 入口温度以及 LSTM 生成的温度执行 RELAP5-3D 模拟，并比较所得的 FCU 出口温度。使用测量的和预测的 FCU 入口温度的模拟结果显示分别在 PI 系统报告值的 7.35% 和 5.16% 以内。因此，已经证明了一种可行的方法来预测预期的 FCU 出口温度。通过将 FCU 出口温度的实时测量值与此处介绍的预测值进行比较，可以轻松检测到非正常运行。成功地将 RELAP5-3D 与 LSTM 结果一起用于干井 FCU 的基于物理的异常检测模型原型。