Prediction of Leak Flow Rate Using Fuzzy Neural Networks in Severe Post-LOCA Circumstances
Prediction of Leak Flow Rate Using Fuzzy Neural Networks in Severe Post-LOCA Circumstances
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DOI：
10.1109/tns.2014.2357583
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发表时间：
2014-12-01
影响因子：
1.8
通讯作者：
Kim, Chang-Hwoi
中科院分区：
文献类型：
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作者：
Kim, Dong Yeong;Yoo, Kwae Hwan;Kim, Chang-Hwoi
向核电厂 (NPP) 运行人员提供有关冷却剂损失事故 (LOCA) 引起的泄漏流量的信息，是管理和缓解主动安全注入系统无法启动的核电厂严重 LOCA 后情况的关键。泄漏流量是破裂尺寸、压差（即反应器容器内部和外部压力之间的差）、温度等的函数。在这项研究中，首先识别和预测破裂位置和大小，然后使用模糊神经网络（FNN）预测泄漏流量。 FNN 是使用训练数据开发的，并使用独立测试数据进行验证。这些数据是使用 MAAP4 代码对优化的动力反应堆 1000 (OPR1000) 进行模拟而生成的。采用减法聚类方法从获取的数据中选择训练FNN模型的数据，提高了FNN的性能。开发的 FNN 模型足够准确，可用于预测泄漏流量，这对于管理严重的 LOCA 后情况是有用的信息。
Providing information about the leak flow rate caused by a loss-of-coolant accident (LOCA) to nuclear power plant (NPP) operation personnel is a key to the management and mitigation of severe post-LOCA circumstances at NPPs where active safety injection systems do not actuate. The leak flow rate is a function of break size, differential pressure (i. e., difference between internal and external reactor vessel pressure), temperature, and so on. In this study, the break position and size were first identified and predicted, and then, the leak flow rate was predicted using a fuzzy neural network (FNN). The FNN was developed using training data and validated using independent test data. The data were generated from simulations of the optimized power reactor 1000 (OPR1000) using MAAP4 code. The data for training the FNN model were selected among the acquired data using the subtractive clustering method, and FNN performance was improved. The developed FNN model was sufficiently accurate to be used for predicting leak flow rate, which is useful information for managing severe post-LOCA situations.