It is difficult to detect a small-scale leakage in a nuclear power plant (NPP) quickly and take appropriate action. Delaying these procedures can have adverse effects on NPPs. In this paper, we propose leak flow rate prediction using the bidirectional gated recurrent unit (Bi-GRU) method to detect leakage quickly and accurately in small-scale leakage situations because large-scale leak rates are known to be predicted accurately. The data were acquired by simulating small loss-of-coolant accidents (LOCA) or small-scale leakage situations using the modular accident analysis program (MAAP) code. In addition, to improve prediction performance, data were collected by distinguishing the break sizes in more detail. In addition, the prediction accuracy was improved by performing both LOCA diagnosis and leak flow rate prediction in small LOCA situations. The prediction model developed using the Bi-GRU showed a superior prediction performance compared with other artificial intelligence methods. Accordingly, the accurate and effective prediction model for small-scale leakage situations proposed herein is expected to support operators in decision-making and taking actions.

中文翻译：

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使用双向 GRU 预测 LOCA 情况下的小规模泄漏流量

快速发现核电站（NPP）中的小规模泄漏并采取适当行动是很困难的。延迟这些程序可能会对核电厂产生不利影响。在本文中，我们提出使用双向门控循环单元（Bi-GRU）方法进行泄漏流量预测，以在小规模泄漏情况下快速准确地检测泄漏，因为已知大规模泄漏率可以准确预测。这些数据是通过使用模块化事故分析程序 (MAAP) 代码模拟小型冷却剂流失事故 (LOCA) 或小规模泄漏情况来获取的。此外，为了提高预测性能，通过更详细地区分断裂尺寸来收集数据。此外，通过在小LOCA情况下同时进行LOCA诊断和泄漏流量预测，提高了预测精度。与其他人工智能方法相比，使用 Bi-GRU 开发的预测模型表现出优越的预测性能。因此，本文提出的小规模泄漏情况准确有效的预测模型有望为操作人员决策和采取行动提供支持。