Prediction of small-scale leak flow rate in LOCA situations using bidirectional GRU
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초록
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 smallscale 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.
Abstract
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 smallscale 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.
참고문헌 (18)
- "Operational performance information system for nuclear power plant, Nucl. Accid. Fail. status" (2024)[URL]
- T. S. Kwon, "Development of an unidentified RCS leakage detection sensor system less than 0. 5 gpm", KSFM J. Fluid Mach, 24, pp.13~19 (2021)
- T. S. Kwon, "Development of a RCS coolant leak detection system for reactor safety enhancement", KSFM J. Fluid Mach, 26, pp.122~128 (2023)
- J. Zhang, "Prediction of LBB leakage for various conditions by genetic neural network and genetic algorithms", Nucl. Eng. Des, 325, pp.33~43 (2017)
- M. Saghafi, "Real-time estimation of break sizes during LOCA in nuclear power plants using NARX neural network", Nucl. Eng. Technol, 51, pp.702~708 (2019)
- M. Liu, "Diagnosis of break size and location in LOCA and SGTR accidents using support vector machines", Prog. Nucl. Energy, 140, pp.103902~ (2021)
- F. S. M. Desterro, "Prediction of LOCA’s break size and location based on random forest and multi tasking deep neural network", Nucl. Eng. Des, 415, pp.112711~ (2023)
- J. She, "Diagnosis and prediction for loss of coolant accidents in nuclear power plants using deep learning methods", Front. Energy Res, 9, pp.665262~ (2021)
- J. H. Park, "Leak flow prediction during loss of coolant accidents using deep fuzzy neural networks", Nucl. Eng. Technol, 53, pp.2546~2555 (2021)
- C. Fukun, "GRU-CNN-based prediction of LOCA accident condition in nuclear power plants", International Conference on System Reliability and Safety Engineering (SRSE) (2022)
- S. Gong, "Multivariate time series prediction for loss of coolant accidents with a zigmoid-based LSTM", Front. Energy Res, 10, pp.852349~ (2022)
- F. Fahim, "A deep learning method to predict loss of coolant accident in nuclear power plants", 2023 11th International Symposium on Digital Forensics and Security (ISDFS) (2023)
- M. Kaminski, "Time-series forecasting of a typical PWR undergoing large break LOCA", Sci. Technol. Nucl. Install, 2024, pp.6162232~ (2024)
- BMAAP4 Applications Guidance, "EPRI", Electric Power Research Institute, ~, pp.Final Report 1020236 (2010)
- S. Hochreiter, "Long short-term memory", Neural Comput, 9(8), pp.1735~1780 (1997)
- K. Cho, "Learning phrase representations using RNN encoder-decoder for statistical machine translation", Conference on Empirical Methods in Natural Language Processing
- M. Schuster, "Bidirectional recurrent neural networks", IEEE Trans. Signal Process, 45(11), pp.2673~2681 (1997)
- L. Li, "Hyperband : a novel bandit-based approach to hyperparameter optimization", J. Mach. Learn. Res, 18, pp.1~52 (2018)