A RELAP5-3D/LSTM model for the analysis of drywell cooling fan failure

• Published in: Progress in nuclear energy (New series) Vol. 130; no. 1; p. 103540
• Main Authors: Guillen, D.P., Anderson, N., Krome, C., Boza, R., Griffel, L.M., Zouabe, J., Al Rashdan, A.Y.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.12.2020; Elsevier BV; Elsevier
• Subjects: Anomalies; Anomaly detection; Boiling water reactors; Coils; Cooling; drywell cooling; Drywell cooling fan; Failure analysis; Fan coil unit; Flow velocity; GENERAL STUDIES OF NUCLEAR REACTORS; Heat exchangers; Heat transfer; Hydraulic models; Information management; Inlet temperature; Long short-term memory; Machine learning; Management information systems; Nuclear power plants; Nuclear reactor containment; Nuclear reactors; Plant management; Process information; Reactors; recurrent neural network; Recurrent neural networks; RELAP5-3D; Simulation; Temperature sensors; thermal hydraulic; Thermal hydraulics; Three dimensional models; Time measurement; Time synchronization; RELAP5-3D; Drywell cooling fan; Process information; Machine learning; Long short-term memory; Fan coil unit; Thermal hydraulics; Anomaly detection
• ISSN: 0149-1970; 1878-4224
• DOI: 10.1016/j.pnucene.2020.103540

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.