Dissertation Defense: Development of a Tool to Assist the Nuclear Power Plant Operator in Declaring a State of Emergency Based on the Use of Dynamic Event Trees and Deep Learning Tools

Committee Members

• Tunc Aldemir, Chair (NE)
• Carol Smidts (NE),
• Alper Yilmaz (CSE)
• Richard Denning

Abstract

Safety is the utmost important requirement in nuclear power plant operation. A new approach that may be used as a real-time emergency guideline to support the declaration of a site emergency is proposed in this study. Temporal behavior of the early stages of a severe accident can be used to project the likelihood of different levels of offsite release of radionuclides based on the results of accident simulations with severe accident codes. Depending on the severity of the accident and the potential magnitude of the release of radioactive material to the environment, an offsite emergency response such as evacuation or sheltering may be warranted. The approach is based on the simulation of the possible NPP behavior following an initiating event and projects the likelihood of different levels of offsite release of radionuclides from the plant using deep learning (DL) techniques. Training of the DL process is accomplished using results of a large number of scenarios generated with the ADAPT/MELCOR/RASCAL computer codes to simulate the variety of possible consequences following a station blackout (SBO) event involving the loss of all AC power for a large pressurized water reactor (PWR). The ability of the model to predict the likelihood of different levels of consequences is assessed using a separate test set of MELCOR/RASCAL calculations. The set of data to be used in training and testing the machine were obtained previously in the Ph.D. dissertation work performed by Dr. Douglas Osborn. An initiating event that disrupts regular nuclear power plant (NPP) operation can result in a variety of different scenarios as time progresses depending on the response of standby safety systems, operator actions to bring the NPP plant to a safe and stable state, or the uncertainties in accident phenomenology. An approach that could be used for real-time emergency guidance to support the declaration of a site emergency and to guide off-site response is presented using observable plant data in the early stages of a severe accident.