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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

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2018, Doctor of Philosophy, Ohio State University, Nuclear Engineering.
Safety is the utmost important requirement in nuclear power plant operation. An approach to develop a real-time operator support tool (OST) for declaring 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 nuclear power plant (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. Two convolutional neural network (CNN) models are implemented to classify possible scenarios under two different labels. 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 event involving the loss of all AC power for a large pressurized water reactor. 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 from the Ph.D. dissertation work performed by Dr. Douglas Osborn. The OST is illustrated for a station blackout event in a pressurized water reactor for possible offsite dose outcomes at: 1) 2-mile area, 2) 10-mile area, 3) 2-mile boundary, and, 4) 10-mile boundary which are being considered as key locations for emergency response planning 4 days after release starts. Also, two meteorological conditions, historical and standard meteorology, are considered. Instead of random sampling from the total set, the scenarios are clustered based on their similarities using mean shift methodology. Two CNN models that are implemented label whether a scenario falls into Bin over 10rem or Bin 0-10rem. CNN1 model has an average of 87.19 percent accuracy among possible offsite dose outcomes considered with the maximum accuracy reaching up to 99.96 percent. CNN2 has better performance rate with the lowest accuracy as 92.79 percent. All case studies for 3 hours simulation after release starts have over 74.82 percent accuracy.
Tunc Aldemir, PhD (Advisor)
Alper Yilmaz, PhD (Advisor)
Richard Denning, PhD (Committee Member)
Carol Smidts, PhD (Committee Member)
124 p.

Recommended Citations

Citations

  • Lee, J. H. (2018). 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 [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1543069550674204

    APA Style (7th edition)

  • Lee, Ji Hyun. 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. 2018. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1543069550674204.

    MLA Style (8th edition)

  • Lee, Ji Hyun. "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." Doctoral dissertation, Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1543069550674204

    Chicago Manual of Style (17th edition)