On 3/14/2025, Johndel Baluyot Obra, Okamoto/Miwa lab, Department of Nuclear Engineering and Management,(M2), received the “ Student Presentation Award” at the Atomic Energy Society of Japan, Computational Science and Engineering Division.

〈Name of award and short explanation about the award〉

A student who gave an exemplary presentation serving as a model for others in the Computational Science and Engineering Division of the Atomic Energy Society of Japan is awarded.

〈About awarded research〉

Title: Development of LSTM models for the combined forecasting and diagnosis of PWR LOCA accident events

Abstract: The real-time diagnosis of loss of coolant accidents (LOCA) in a pressurized water reactor (PWR) by forecasting the progress of the accident and predicting its cause is important in aiding plant operators with their decision-making process and accident response strategy. In a previous work, the potential of Long Short-Term Memory (LSTM) models in accident diagnosis was established by predicting the extent of pipe damage and location of pipe break from simulated LOCA events that were generated from RELAP5/SCDAPSIM. In this study, a forecasting model was supplemented to the existing diagnostic model to develop an LSTM-based real-time LOCA accident forecasting and diagnosis tool. The combined forecasting and diagnosis model has better capabilities and less real-time prediction error compared to the diagnostic model alone.

〈Your impression & future plan〉

I am truly honored and grateful to receive this award from the esteemed Computational Science and Engineering Division of AESJ. Firstly, I would like to express my sincerest gratitude to my adviser, Dr. Shuichiro Miwa, for his guidance, support, and understanding. Secondly, I would also like to thank Prof. Koji Okamoto for leading the laboratory’s research endeavors. Finally, I thank my colleagues for continuously motivating me to pursue research and to produce quality outputs. This year, I am shifting my focus to publishing journals and I hope that I can contribute to the research output of the department and also in advancing the applications of AI in enhancing nuclear safety and accident diagnosis.