A simulation and machine learning informed diagnosis of the severe accidents

Abstract

We propose a simulation and machine learning informed model (SMLIM) for the diagnosis of severe accidents. A machine learning model which consisted of one hidden Long Short Term Memory (LSTM) layer and two dense layers with variations in the number of neurons and regularization parameters and an Adams optimizer was constructed for the multi-time step ahead forecasting analysis and the regression analysis. Using feature variables of lower plenum liquid level, core liquid level, reactor vessel pressure, and dry-well pressure, which were monitored during the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident, the target variable of the dry-well pressure in time was predicted. Training data representing the reference scenario was produced by a MELCOR simulation of the FDNPP accident at unit 1. The first test set obtained from the MELCOR simulation was chosen to consider the aleatoric uncertainty, while the second test set from the MELCOR simulation addresses the epistemic uncertainty. It is shown that the proposed SMLIM model can forecast the time series data with reasonable accuracy for the test cases selected. Then another test sets were prepared using the measurement data taken from the FDNPP. While the forecasted prediction was quite different from the actual measurement data, which were corrupted, the LSTM network model showed better agreement between the prediction and actual data when the measurement data were corrected. This trend was similar in the case of regression analysis. We suggest that the proposed simulation and machine learning informed model can help the operator forecast the future trend of accident progression and diagnose corrupted measurement signals.