Abstract
The MAAP5.04 code uncertainty analysis was carried out for the Power Burst Facility Severe Fuel Damage Test 1-4. Comparisons between experimental data and analysis results were focused on hydrogen generation. The uncertainty propagation analysis was conducted through random variations of input uncertainty parameters of phenomenological models whose ranges were determined by the MAAP5 Zion parameter file. The time series clustering technique using the mean-shift algorithm was applied to the data set generated by the uncertainty propagation analysis. It was confirmed that the code predicted well the hydrogen mass generated and the uncertainty bounds of the analysis included the measured hydrogen generation history. The time series clustering technique demonstrated that the key model parameters could be identified for classifying the uncertainty analysis results using a decision tree classifier. Furthermore, the regression models were constructed which predict the uncertainty of the hydrogen generation from the model uncertainty parameters by using the support vector regressions. The hold-out method of cross validation was applied to the regression models of the hydrogen generation to investigate the training error and the test error for the uncertainty prediction of the hydrogen generation.
