Efficient uncertainty analyses using fast probability integration Original Research Article

Fast probability integration (FPI) algorthms are adapted, extended and used to perform nuclear engineering uncertainty analyses. Methods are presented to improve the efficiency and precision of FPI for frequently encountered input distributions, to permit quick estimates of extreme model output quantiles and to provide appropriate sensitivity and uncertainty importance measures. Advantages and disadvantages of FPI as a stand-alone method are explored in two demonstration applications. FPI is first applied to estimate extreme (0.95, 0.99, 0.999 and 0.9999) quantiles of the frequency of a dominant station blackout accident. Sensitivity measures are quantified to indicate how the relative importance of each model input changes at progressively more extreme output quantiles. FPI is next applied to analyze uncertainties in fire damage times predicted by compbrn iiie. Alternative FPI-based uncertainty importance measures are compared and shown to consistently rank the input contributions to the output uncertainty. FPI results are compared with results obtained from Monte Carlo sampling to demonstrate the computational advantage that clearly favors FPI, especially at the more extreme output quantiles.