EFFECTS OF NON-NORMAL INPUT DISTRIBUTIONS AND SAMPLING REGION ON MONTE CARLO RESULTS

摘要

In order to address the risks associated with the operation of ageing pressure boundary components, many assessments incorporate probabilistic analysis tools for alleviating excessive conservatism of deterministic methodologies. In general, deterministic techniques utilize conservative bounding values for all critical parameters. Recently, various Probabilistic Fracture Mechanics (PFM) codes have been employed to identify governing parameters which could affect licensing basis margins of pressure retaining components. Moreover, these codes are used to calculate a probability of failure in order to estimate potential risks under operating and design loading conditions for the pressure retaining components experiencing plausible and active degradation mechanisms. Probabilistic approaches typically invoke the Monte-Carlo (MC) method where a set of critical input variables are randomly distributed and inserted in deterministic computer models. Estimates of results from probabilistic assessments are then compared against various assessment criteria. During the PVP-2016 conference, we investigated the assumption of normality of the Monte Carlo results utilizing a non-linear system function. In this paper, we extend the study by employing non-normal input distributions and investigating the effects of sampling region on the system function.