Effects of Non-Normal Input Distributions and Sampling Region on Monte Carlo Results

Abstract of the technical paper presented at:

ASME PVP 2018
July 15–20, 2018
Prague, Czech Republic

Prepared by:
K. Tsembelis, S. Eom, J. Jin and C. Cole
Canadian Nuclear Safety Commission

Abstract:

In order to address the risks associated with the operation of ageing pressure boundary components, many assessments incorporate probabilistic analysis methodologies for alleviating excessive conservatism of deterministic methodologies. In general, deterministic techniques utilize conservative upper bound values for all critical parameters. Recently, various probabilistic fracture mechanics (PFM) codes have been used to identify governing parameters which could affect the structural integrity of pressure retaining components. Moreover, these codes are used to calculate a probability of failure in order to estimate potential risks under operating conditions and design loading conditions for the pressure retaining components experiencing plausible and active degradation mechanisms.

Probabilistic approaches typically invoke the Monte Carlo (MC) approach where a set of critical input variables are randomly distributed and inserted in deterministic computer models. Estimates of results from probabilistic structural integrity assessments are then compared against 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.

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