Achieving reasonable conservatism in nuclear safety analyses

• Multiple conservative assumptions can quickly diverge into extreme conservatism.
• Mathematics and attractive properties provide basis for wide use of lognormal distribution.
• Mean values are ideal candidates for representation of parameter uncertainties.
• Mean values are proposed as reasonably conservative estimates of parameter uncertainties.

In the absence of methods that explicitly account for uncertainties, seeking reasonable conservatism in nuclear safety analyses can quickly lead to extreme conservatism. The rate of divergence to extreme conservatism is often beyond the expert analysts’ intuitive feeling, but can be demonstrated mathematically. Too much conservatism in addressing the safety of nuclear facilities is not beneficial to society. Using certain properties of lognormal distributions for representation of input parameter uncertainties, example calculations for the risk and consequence of a fictitious facility accident scenario are presented. Results show that there are large differences between the calculated 95th percentiles and the extreme bounding values derived from using all input variables at their upper-bound estimates. Showing the relationship of the mean values to the key parameters of the output distributions, the paper concludes that the mean is the ideal candidate for representation of the value of an uncertain parameter. The mean value is proposed as the metric that is consistent with the concept of reasonable conservatism in nuclear safety analysis, because its value increases towards higher percentiles of the underlying positively skewed distribution with increasing levels of uncertainty. Insensitivity of the results to the actual underlying distributions is briefly demonstrated.