Efficient Simulation of Risk and Performance Measures, With Applications to the Design and Operation of Nuclear Power Plants

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This award provides funding for the development of efficient simulation methods for estimating risk measures and other performance metrics. A primary focus of the research is on variance-reduction techniques (VRTs) for estimating a quantile and constructing confidence intervals for it. Quantiles are commonly employed to measure risk in a diverse range of application areas. For example, Nuclear Regulatory Commission (NRC) regulations sometimes specify that risk is measured using a "95/95 criterion," which requires computing a 95% confidence interval for a 0.95-quantile. To satisfy the NRC requirements, practitioners currently resort to crude Monte Carlo, which can be hugely inefficient, so the research on constructing confidence intervals for quantiles with VRTs has the potential for altering the way uncertainty and safety analyses are performed in the nuclear industry. In finance, where a quantile is known as a "value-at-risk," there are banking rules for capital requirements given as a function of a 0.99-quantile. Other fields in which the research on efficient quantile estimation can be applied include project planning, telecommunications, manufacturing, supply chains, and computing. If successful, the research will yield new simulation methodologies to increase efficiency, sometimes by orders of magnitude, of estimators of quantiles and confidence intervals for them. The construction of confidence intervals when applying VRTs will involve developing new estimators for the variance constant appearing in the VRT quantile estimator?s central limit theorem. Moreover, other techniques for building confidence intervals for quantiles also will be investigated.