Uncertainty Quantification in a Regulatory Environment

Vincent A. Mousseau () and Brian J. Williams ()
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Vincent A. Mousseau: Sandia National Laboratories
Brian J. Williams: Los Alamos National Laboratory, Statistical Sciences Group

Chapter 48 in Handbook of Uncertainty Quantification, 2017, pp 1613-1648 from Springer

Abstract: Abstract This chapter describes the use of the Predictive Capability Maturity Model (PCMM) (Oberkampf et al., Predictive capability maturity model for computational modeling and simulation. Technical report, SAND2007-5948, Sandia National Laboratories, 2007) applied to a nuclear reactor simulation. The application and PCMM will be discussed relative to review by the Nuclear Regulatory Commission. In a regulatory environment, one takes on the role of a lawyer presenting evidence to a judge with a prosecuting attorney allowed to cross-examine. In this type of “hostile” environment, a structured process that logically presents the evidence is helpful. In addition, many simulations are now multi-scale, multi-physics, and multi-code. For this level of complexity, it is easy to get lost in the details. The PCMM method has been adapted for this multi-physics multi-code software. Since the key is to provide the regulator with confidence that the software is capable of predicting the quantity of interest (QoI) with a well-quantified uncertainty, the PCMM approach is a natural solution.

Keywords: Bayesian calibration; Code scaling; applicability; and uncertainty (CSAU); Dittus-Boelter correlation; Expert opinion distributions; Graphical user interface (GUI); Joint distributons; Marginal distributions; Markov chain; Monte Carlo (MCMC); Neutronics; Numerical uncertainty; Phenomena Identification and Ranking Table (PIRT); Predictive Capability Maturity Model; (PCMM); Quantified Parameter Ranking Table (QPRT); Quantity of interest (QoI); Reynolds number; Uncertainty quantification (UQ); Wilks formula (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/978-3-319-12385-1_49

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