Uncertainty and sensitivity analysis of early exposure results with the MACCS reactor accident consequence model

Helton, Jon C.; Johnson, Jay; Shiver, A.W.; Sprung, J.L.

doi:10.1016/0951-8320(95)00002-j

Cited by 25 publications

(5 citation statements)

References 37 publications

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“…Many sampling methods have been proposed, such as simple random sampling and LHS. LHS is a stratified sampling scheme, which can cover the input parameter space with a minimal number of samples and allows for the extraction of a large amount of uncertainty information from this relatively smaller number of samples [27]. Moreover, LHS can generate random samples from the ranges of all possible values, thus giving insight into the tails of the probability distributions.…”

Section: Uncertainty Analysismentioning

confidence: 99%

A Monte Carlo analysis of the effect of heat release rate uncertainty on available safe egress time

Kong¹,

Johansson²,

Hees³

et al. 2012

Journal of Fire Protection Engineering

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Available safe egress time is an important criterion to determine occupant safety in performance-based fire protection design of buildings. There are many factors affecting the calculation of available safe egress time, such as heat release rate, smoke toxicity and the geometry of the building. Heat release rate is the most critical factor. Due to the variation of fuel layout, initial ignition location and many other factors, significant uncertainties are associated with heat release rate. Traditionally, fire safety engineers prefer to ignore these uncertainties, and a fixed value of heat release rate is assigned based on experience. This makes the available safe egress time results subjective. To quantify the effect of uncertainties in heat release rate on available safe egress time, a Monte Carlo simulation approach is implemented for a case study of a single hypothetical fire compartment in a commercial building. First, the effect of deterministic peak heat release rate and fire growth rate on the predicted available safe egress time is studied. Then, the effect of uncertainties in peak heat release rate and fire growth rate are analyzed separately. Normal and log-normal distributions are employed to characterize peak heat release rate and fire growth rate, respectively. Finally, the effect of uncertainties in both peak heat release rate and fire growth rate on available safe egress time are analyzed. Illustrations are also provided on how to utilize probabilistic functions, such as the cumulative density function and complementary cumulative distribution function, to help fire safety engineers develop proper design fires.

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Section: Uncertainty Analysismentioning

confidence: 99%

A Monte Carlo analysis of the effect of heat release rate uncertainty on available safe egress time

Kong¹,

Johansson²,

Hees³

et al. 2012

Journal of Fire Protection Engineering

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show abstract

“…155 In addition, the NRC has used sampling-based uncertainty and sensitivity analysis procedures in a sequence of studies involving reactor accident consequence models. [156][157][158][159] Similar uncertainty propagation procedures were also used in the DOE's successful compliance certification application to the EPA for the Waste Isolation Pilot Plant. 61,62 A simple example is now presented using the following definition for D N (τ |e D ), which is introduced solely for illustrative purposes: is a vector of epistemically uncertain analysis inputs with the distributions specified in Table 2.…”

Section: Calculation Of Expected Dose and Display Of Epistemic Uncertmentioning

confidence: 99%

Illustration of sampling-based approaches to the calculation of expected dose in performance assessments for the proposed high level radioactive waste repository at Yucca Mountain, Nevada.

Helton

Sallaberry

2007

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A deep geologic repository for high level radioactive waste is under development by the U.S. Department of Energy at Yucca Mountain (YM), Nevada. As mandated in the Energy Policy Act of 1992, the U.S. Environmental Protection Agency (EPA) has promulgated public health and safety standards (i.e., 40 CFR Part 197) for the YM repository, and the U.S. Nuclear Regulatory Commission has promulgated licensing standards (i.e., 10 CFR Parts 2, 19, 20, etc.) consistent with 40 CFR Part 197 that the DOE must establish are met in order for the YM repository to be licensed for operation. Important requirements in 40 CFR Part 197 and 10 CFR Parts 2, 19, 20, etc. relate to the determination of expected (i.e., mean) dose to a reasonably maximally exposed individual (RMEI) and the incorporation of uncertainty into this determination. This presentation describes and illustrates how general and typically nonquantitive statements in 40 CFR Part 197 and 10 CFR Parts 2, 19, 20, etc. can be given a formal mathematical structure that facilitates both the calculation of expected dose to the RMEI and the appropriate separation in this calculation of aleatory uncertainty (i.e., randomness in the properties of future occurrences such as igneous and seismic events) and epistemic uncertainty (i.e., lack of knowledge about quantities that are poorly known but assumed to have constant values in the calculation of expected dose to the RMEI).

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“…Uncertainty quantification and global sensitivity analysis are an integral part of quantitative risk assessments (Apostolakis, 2004; Helton & Davis, 2002; Saltelli, 2002). Applications range from the quantification of early radiation exposure (Helton, Johnson, Shiver, & Sprung, 1995), to nuclear probabilistic safety assessment (Iman & Helton, 1991), to the performance assessment of waste repositories (Helton & Johnson, 2011; Helton & Sallaberry, 2009), to food safety assessment (Frey, 2002; Patil & Frey, 2004), and to the reliability analysis of mechanical systems (Urbina, Mahadevan, & Paez, 2011). The literature evidences the use of both local and global methods.…”

Section: Introductionmentioning

confidence: 99%

Global Sensitivity Analysis with Mixtures: A Generalized Functional ANOVA Approach

Borgonovo

Barr

et al. 2021

Risk Analysis

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This work investigates aspects of the global sensitivity analysis of computer codes when alternative plausible distributions for the model inputs are available to the analyst. Analysts may decide to explore results under each distribution or to aggregate the distributions, assigning, for instance, a mixture. In the first case, we lose uniqueness of the sensitivity measures, and in the second case, we lose independence even if the model inputs are independent under each of the assigned distributions. Removing the unique distribution assumption impacts the mathematical properties at the basis of variance‐based sensitivity analysis and has consequences on result interpretation as well. We analyze in detail the technical aspects. From this investigation, we derive corresponding recommendations for the risk analyst. We show that an approach based on the generalized functional ANOVA expansion remains theoretically grounded in the presence of a mixture distribution. Numerically, we base the construction of the generalized function ANOVA effects on the diffeomorphic modulation under observable response preserving homotopy regression. Our application addresses the calculation of variance‐based sensitivity measures for the well‐known Nordhaus' DICE model, when its inputs are assigned a mixture distribution. A discussion of implications for the risk analyst and future research perspectives closes the work.

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Uncertainty and sensitivity analysis of early exposure results with the MACCS reactor accident consequence model

Cited by 25 publications

References 37 publications

A Monte Carlo analysis of the effect of heat release rate uncertainty on available safe egress time

A Monte Carlo analysis of the effect of heat release rate uncertainty on available safe egress time

Illustration of sampling-based approaches to the calculation of expected dose in performance assessments for the proposed high level radioactive waste repository at Yucca Mountain, Nevada.

Global Sensitivity Analysis with Mixtures: A Generalized Functional ANOVA Approach

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