Guest editorial: treatment of aleatory and epistemic uncertainty in performance assessments for complex systems

Helton, Jon C.; Burmaster, David E.

doi:10.1016/s0951-8320(96)00066-x

Cited by 212 publications

(88 citation statements)

References 19 publications

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“…In the design and implementation of analyses for complex systems, it is useful to distinguish between two types of uncertainty: aleatory uncertainty and epistemic uncertainty [4][5][6][7][8][9][10][11][12][13][14][15][16].…”

Section: Treatment Of Uncertaintymentioning

confidence: 99%

“…This question is answered by the epistemic uncertainty present in the pipe rupture probability p R (t|e) defined in Eq. (5)(6)(7)(8)(9)(10)(11)(12)(13). Specifically, the spread in the values for p R (t|e) that results from the possible values for e characterizes the uncertainty in an estimate of the probability of pipe rupture.…”

Section: Q2 What Is the Probability Of Pipe Rupture?mentioning

confidence: 99%

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Development, analysis, and evaluation of a commercial software framework for the study of Extremely Low Probability of Rupture (xLPR) events at nuclear power plants.

Kalinich¹,

Helton²,

Sallaberry³

et al. 2010

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Sandia National Laboratories (SNL) participated in a Pilot Study to examine the process and requirements to create a software system to assess the extremely low probability of pipe rupture (xLPR) in nuclear power plants. This project was tasked to develop a prototype xLPR model leveraging existing fracture mechanics models and codes coupled with a commercial software framework to determine the framework, model, and architecture requirements appropriate for building a modular-based code. The xLPR pilot study was conducted to demonstrate the feasibility of the proposed developmental process and framework for a probabilistic code to address degradation mechanisms in piping system safety assessments. The pilot study includes a demonstration problem to assess the probability of rupture of DM pressurizer surge nozzle welds degraded by primary water stress-corrosion cracking (PWSCC). The pilot study was designed to define and develop the framework and model; then construct a prototype software system based on the proposed model. The second phase of the project will be a longer term program and code development effort focusing on the generic, primary piping integrity issues (xLPR code). The results and recommendations presented in this report will be used to help the U.S. Nuclear Regulatory Commission (NRC) define the requirements for the longer term program.iv

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Section: Treatment Of Uncertaintymentioning

confidence: 99%

Section: Q2 What Is the Probability Of Pipe Rupture?mentioning

confidence: 99%

Development, analysis, and evaluation of a commercial software framework for the study of Extremely Low Probability of Rupture (xLPR) events at nuclear power plants.

Kalinich¹,

Helton²,

Sallaberry³

et al. 2010

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show abstract

“…Within this framework, it is useful to distinguish two fundamentally different types of uncertainties, namely epistemic and aleatory uncertainties. This distinction has been considered for risk assessment of technical systems (Apostolakis 1990, Helton andBurmaster 1996), and increasingly for natural hazards (Hall 2003, Apel et al 2004, Straub and Der Kiureghian 2007, but has been discussed also for general geological applications by Mann (1993). Aleatory uncertainties are interpreted as random uncertainties, which, for a given model, are inherent to the considered process; epistemic uncertainties are related to our incomplete knowledge of the process, often because of limited data.…”

Section: Modeling Uncertainty In Rock-fall Hazardsmentioning

confidence: 99%

Modeling and managing uncertainties in rock-fall hazards

Straub

Schubert

2008

Georisk: Assessment and Management of Risk for Engineered Syste

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The assessment of rock-fall hazards is subject to significant uncertainty, which is not fully considered in general practice and research. This paper reviews and classifies the various sources of the uncertainty. Taking a generic framework for risk assessment as source, a probabilistic model is presented that consistently combines the different types of uncertainties, in order to obtain a unified estimate of rock-fall risk. An important aspect of the model is that it allows for incorporating all available information, including physical and empirical models, observations and expert knowledge, by means of Bayesian updating. Detailed formulations are developed for various types of information. Finally, two examples considering rock-fall risk on roads, with and without protection structures, illustrate the application of the probabilistic modeling framework to practical problems.

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“…Aleatory uncertainty describes the inherent variation of the physical system or environment under study. Epistemic uncertainty is a potential inaccuracy that is due to a lack of knowledge (Hoffman and Hammonds 1994;Helton and Burmaster 1996). The aleatory uncertainties are generally modeled as random variables with probability theory, while the epistemic ones are treated with non-probabilistic approaches, e.g.…”

Section: Introductionmentioning

confidence: 99%

A surrogate based multistage-multilevel optimization procedure for multidisciplinary design optimization

Yao

Chen

Ouyang

et al. 2011

Struct Multidisc Optim

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Optimization procedure is one of the key techniques to address the computational and organizational complexities of multidisciplinary design optimization (MDO). Motivated by the idea of synthetically exploiting the advantage of multiple existing optimization procedures and meanwhile complying with the general process of satellite system design optimization in conceptual design phase, a multistage-multilevel MDO procedure is proposed in this paper by integrating multiple-discipline-feasible (MDF) and concurrent subspace optimization (CSSO), termed as MDF-CSSO. In the first stage, the approximation surrogates of high-fidelity disciplinary models are built by disciplinary specialists independently, based on which the single level optimization procedure MDF is used to quickly identify the promising region and roughly locate the optimum of the MDO problem. In the second stage, the disciplinary specialists are employed to further investigate and improve the baseline design obtained in the first stage with highfidelity disciplinary models. CSSO is used to organize the concurrent disciplinary optimization and system coordination so as to allow disciplinary autonomy. To enhance the reliability and robustness of the design under uncertainties, the probabilistic version of MDF-CSSO (PMDF-CSSO) is developed to solve uncertainty-based optimization problems. The effectiveness of the proposed methods is verified with one MDO benchmark test and one practical satellite conceptual design optimization problem, followed by conclusion remarks and future research prospects.

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Guest editorial: treatment of aleatory and epistemic uncertainty in performance assessments for complex systems

Cited by 212 publications

References 19 publications

Development, analysis, and evaluation of a commercial software framework for the study of Extremely Low Probability of Rupture (xLPR) events at nuclear power plants.

Development, analysis, and evaluation of a commercial software framework for the study of Extremely Low Probability of Rupture (xLPR) events at nuclear power plants.

Modeling and managing uncertainties in rock-fall hazards

A surrogate based multistage-multilevel optimization procedure for multidisciplinary design optimization

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