When to separate uncertainties and when not to separate

Hofer, Ėduard

doi:10.1016/s0951-8320(96)00068-3

Cited by 49 publications

(38 citation statements)

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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%

“…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. This spread is approximated by the mapping [5][6][7][8][9][10][11][12][13][14][15] between epistemically uncertain analysis inputs and the rupture probability generated with the sample in Eq. (5)(6)(7)(8)(9)(10).…”

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

confidence: 99%

“…This spread is approximated by the mapping [5][6][7][8][9][10][11][12][13][14][15] between epistemically uncertain analysis inputs and the rupture probability generated with the sample in Eq. (5)(6)(7)(8)(9)(10). Once the indicated mapping is generated, the epistemic uncertainty in p R (t|e) can be displayed with cumulative and complementary cumulative distribution functions, and the effects of the individual elements of e on the uncertainty present in the estimate of p R (t|e) can be investigated with a variety of sensitivity analysis procedures.…”

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

confidence: 99%

See 3 more Smart Citations

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%

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

confidence: 99%

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

confidence: 99%

See 2 more Smart Citations

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

View full text Add to dashboard Cite

show abstract

“…[19][20][21] The significant dependence of uncertainty on the known information is also detailed in Figure 5. The decreasing level of known (or at least vague) information results in an increasing level of uncertainty.…”

mentioning

confidence: 99%

On uncertainties in simulations in engineering design: A statistical tolerance analysis application

2014

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Simulations not only facilitate new and unprecedented insights in highly sophisticated science areas, but also support product design in engineering in terms of improved functionality, cost and time issues. However, as a matter of fact, simulations examine limited excerpts of real systems with accompanying simplifications, abstractions and idealizations. Hence, there is a distinct need to be aware of upcoming risks in simulation outcomes caused by uncertainties. These influence every step of forward-thinking simulation design which is not only restrained by modeling practice but begins with reality perception itself.The intention of the paper is to embed an awareness of uncertainty in the context of simulation by linking major classes of uncertainty with uncertainties within simulations in engineering design. Besides the decisive inclusion of reality as the starting point, mathematic approaches are also used to understand how those uncertainty classes evolve through exponential knowledge creation of systems. The transfer to statistical tolerance analysis shall finally put emphasis on the practical classification of uncertainty, starting from data preparation via concept design and mathematical implementation to result depiction. In the end, the reader's conception of possible uncertainties in special simulation cases shall be sharpened which, vice versa, shall lead to even better and well-thought-out simulation outcomes.

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Violence Risk Assessment in Clinical Settings: Being Sure about Being Sure

Buchanan

2012

Behavioral Sci & The Law

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Psychiatrists and psychologists have available structured risk assessment instruments to assess the risk of patient violence. These instruments are also used to help make important legal decisions, including which prisoners will be evaluated for continued detention at the end of their sentence. The predictive validity of structured instruments has been demonstrated in operationally defined groups. Their application to individual cases has led to objections that the standard deviations for the risk categories generated by the instruments overlap significantly. This debate has paid insufficient attention to the differences between aleatory (statistical) and epistemic (degree of confirmation) approaches to uncertainty. The approach to uncertainty in psychiatric violence risk assessment is, of necessity, largely epistemic. Providing statistical data can only be part of establishing the precision of an estimate of the probability of someone acting violently.

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When to separate uncertainties and when not to separate

Cited by 49 publications

References 1 publication

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.

On uncertainties in simulations in engineering design: A statistical tolerance analysis application

Violence Risk Assessment in Clinical Settings: Being Sure about Being Sure

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