Deriving parameter probability density functions

Stephens, M. A.; Goodwin, B.W.; Andres, Terry

doi:10.1016/0951-8320(93)90094-f

Cited by 33 publications

(4 citation statements)

References 3 publications

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“…In general, the probabilistic method to propagate input uncertainty [11] is particularly suitable to be coupled with codes since it is based on the creation of a number of code runs with different uncertain input parameters to characterize the uncertainty of the output Figure Of Merits (FOMs), target of the analysis. The uncertain input parameters are characterized by a range of variation and a Probability Density Function (PDF) [12]. A random sampling (e.g.…”

Section: Probabilistic Methods To Propagate Input Uncertaintymentioning

confidence: 99%

Cold Leg LBLOCA uncertainty analysis using TRACE/DAKOTA coupling

Agnello

Maio

Bersano

et al. 2022

J. Phys.: Conf. Ser.

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Safety analyses for nuclear power plants were carried out in the past using a conservative approach. With the increase of the phenomenological knowledge, through experimental data, and computational power, it became possible to adopt best estimate thermal- hydraulic system codes to perform deterministic safety analyses. However, some uncertainties are still present in the models, correlations, initial and boundary conditions, etc. Therefore, it is fundamental to quantify the uncertainty of calculation. This approach is called “Best Estimate Plus Uncertainty” (BEPU). Among the available uncertainty analysis methodologies, the probabilistic method to propagate input uncertainty is widely adopted. In the present study, an uncertainty analysis of a cold leg large break loss of coolant accident in a generic PWR-900 MWe has been developed and it has been carried out coupling the best estimate thermal- hydraulic system code TRACE and the uncertainty quantification tool DAKOTA in the SNAP environment/architecture.

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Section: Probabilistic Methods To Propagate Input Uncertaintymentioning

confidence: 99%

Cold Leg LBLOCA uncertainty analysis using TRACE/DAKOTA coupling

Agnello

Maio

Bersano

et al. 2022

J. Phys.: Conf. Ser.

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show abstract

“…Modeling is the process of understanding processes, which attempts to predict responses and supports the decisionmaking process [105]. The initial conceptualization stage develops into a numerical or computational representation [106], which is the most important stage and where uncertainties may spread further during the rest of the modelling. Given that the features consisting of multiple factors and their dependencies are complex, they need to be simplified, where oversimplification can lead to the omission of essential details but undersimplification can result in an overly complex model [107].…”

Section: ) Limited Knowledgementioning

confidence: 99%

Scheduling Under Uncertainty for Industry 4.0 and 5.0

et al. 2022

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This article provides a review about how uncertainties in increasingly complex production and supply chains should be addressed in scheduling tasks. Uncertainty management will be particularly important in Industry 5.0 solutions that will require the close integration of operators and technical systems. To prepare for these challenging developments, this work reviews the sources of uncertainty and the scheduling algorithms that deal with the different types of models developed to handle the uncertain nature of the elements and the environment of complex technological systems. The paper not only identifies the challenges, but also the main building blocks that can help to manage and reduce uncertainties based on the I4.0 and I5.0 solutions. We hope that this study will serve as a starting point for R&D projects and algorithm developments, which will be needed primarily in the field of multi-agent, multistage and inverse optimizations.

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“…With regard to a system of interest, modelling is an attempt to understand processes, predict responses, evaluate management alternatives, and support the policy and decisionmaking process (Arhonditsis et al 2007). Modelling procedures vary according to the system of study and desired outcomes, though they invariably involve an initial conceptualisation stage, which is then developed into a numerical and/or computational representation (Stephens et al 1993). Simplifications and assumptions are usually necessary features of the structural process, since natural features and dependencies are complex and numerous.…”

Section: Model Uncertaintymentioning

confidence: 99%

Identifying Uncertainty in Environmental Risk Assessments: The Development of a Novel Typology and Its Implications for Risk Characterization

Skinner¹,

Rocks²,

Pollard³

et al. 2014

Human and Ecological Risk Assessment: An International Journal

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Environmental risk analysts need to draw from a clear typology of uncertainties when qualifying risk estimates and/or significance statements about risk. However, categorisations of uncertainty within existing typologies are largely overlapping, contradictory, and subjective, and many typologies are not designed with environmental risk assessments (ERAs) in mind. In an attempt to rectify these issues, this research provides a new categorisation of uncertainties based, for the first time, on the appraisal of a large subset of ERAs, namely 171 peer-reviewed environmental weight-of-evidence assessments. Using this dataset, a defensible typology consisting of seven types of uncertainty (data, language, system, extrapolation, variability, model, and decision) and 20 related sub-types is developed. Relationships between uncertainties and the techniques used to manage them are also identified and statistically evaluated. A highly preferred uncertainty management option is to take no action when faced with uncertainty, although where techniques are applied they are commensurate with the uncertainty in question. Key observations are applied in the form of guidance for dealing with uncertainty, demonstrated through ERAs of genetically modified higher plants in the EU. The presented typology and accompanying guidance will have positive implications for the identification, prioritisation, and management of uncertainty during risk characterisation.

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Deriving parameter probability density functions

Cited by 33 publications

References 3 publications

Cold Leg LBLOCA uncertainty analysis using TRACE/DAKOTA coupling

Cold Leg LBLOCA uncertainty analysis using TRACE/DAKOTA coupling

Scheduling Under Uncertainty for Industry 4.0 and 5.0

Identifying Uncertainty in Environmental Risk Assessments: The Development of a Novel Typology and Its Implications for Risk Characterization

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