A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing

An overview of a comprehensive framework is given for estimating the predictive uncertainty of scientific computing applications. The framework is comprehensive in the sense that it treats both types of uncertainty (aleatory and epistemic), incorporates uncertainty due to the mathematical form of th...

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Vydáno v:	Computer methods in applied mechanics and engineering Ročník 200; číslo 25; s. 2131 - 2144
Hlavní autoři:	Roy, Christopher J., Oberkampf, William L.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Kidlington Elsevier B.V 15.06.2011 Elsevier
Témata:	Applied sciences Compressible flows; shock and detonation phenomena Computation Computational methods in fluid dynamics Computational simulation Computer science; control theory; systems Computer simulation Discretization Errors Estimating Exact sciences and technology Fluid dynamics Fundamental areas of phenomenology (including applications) Mathematical models Modeling Physics Propagation Simulation Software Supersonic and hypersonic flows Uncertainty Uncertainty quantification Validation Verification Validation Uncertainty quantification Verification Modeling Computational simulation Uncertainty Error estimation Quantization Probability distribution Numerical approximation Interval analysis Uncertain system Model matching Discretization Incomplete information Random variable Experimental data Probabilistic approach Computational fluid dynamics Decision making Wind tunnel test Conversion Extrapolation Scientific computation Statistical model Approximation error Artificial intelligence
ISSN:	0045-7825, 1879-2138
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Popis
Shrnutí:	An overview of a comprehensive framework is given for estimating the predictive uncertainty of scientific computing applications. The framework is comprehensive in the sense that it treats both types of uncertainty (aleatory and epistemic), incorporates uncertainty due to the mathematical form of the model, and it provides a procedure for including estimates of numerical error in the predictive uncertainty. Aleatory (random) uncertainties in model inputs are treated as random variables, while epistemic (lack of knowledge) uncertainties are treated as intervals with no assumed probability distributions. Approaches for propagating both types of uncertainties through the model to the system response quantities of interest are briefly discussed. Numerical approximation errors (due to discretization, iteration, and computer round off) are estimated using verification techniques, and the conversion of these errors into epistemic uncertainties is discussed. Model form uncertainty is quantified using (a) model validation procedures, i.e., statistical comparisons of model predictions to available experimental data, and (b) extrapolation of this uncertainty structure to points in the application domain where experimental data do not exist. Finally, methods for conveying the total predictive uncertainty to decision makers are presented. The different steps in the predictive uncertainty framework are illustrated using a simple example in computational fluid dynamics applied to a hypersonic wind tunnel.
Bibliografie:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23
ISSN:	0045-7825 1879-2138
DOI:	10.1016/j.cma.2011.03.016