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dc.contributor.authorSchroeder, Benjamin B.
dc.contributor.authorSilva, III, Humberto
dc.contributor.authorSmith, Kyle D.
dc.date.accessioned2019-09-18T09:04:04Z
dc.date.available2019-09-18T09:04:04Z
dc.date.copyright2/22/2019 12:00:00 AM
dc.date.issued2019
dc.identifier.issn2377-2158
dc.identifier.othervvuq_003_03_031006.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4258470
dc.description.abstractWhen making computational simulation predictions of multiphysics engineering systems, sources of uncertainty in the prediction need to be acknowledged and included in the analysis within the current paradigm of striving for simulation credibility. A thermal analysis of an aerospace geometry was performed at Sandia National Laboratories. For this analysis, a verification, validation, and uncertainty quantification (VVUQ) workflow provided structure for the analysis, resulting in the quantification of significant uncertainty sources including spatial numerical error and material property parametric uncertainty. It was hypothesized that the parametric uncertainty and numerical errors were independent and separable for this application. This hypothesis was supported by performing uncertainty quantification (UQ) simulations at multiple mesh resolutions, while being limited by resources to minimize the number of medium and high resolution simulations. Based on this supported hypothesis, a prediction including parametric uncertainty and a systematic mesh bias is used to make a margin assessment that avoids unnecessary uncertainty obscuring the results and optimizes use of computing resources.
dc.publisherAmerican Society of Mechanical Engineers (ASME)
dc.titleSeparability of Mesh Bias and Parametric Uncertainty for a Full System Thermal Analysis
dc.typeJournal Paper
dc.journal.volume3
dc.journal.issue3
dc.journal.titleJournal of Verification, Validation and Uncertainty Quantification
dc.identifier.doi10.1115/1.4042815
dc.journal.fristpage31006
dc.journal.lastpage031006-12
dc.treeJournal of Verification, Validation and Uncertainty Quantification:;2019:;volume( 003 ):;issue: 003
dc.contenttypeFulltext


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