Separability of Mesh Bias and Parametric Uncertainty for a Full System Thermal Analysis
Author(s) -
Benjamin Schroeder,
Humberto Silva,
Kyle Smith
Publication year - 2018
Language(s) - English
Resource type - Conference proceedings
DOI - 10.1115/vvs2018-9339
Subject(s) - uncertainty quantification , parametric statistics , uncertainty analysis , computer science , credibility , propagation of uncertainty , measurement uncertainty , workflow , monte carlo method , sensitivity analysis , aerospace , algorithm , data mining , simulation , aerospace engineering , mathematics , engineering , statistics , machine learning , database , political science , law
When making computational simulation predictions of multi-physics 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 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 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 are used to make a margin assessment that avoids unnecessary uncertainty obscuring the results and optimizes computing resources.
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