Quantification of uncertainty in the simulation results becomes difficult for complex real-world systems with little or no experimental data. This paper describes a validation and uncertainty quantification (VUQ) approach that integrates computational and experimental data through a range of experimental scales and a hierarchy of complexity levels. This global approach links dissimilar experimental datasets at different scales, in a hierarchy, to reduce quantified error bars on case with sparse data, without running additional experiments. This approach was demonstrated by applying on a real-world problem, greenhouse gas (GHG) emissions from wind tunnel flares. The two-tier validation hierarchy links, a buoyancy-driven helium plume and a wind tunnel flare, to increase the confidence in the estimation of GHG emissions from wind tunnel flares from simulations.
Multiscale Validation and Uncertainty Quantification for Problems With Sparse Data
Manuscript received August 2, 2016; final manuscript received January 26, 2017; published online February 9, 2017. Assoc. Editor: Jeffrey E. Bischoff.
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Jatale, A., Smith, P. J., Thornock, J. N., Smith, S. T., Spinti, J. P., and Hradisky, M. (February 9, 2017). "Multiscale Validation and Uncertainty Quantification for Problems With Sparse Data." ASME. J. Verif. Valid. Uncert. March 2017; 2(1): 011001. https://doi.org/10.1115/1.4035864
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