Multiscale Validation and Uncertainty Quantification for Problems With Sparse Data

Abstract

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.