Application of a Verification, Validation and Uncertainty Quantification Framework to a Turbulent Buoyant Helium Plume

2015 ◽  
Vol 95 (1) ◽  
pp. 143-168 ◽  
Author(s):  
Anchal Jatale ◽  
Philip J. Smith ◽  
Jeremy N. Thornock ◽  
Sean T. Smith ◽  
Jennifer P. Spinti ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Helium Plume

A Collaboration-based Approach to CFD Model Validation and Uncertainty Quantification (VUQ) Using Data from a Laminar Helium Plume

2016 ◽  
Vol 97 (2) ◽  
pp. 427-449
Author(s):  
Weston M. Eldredge ◽  
Pál Tóth ◽  
Laurie Centauri ◽  
Eric G. Eddings ◽  
Kerry E. Kelly ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Model Validation ◽  
Cfd Model ◽  
Using Data ◽  
Helium Plume

Multiscale Validation and Uncertainty Quantification for Problems With Sparse Data

2017 ◽  
Vol 2 (1) ◽  
Author(s):  
Anchal Jatale ◽  
Philip J. Smith ◽  
Jeremy N. Thornock ◽  
Sean T. Smith ◽  
Jennifer P. Spinti ◽  
...  
Keyword(s):  
Experimental Data ◽  
Wind Tunnel ◽  
Uncertainty Quantification ◽  
Real World ◽  
Ghg Emissions ◽  
Sparse Data ◽  
World Systems ◽  
Global Approach ◽  
Real World Problem ◽  
Helium Plume

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.

INVERSE UNCERTAINTY QUANTIFICATION OF A CELL MODEL USING A GAUSSIAN PROCESS METAMODEL

2020 ◽  
Vol 10 (4) ◽  
pp. 333-349
Author(s):  
Kevin de Vries ◽  
Anna Nikishova ◽  
Benjamin Czaja ◽  
Gábor Závodszky ◽  
Alfons G. Hoekstra
Keyword(s):  
Gaussian Process ◽  
Uncertainty Quantification ◽  
Cell Model ◽  
A Cell
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