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A framework is developed based on different uncertainty quantification (UQ)techniques in order to assess validation and verification (V&V) metrics incomputational physics problems, in general, and computational fluid dynamics(CFD), in particular. The metrics include accuracy, sensitivity and robustnessof the simulator's outputs with respect to uncertain inputs and computationalparameters. These parameters are divided into two groups: based on thevariation of the first group, a computer experiment is designed, the data ofwhich may become uncertain due to the parameters of the second group. Toconstruct a surrogate model based on uncertain data, Gaussian processregression (GPR) with observation-dependent (heteroscedastic) noise structureis used. To estimate the propagated uncertainties in the simulator's outputsfrom first and also the combination of first and second groups of parameters,standard and probabilistic polynomial chaos expansions (PCE) are employed,respectively. Global sensitivity analysis based on Sobol decomposition isperformed in connection with the computer experiment to rank the parametersbased on their influence on the simulator's output. To illustrate itscapabilities, the framework is applied to the scale-resolving simulations ofturbulent channel flow using the open-source CFD solver Nek5000. Due to thehigh-order nature of Nek5000 a thorough assessment of the results' accuracy andreliability is crucial, as the code is aimed at high-fidelity simulations. Thedetailed analyses and the resulting conclusions can enhance our insight intothe influence of different factors on physics simulations, in particular thesimulations of wall-bounded turbulence.

An Uncertainty-Quantification Framework for Assessing Accuracy, Sensitivity, and Robustness in Computational Fluid Dynamics