  A framework is developed based on different uncertainty quantification (UQ)
techniques in order to assess validation and verification (V&V) metrics in
computational physics problems, in general, and computational fluid dynamics
(CFD), in particular. The metrics include accuracy, sensitivity and robustness
of the simulator's outputs with respect to uncertain inputs and computational
parameters. These parameters are divided into two groups: based on the
variation of the first group, a computer experiment is designed, the data of
which may become uncertain due to the parameters of the second group. To
construct a surrogate model based on uncertain data, Gaussian process
regression (GPR) with observation-dependent (heteroscedastic) noise structure
is used. To estimate the propagated uncertainties in the simulator's outputs
from 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 is
performed in connection with the computer experiment to rank the parameters
based on their influence on the simulator's output. To illustrate its
capabilities, the framework is applied to the scale-resolving simulations of
turbulent channel flow using the open-source CFD solver Nek5000. Due to the
high-order nature of Nek5000 a thorough assessment of the results' accuracy and
reliability is crucial, as the code is aimed at high-fidelity simulations. The
detailed analyses and the resulting conclusions can enhance our insight into
the influence of different factors on physics simulations, in particular the
simulations of wall-bounded turbulence.
