  Turbulent flows play an important role in many scientific and technological
design problems. Both Sub-Grid Scale (SGS) models in Large Eddy Simulations
(LES) and Reynolds Averaged Navier Stokes (RANS) based modeling will require
turbulence models for computational research of turbulent flows in the future.
Turbulence model-based simulations suffer from a multitude of causes of
forecast uncertainty. For example, the simplifications and assumptions employed
to make these turbulence models computationally tractable and economical lead
to predictive uncertainty. For safety-critical engineering design applications,
we need reliable estimates of this uncertainty. This article focuses on
Uncertainty Quantification (UQ) for Computational Fluid Dynamics (CFD)
simulations. We review recent advances in the estimate of many types of
uncertainty components, including numerical, aleatoric, and epistemic. We go
into further depth on the possible use of Machine Learning (ML) methods to
quantify these uncertainties. Above all, we elaborate further on significant
limitations in these techniques. These range from realizability constraints on
the Eigenspace Perturbation Method (EPM) to the requirement for Monte Carlo
(MC) approaches for mixed uncertainty. Based on this study, we pinpoint
important problems that need to be addressed and offer focused solutions to
move beyond these obstacles.
