  This work determines the inaccuracy of using Reynolds averaged Navier Stokes
(RANS) turbulence models in transition to turbulent flow regimes by predicting
the model-based discrepancies between RANS and large eddy simulation (LES)
models and then incorporates the capabilities of machine learning algorithms to
characterize the discrepancies which are defined as a function of mean flow
properties of RANS simulations. First, three-dimensional CFD simulations using
k-omega Shear Stress Transport (SST) and dynamic one-equation subgrid-scale
models are conducted in a wall-bounded channel containing a cylinder for RANS
and LES, respectively, to identify the turbulent kinetic energy discrepancy.
Second, several flow features such as viscosity ratio, wall-distance based
Reynolds number, and vortex stretching are calculated from the mean flow
properties of RANS. Then these flow features are regressed onto the discrepancy
using a Random Forests regression algorithm. Finally, the discrepancy of the
test flow is predicted using the trained algorithm. The results reveal that a
significant discrepancy exists between RANS and LES simulations and ML
algorithm successfully predicts the increased model uncertainties caused by the
employment of k-omega SST turbulence model for transitional fluid flows.
