  In the turbulence modeling community, significant efforts have been made to
quantify the uncertainties in the Reynolds-Averaged Navier--Stokes (RANS)
models and to improve their predictive capabilities. Of crucial importance in
these efforts is the understanding of the discrepancies in the RANS modeled
Reynolds stresses. However, to what extent these discrepancies can be predicted
or whether they are completely random remains a fundamental open question. In
this work we used a machine learning algorithm based on random forest
regression to predict the discrepancies. The success of the
regression--prediction procedure indicates that, to a large extent, the
discrepancies in the modeled Reynolds stresses can be explained by the mean
flow feature, and thus they are universal quantities that can be extrapolated
from one flow to another, at least among different flows sharing the same
characteristics such as separation. This finding has profound implications to
the future development of RANS models, opening up new possibilities for
data-driven predictive turbulence modeling.
