In this article, we propose a data-driven methodology for combining the
solutions of a set of competing turbulence models. The individual model
predictions are linearly combined for providing an ensemble solution
accompanied by estimates of predictive uncertainty due to the turbulence model
choice. First, for a set of training flow configurations we assign to component
models high weights in the regions where they best perform, and vice versa, by
introducing a measure of distance between high-fidelity data and individual
model predictions. The model weights are then mapped into a space of features,
representative of local flow physics, and regressed by a Random Forests (RF)
algorithm. The RF regressor is finally employed to infer spatial distributions
of the model weights for unseen configurations. Predictions of new cases are
constructed as a convex linear combination of the underlying models solutions,
while the between model variance provides information about regions of high
model uncertainty. The method is demonstrated for a class of flows through the
compressor cascade NACA65 V103 at Re~3e5. The results show that the aggregated
solution outperforms the accuracy of individual models for the quantity used to
inform the RF regressor, and performs well for other quantities well-correlated
to the preceding one. The estimated uncertainty intervals are generally
consistent with the target high-fidelity data. The present approach then
represents a viable methodology for a more objective selection and combination
of alternative turbulence models in configurations of interest for engineering
practic