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Calibrating a Reynolds-averaged Navier-Stokes (RANS) model against data leadsto an improvement. Determining {\it a priori} if such an improvementgeneralizes to flows outside the calibration data is an outstanding challenge.This work attempts to address this challenge via global epistemic UncertaintyQuantification (UQ). Unlike the available epistemic UQ methods that are localand tell us a model's uncertainty at one specific flow condition, the globalepistemic UQ method presented in this work tells us also whether a perturbationof the original model would generalize. Specifically, the global epistemic UQmethod evaluates a potential improvement in terms of its "effectiveness" and"inconsistency". Any improvement can be put in one of the following fourquadrants: first, high effectiveness, low inconsistency; second, higheffectiveness, high inconsistency; third, low effectiveness, low inconsistency;and fourth, low effectiveness, high inconsistency. An improvement wouldgeneralize if and only if it is in the high effectiveness and low inconsistencyquadrant. To demonstrate the concept, we apply the global epistemic UQ to fullReynolds stress modeling of a stratified shear layer. The global epistemic UQresults point to a model coefficient in the pressure-strain correlation closure(among others) as effective and consistent for predicting the quantity ofinterest of shear layer's growth. We calibrate the model coefficient such thatour RANS matches direct numerical simulation data at one flow condition. Weshow that the calibrated model generalizes to several other test flowconditions. On the other hand, when calibrating a high inconsistency term, weget a model that works at only the calibrated condition.

Determining a priori a RANS model's applicable range via global epistemic uncertainty quantification