Numerical simulations based on Reynolds averaged Navier-Stokes models are routinely used in the analysis and design of turbomachinery components. It is however known that RANS closures are fundamentally limited in their ability to represent turbulent processes — introducing epistemic model-form uncertainties in the predictions. These are compounded by variability (aleatory uncertainties) introduced by limited information regarding, for example, the operating upstream conditions of turbo machinery components. In this work we consider a polynomial chaos approach to characterize and rank the aleatory uncertainty sources and a novel, non-probabilistic strategy to characterize the model-form errors. The present analysis enables us to break down the relative importance of epistemic and aleatory uncertainty and to build confidence intervals on the predictions. We consider simulations of a turbine guide vane cascade and assess the effect of combined uncertainties on the prediction of the wall heat flux.

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