Turbulence models in Reynolds-Averaged Navier-Stokes simulations have a crucial effect on the compressor stall margin. In this paper, a parametric uncertainty analysis of the Spalart-Allmaras (SA) turbulence model on compressor ﬂows is performed by a metamodel-based Monte Carlo method. The 2D NACA airfoils series representing the compressor hub and tip sections are investigated first, followed by a detailed analysis of a single-passage model of the NASA Rotor 67 compressor. The SA model coefﬁcients are represented by uniform distributions within intervals, and the quantities of interest include the compressor total pressure ratio and isentropic efficiency, radial and circumferential profiles of velocity components, tip leakage vortex trajectory, and turbulent viscosity. An artiﬁcial neural network from machine learning is applied as the metamodel, which is tuned, trained, and tested by databases from the ﬂow solver to achieve high accuracy. The uncertainty of quantities of interest is determined by the range of the metamodel and the database samples from the ﬂow solver. The sensitivity of model coefﬁcients is quantiﬁed by calculating the gradient of quantities of interest from the metamodel. Results show that the level of uncertainty is stronger at high-incidence conditions than that at low-incidence conditions, indicating the deficiency of the SA turbulence model for separated flows. Crucial model coefﬁcients on the quantities of interest are identiﬁed by a sensitivity analysis. However, the re-calibration of these coefﬁcients is contradictory for different quantities of interest and ﬂow regimes. This indicates the need for a modiﬁed Spalart-Allmaras turbulence model form to improve the accuracy for predicting compressor ﬂows.


Uncertainty Quantification of Spalart-Allmaras Turbulence Model Coefficients for Compressor Stall

Division / Sponsor:
International Gas Turbine Institute (IGTI)

Paper Proceedings: https://asmedigitalcollection.asme.org/GT/GT2020/volume/84058

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