Data-driven sparse polynomial analysis of inflow uncertainty effects on transonic compressor aerodynamics at different Reynolds numbers

To meet the increasing demands for high thrust-to-weight ratios and low specific fuel consumption in modern aero engines, compressor designs are trending toward higher stage loading to reduce stage counts. However, these advancements introduce challenges related to low Reynolds number (Re) effect and elevated sensitivity to uncertainty. This study proposes a data-driven sparse arbitrary polynomial chaos expansion (DD-SAPCE) method to investigate the impact of inflow uncertainty on the aerodynamic performance of a transonic compressor rotor across different Re conditions. The DD-SAPCE framework effectively mitigates the curse of dimensionality inherent in conventional polynomial chaos approaches, reducing computational cost by nearly 60% while achieving comparable accuracy. Furthermore, the method supports modeling with discrete data, making it suitable for practical engineering applications. Statistical comparisons reveal that low Re conditions intensify the variability in mass flow while reducing the dispersion of isentropic efficiency. These effects are accompanied by enhanced nonlinear sensitivity of aerodynamic performance to inflow uncertainty. Spanwise analyses further show that changes in blade loading and shock structures are the key contributors to performance fluctuations. Furthermore, global and local sensitivity analyses based on the Shapley method identify inlet total pressure, back pressure, and flow angle as the dominant factors influencing the aerodynamic performance sensitivity to inlet flow disturbances. These findings offer critical insights for the performance influence mechanism and robust aerodynamic optimization of transonic compressor systems.