Effects of Inlet Incidence Perturbations on Compressor Cascade Performance using Adaptive Sparse Grid Collocation

The effects of inflow variations due to the working environment and flight attitude changes on turbomachines are considerable in the real world. Nevertheless, uncertainty quantification can be adopted to assess mean performance changes and perform the aerodynamic shape design as well as optimization. Thus, an uncertainty quantification method of adaptive sparse grid collocation (ASGC) was first introduced to address the inflow uncertainties’ effect issue effectively and accurately. Then, ASGC was utilized to evaluate the impacts of inlet incidence perturbations at different perturbation scales and reference inflow Mach numbers on the aerodynamic performance of a controlled diffusion cascade. The results showed that compared with the Monte Carlo simulation and static sparse gird collocation, the statistical accuracy and response accuracy of ASGC were maintained, and meanwhile its model construction efficiency was significantly improved because of the nested adaptive sampling feature. Under the perturbations of inlet incidences with high reference incidences, the mean aerodynamic loss always aggravates. The changes in aerodynamic loss nonlinearly depend on the inlet incidence perturbations, and the nonlinear dependence becomes greater when the perturbation scale. expands. At the same perturbation scale, the nonlinear dependence on the inlet incidence perturbations is further enhanced when the reference inflow Mach number rises. Finally, uncertainty quantification of the flow field revealed that the fluctuation of flow accelerations at the leading edge plays a fundamental role in determining the uncertainty of the aerodynamic loss.

Keywords:

Uncertainty quantification , Turbomachinery blade , Adaptive sparse grid collocation method , Aerodynamic performance , Inlet flow uncertainties