Abstract
The impact of inlet flow variations on turbomachines is common in the real world, resulting in undesirable performance deteriorations in engineering. The origin of performance changes and how to evaluate the changes require to be investigated. On use of a polynomial chaos based surrogate model method and Monte Carlo simulation (MCS), performance impact considering the stochastic variations of the inlet flow angle for a transonic compressor rotor blade is studied, and the impact mechanisms are investigated by MCS-based statistical flow analysis. A detailed grid-independent study assisted by Richardson extrapolation is first presented to illustrate the reliability of numerical solutions. Then, the implementations of non-intrusive polynomial chaos employing an adaptive sparse grid are introduced. Uncertainty quantifications of adiabatic efficiency and mass flow rate changes of NASA Rotor 67 are achieved, and the effects of different distributions of inlet flow angle variation (IFAV) and different ranges of IFAV on the performance changes under different operation conditions are investigated. Finally, statistical analysis of flow solutions is performed by MCS. The spanwise distributions of the changes in adiabatic efficiency and mass flow rate at the outlet are presented. Moreover, the flow solutions on the blade-to-blade stream surfaces at different spans are statistically analyzed, and discussions are given to demonstrate the impact mechanisms of IFAV on performance changes.
Highlights
Over the past few decades, highly optimized blades and efficient compression system had been designed benefiting from the advanced numerical methods in terms of computational fluid dynamics (CFD) and design optimization.1 Besides performance improvements, robust design considering the effects of uncertainties has attracted wide attention in recent years because the performance dispersity can be reduced through robust design
The results demonstrate that the proposed adaptive sparse grid works effectively
The results demonstrate that flow separation is, the most sensitive to inlet flow angle variation (IFAV) on the inner spans
Summary
Over the past few decades, highly optimized blades and efficient compression system had been designed benefiting from the advanced numerical methods in terms of computational fluid dynamics (CFD) and design optimization. Besides performance improvements, robust design considering the effects of uncertainties has attracted wide attention in recent years because the performance dispersity can be reduced through robust design. Loeven and Bijl employed the probabilistic collocation method to quantify the performance impact of the uncertain inlet total pressure profile for NASA Rotor 37 They illustrated that the flow near the shock wave exhibits the most intensified variations, especially on the outer spans. A few open literature studies have quantified the impact of different uncertainties on the aerodynamic performance of transonic compressor rotor blades, the mechanisms of performance variations due to the uncertainties have not been well demonstrated. A considerable number of threedimensional numerical simulations are performed as required by MCSs. The complex flow in transonic compressor rotor blade passage, such as shock wave, flow separation, and tip-clearance flow, is resolved, which favors the demonstrations of impact mechanisms of uncertainties. Statistical flow solutions obtained by MCSs are presented to demonstrate the impact mechanisms of the IFAV on the typical flow of transonic compressor rotor blade
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