CFD analysis on the performance of a coaxial rotor with lift offset at high advance ratios

The aerodynamic performance of an isolated coaxial rotor in forward flight is analyzed by a high-fidelity computational fluid dynamics (CFD) approach. The analysis focuses on the high-speed forward flight with an advance ratio of 0.5 or higher, which is the ratio of the forward speed to the rotor tip speed. The effect of the degree of the rolling moment on the rotor thrust, called lift offset, is studied in detail. The coaxial rotor model is a pair of contrarotating rotors, each rotor consisting of two untwisted blades with a radius of 1.016 m. The pitch angle of the blades is controlled by both collective and cyclic as in a conventional single main-rotor helicopter. CFD analysis is performed using a flow solver based on the compressible Navier-Stokes equations with a Reynolds-averaged turbulence model. Laminar/turbulent transition in the boundary layer is taken into account in the calculation. The rotor trim for target forces and moments is achieved using a gradient-based delta-form blade pitch angle adjusting technique in conjunction with CFD analysis. The reliability of the calculations is confirmed by comparison with published wind tunnel experiments and two comprehensive analyses. Applying the lift offset improves the lift-to-effective drag ratio (lift-drag ratio) and reduces thrust fluctuations. However, in the case where the advance ratio exceeds 0.6, the lift-drag ratio drops significantly even if the lift offset is 0.3. The thrust fluctuation also increases with such a high advance ratio. Detailed analysis reveals that the degradation of aerodynamic performance and vibratory aerodynamic loads is closely related to the pitch angle control to compensate for the reduction in thrust on the retreating side due to the increased reverse flow region. It is effective to reduce the collective and longitudinal cyclic pitch angles for the improvement of the aerodynamic performance of coaxial rotors with an appropriate lift offset.(c) 2023 Elsevier Masson SAS. All rights reserved.