Hanru Liu

The propfan–swirl recovery vane (SRV) configuration is gaining renewed attention for its potential for higher propulsive efficiency, simpler transmission structure, and lower noise as compa...

Compared to a single rotor fan, a contra-rotating ducted fan (CRDF) has more power output per unit volume and can effectively reduce the weight of propulsor. However, contra-rotating turbomachinery has a stronger aerodynamic interaction between fan rows, consequently a reasonable balance between the thrust advantage and the noise disadvantage is required. In this paper, aerodynamic and aeroacoustic experimental studies were performed on a small-scale electrical contra-rotating ducted fan which is applicable to the propulsor of electrical unmanned aerial vehicles (UAV). The influence of different speed matching of the two rotors on noise characteristics is studied. The dominant tone of contra-rotating fan is concentrated at the harmonics of the front rotor BPF. Increasing the front rotor rotating speed leads to increasing noise. However, speeding up the rear rotor appropriately can reduce the noise. Regarding the present electrical ducted fan, a corrected broadband Heidmann noise model is established by analyzing the original thermodynamic parameters of the compressor with the motor power to obtain the characteristic sound pressure level. The prediction of the spectrum content of the original rotor-stator fan is extend to the contra-rotating fan. The proposed noise model can predict the broadband noise of the CRDF efficiently and accurately in the frequency range of [100 Hz, 10,000 Hz], which has been validated through the present experiment data. These works are important for the future synergistic design of aerodynamics and aeroacoustics of CRDF propulsors.

For compressor cascade flow field modeling, there exists strong velocity shear in the leading edge separation flow, boundary layer, and wake, which leads to increased modeling errors. To improve the accuracy of the flow field modeling method, this paper introduces the concept of numerical flux from the finite volume method into the loss function to implement Euler equation physics-informed learning, and a finite volume flux-informed neural network (FVFI-net) is constructed. Selecting a high-load, large-turning-angle compressor cascade as the study object, a comparative analysis is conducted on the advantages and disadvantages of purely data-driven, weak physical constraint, and finite volume flux-informed methods in compressor cascade flow field modeling. The study found that compared to purely data-driven and weak physical constraint methods, FVFI-net can reduce the average error of aerodynamic parameters in the flow field by approximately 45.6% and 29.5%, respectively, at a 0° angle of attack. For the flow separation problem occurring at the suction side leading edge and the blade wake area caused by a 5° angle of attack, FVFI-net can effectively reduce modeling errors near the leading edge, in the wake region, and near the periodic boundaries, thus reducing the average error of the aerodynamic parameters of the flow field by about 49.2%and 31.3%, respectively, compared to pure data-driven and weak physical constraint methods.