Abstract:
Blade–wake interaction (BWI) is a significant source of broadband noise and is often dominant in rotors with high blade counts. Accurately capturing the resulting unsteady blade loading is computationally expensive and, therefore, drives the cost of BWI noise calculation. To address this challenge, a low-fidelity BWI noise prediction tool was developed using aerodynamic data from the blade element momentum theory (BEMT) and the lattice Boltzmann method (LBM) for a series of rotor configurations with medium to high solidity. Starting from a six-bladed baseline rotor, 13 additional configurations were generated by varying blade twist, taper, root collective, solidity, and blade count. The relationship between vortex miss distance and blade loading unsteadiness was quantified to construct a semi-empirical BWI noise model. The model predicted BWI noise with a root mean square error of 3.9 dBA and a mean absolute percentage error of 1%. It was subsequently integrated into a BEMT framework to produce aerodynamic and acoustic data for training a tandem neural network (TNN) that was employed to optimize two rotor geometries. The optimized designs achieved up to a 7% reduction in BWI noise and a 7% improvement in performance. Additional geometric modifications—including blade tip anhedral, forward sweep, and a mixed configuration—were also assessed using LBM, each demonstrating notable noise reduction.