Details of Research Outputs

The design of quiet and efficient propellers/rotors is driven by the rapid development of Vertical Takeoff and Landing (VTOL) vehicles in urban areas. However, this task is challenging due to the expensive computational cost of propeller aerodynamic and aeroacoustic optimization based on accurate high-fidelity (HF) models. In contrast, many low-fidelity (LF) methods are less costly but also less accurate for complex propeller blade geometries, such as those involving sweep. This work aims to use a multi-fidelity (MF) surrogate model (SM) that combines a small HF data set for accuracy and a larger LF data set to speed up modeling. The MF approach inherits the accuracy of the HF method while retaining the computational efficiency of the LF model. The HF and LF aerodynamic data sets for training the MF model are obtained from a RANS solver and a meshless Large Eddy Simulation (LES) method, respectively. The obtained surface pressure of the blade is then used to calculate the noise signals radiating from the propeller using a Farassat's Formulation 1A (F1A) code. For practical design, we developed an optimization framework that combines these aerodynamic and aeroacoustic solvers, the MF model, design of experiment (DoE), and a multi-objective optimizer. The framework aims to optimize the blade aerodynamic shape in terms of chord, twist, and sweep distributions along the span from a baseline propeller, with objectives of efficiency and noise reduction under thrust constraints for both cruise and hover operating conditions. The obtained optimal designs, in the form of three-dimensional Pareto fronts, increased the aerodynamic efficiency by approximately 2% for cruise and 5% for hover and decreased the overall sound pressure level (OASPL) for hover by about 4 dB from the baseline propeller. The MF surrogate model, which utilizes both HF and LF data, doubled the optimization efficiency compared to the surrogate model based solely on HF data. Furthermore, the model based only on LF data fails to capture the three-dimensional flow effects induced by propeller sweep, which is crucial for reducing propeller noise. The aerodynamic benefits include reduced flow separation near the blade root during hover and more ideal loading distribution during cruise, while the aeroacoustic improvements are demonstrated through the “dephase” effect on noise signals emitted from different parts of the swept blade.