Multi-Fidelity Propeller Noise Prediction using a Data-Driven Approach

Abstract

Accurate and rapid prediction of propeller aeroacoustic performance is of paramount importance to the design of quiet urban air mobility (UAM) concepts emerging over the last decade. For the broadband noise component, Amiet-type low-fidelity methods are typically used in order to avoid resorting to computationally prohibitive scale-resolving simulations. These methods however, are highly limited in their accuracy and robustness over the range of flow conditions UAM propellers are designed to operate. This paper presents an exploratory effort in developing a multi-fidelity framework to enhance the propeller broadband noise prediction using data-driven approaches. In this framework, a deep neural network machine learning (ML) model is trained in a multi-fidelity manner using transfer learning (TL). The model is first trained using a large number of computationally inexpensive low-fidelity simulations and then enhanced by a small number of high-fidelity aeroacoustic wind tunnel measurements using TL. In the deployment stage, this ML model enables rapid prediction of broadband sound pressure level of isolated 2-bladed propellers between 1kHz and 7kHz at three farfield observer locations given the cross-sectional profile, pitch-to-diameter ratio as well as forward speed and rotational speed of the propeller. Results evaluated based on experimental data from two extra sets of propeller blades, which were withheld from the ML training process, indicated that the multi-fidelity ML model trained with both low- and high-fidelity data based on the TL approach is capable of significantly improving the predictive accuracy of the low-fidelity model. Furthermore, the TL-based multi-fidelity ML model delivered more accurate predictions than the ML models trained solely with limited high-fidelity data.