Rapid Optimisation of Propellers with Tonal and Broadband Aeroacoustic Constraints

Abstract

The increased demand for sustainable aviation and the growing interest in Urban Air Mobility concepts has led to renewed interest in propeller aerodynamics and aeroacoustics. It is necessary to be able to predict the noise produced by propellers at the earliest stages of the design process. In this work, which builds on previous optimisation studies, a computationally efficient, BEMT based propeller simulation framework capable of predicting and optimising the aerodynamic and aeroacoustic performance is presented. The noise prediction provided by this tool includes both tonal and broadband components produced by the propeller. Aerofoil aerodynamics are obtained using a data-driven method, allowing for optimisation of the sectional shape alongside parameters such as chord and pitch distributions. Previous studies using this tool have found that including the aerofoil shape in the optimisation process allowed for an additional efficiency increases at high tonal constraints. This work introduces a trailing edge broadband noise model into the optimisation framework and studies the effect this has on the optimal geometry of a propeller. Optimisation of a small benchmark propeller has found significant reductions in mean broadband SPL of up to 5.18 dB while maintaining a high fraction of the baseline thrust. A study was also performed to maximise propeller efficiency while constraining broadband SPL. It was found that significant reductions in noise of up to 12 dB were possible at the cost of a 12% drop in propeller efficiency.

Recent Advances in Airfoil Self-Noise Passive Reduction

“…These algorithms include various tools that mostly mimic natural [94,128] or industrial [129] appearances. This section shows how optimization methods enable researchers to handle the existing trade-off between multiple objectives in multidimensional problems [130], address data-driven problems, or even create innovative solutions [131]. Researchers have used different heuristic optimization algorithms, including population-based, nature-inspired, and gradient-based techniques, with the help of Artificially Intelligent (AI) techniques [132,133], and some of them will be discussed here.…”