Abstract: Efficient prediction and optimization of propeller aerodynamic and acoustic performance is of paramount importance to the analysis and design of various urban air mobility concepts emerging over the last decade. In this talk, three related research efforts towards propeller noise prediction and minimization will be presented. Firstly, an adjoint-based propeller simulation and optimization framework is presented, capable of predicting the aerodynamic and aeroacoustic performance and optimizing the blade geometry of isolated propellers, subject to acoustic constraints. As part of this tool airfoil aerodynamics are obtained using a machine learning approach, allowing for optimization of the blade profile as well as an increase in computational efficiency. To improve the predictive capability of the propeller simulation code, the second part of this talk presents the first exploratory effort in developing a data-driven framework consisting of a deep neural network machine learning (ML) model trained in a multi-fidelity manner using transfer learning (TL) and active learning (AL). 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. Additional propellers which were manufactured and experimentally measured were intelligently selected based on an AL algorithm designed to minimize the predictive error of the ML model at farfield observer locations. The final part of this talk presents our latest effort in tackling the challenging problem of propeller broadband noise minimization. A ML-adapted wall pressure spectrum (WPS) model is developed using a data-driven approach and is shown to improve the broadband noise prediction when used in conjunction with a trailing edge noise model. 

Bio: Dr. Beckett Y. Zhou is a lecturer in aeroacoustics in the Department of Aerospace Engineering at the University of Bristol. He received his Ph.D. in computational engineering sciences with a thesis entitled ‘Numerical Optimization for Airframe Noise Reduction’ from the RWTH Aachen University in 2018. He subsequently performed post-doctoral research with NASA Langley Research Center (hosted by the National Institute of Aerospace) on the topic of adjoint-based broadband noise reduction via stochastic noise generation. Prior to joining the University of Bristol as a lecturer in March 2021, he worked as a research scientist at the Scientific Computing Group of the Technical University of Kaiserslautern. His research focuses on developing efficient aerodynamic and aeroacoustic simulation and optimization frameworks, using multi-fidelity methodologies and data-driven methods.