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"Advancing Computational Aeroacoustics Research in the New Era of Aviation — Numerical Prediction, Optimization and Data-Driven Methods"

by

Beckett Y. Zhou

Assistant Professor | Department of Aerospace Engineering | University of Bristol

Monday, January 22
2 - 3 pm
Location: Weber Lecture Hall 2

About the Seminar: 
The new era of aviation, marked by the emergence of advanced air mobility and  distributed electric propulsion systems, gives rise to new design challenges related to aeroacoustics. This talk will showcase a number of aeroacoustic prediction and optimization capabilities recently developed at the University of Bristol, with a focus on propeller/rotor applications. Specifically, it will include: 1. mid- and high-fidelity aeroacoustic simulation approaches including an advanced Galerkin time-domain acoustic scattering method. 2. an adjoint-based aeroacoustic optimization framework, with simultaneous consideration of aerodynamic and noise design objectives and constraints, and 3. recent efforts in enhancing broadband noise modeling and accelerating noise prediction using physics-informed machine learning techniques.  

About the Speaker: 
Dr. Beckett Y. Zhou is a lecturer (assistant professor) in aeroacoustics in the Department of Aerospace Engineering at the University of Bristol. He received his masters degree from MIT in 2012 and his Ph.D. from the RWTH Aachen University in 2018 with a thesis entitled ‘Numerical Optimization for Airframe Noise Reduction’. 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. He joined the University of Bristol as a faculty member in March 2021, leading the computational aeroacoustics research in the Department of Aerospace Engineering. His research team focuses on developing efficient aerodynamic and aeroacoustic simulation and optimization frameworks, supported by multi-fidelity methodologies and data-driven methods.