Aeroacoustic Noise Prediction: From Low- to High-Fidelity Modelling
This talk explores the physical mechanisms of aircraft noise generation and the computational modelling strategies used to predict and understand them across different levels of fidelity.
Overview
Aircraft noise remains a significant environmental challenge, motivating the development of increasingly accurate and efficient prediction methods. Computational aeroacoustics plays a key role in understanding the physical mechanisms responsible for noise generation and propagation. This talk explores different aeroacoustic noise sources in aeronautical applications and discusses modelling strategies ranging from low- to high-fidelity approaches. By highlighting their respective capabilities, limitations, and computational costs, it illustrates how different levels of modelling fidelity can provide complementary physical insights and support the design of quieter aircraft.
Presenters
Brief Biography
Luca Galimberti is a Ph.D. candidate in the Applied Mathematics and Computational Sciences (AMCS) at King Abdullah University of Science and Technology (KAUST). He is concurrently pursuing a dual Ph.D. degree in Aeronautical Engineering at Politecnico di Milano, where he also earned his M.Sc. in Aeronautical Engineering and his B.Sc. in Aerospace Engineering.
