High-fidelity simulations for new models that reduce noise pollution
Grant number: DP180100898 | Funding period: 2018 - 2021
This project aims to develop a method for accurate and affordable prediction and mitigation of flow-induced noise. The innovative approach, based on recent developments in simulation and data-driven modelling, expects to reduce environmental noise pollution, improve public health and ease the impact of urbanisation. To date methodological limitations have hampered our ability to predict noise reliably and hence control it. This project, exploiting proven high-fidelity simulation and machine-learning techniques to overcome limitations to produce the scientific knowledge required for practical noise mitigation. Benefits include quieter aerospace, marine and renewable energy technologies, creat..View full description
Related publications (6)
Stability characteristics of different aerofoil flows at Re-c=150, 000 and the implications for aerofoil self-noise
Hao Wu, Richard D Sandberg, Stephane Moreau
Three compressible-flow direct numerical simulations (DNS) of aerofoils were conducted at a chord based Reynolds number of Rec=150..