Data-driven modelling and control of turbulence-generated noise

Are you passionate about understanding the underlying physical principle of noise generation by unsteady turbulent flows? Do you want to contribute to cutting-edge research that will lead to a breaking towards achieving silent aircraft design?

Applications are invited for one funded 3.5-year PhD studentship for the project titled ”Data-driven modelling and control of turbulence-generated noise” in the group of computational aerodynamics and aeroacoustics (CA^2) led by Dr Zhong-Nan Wang at the University of Birmingham. The CA^2 research group focuses on developing high-fidelity Computational Fluid Dynamics (CFD) and data-driven methods for aerodynamics and aeroacoustics with primary application in aerospace engineering. The PhD project is expected to start in September 2026 (but the time can be flexible). The successful applicant will be able to work within a vibrant and multidisciplinary aerospace team under the College of Engineering and Physical Sciences at the University of Birmingham and also have opportunities to engage with researchers at other national and international leading academic institutions, e.g. Cambridge and MIT, and industries, such as Rolls-Royce and Airbus, via established collaboration.

Project details:

Noise pollution is a growing environmental issue and becomes the second-largest environmental cause of health problems in Europe, just after air pollution. Noise generated by turbulent flows is one of the outstanding components in both air and land transport as well as wind farm. However, the reduction of turbulence-generated noise is far from optimal and the design heavily depends on expensive rig testing, because the first-principle understanding of physical mechanisms via which turbulence radiates noise remains incomplete. This has persistently prevented us from efficiently reducing the noise generated by unsteady turbulent flows.

In this project, we will employ high-fidelity CFD to provide the full details of noise generation processes in unsteady turbulent flows. Based on the high-fidelity CFD data, the data-driven method will be developed to inform low-order mathematical modelling of noise-generating dynamics and control strategies, which will lead to an optimal reduction of noise emissions. The application will initially focus on aircraft noise, but the method to be developed in the project is general, so it can be used to tackle a broad range of aero-acoustic problems, such as fan noise and wind turbine noise.

Please contact Dr Zhong-Nan Wang ([email protected]) for an informal query about this studentship.