Project outline
High-fidelity numerical simulation of vehicle aerodynamics and wind noise with experimental validation.
Reducing aerodynamic drag helps cars use less energy, but changes to vehicle shape can also influence wind noise and passenger comfort. This PhD will explore how to design quieter, more efficient vehicles using high-fidelity computational simulations based on the lattice Boltzmann method. By comparing simulation predictions with wind-tunnel measurements, you will investigate how turbulent flow around vehicle components generates sound and develop approaches to reduce wind noise while maintaining low drag.
Through this research, you will gain expertise in fluid mechanics, turbulence modelling, aerodynamics and aeroacoustics, alongside practical skills in advanced computational fluid dynamics (CFD) and the processing, analysis and visualisation of simulation data. You will share your findings at national and international conferences and through peer-reviewed journal publications, contributing to the development of quieter, more energy-efficient vehicles.
Further project information
This project will investigate how vehicle geometry, particularly the underbody, can be designed to reduce aerodynamic drag while limiting wind noise. Using the lattice Boltzmann method (LBM) with high-fidelity large-eddy simulation (LES), you will examine turbulent airflow, unsteady pressure loading and sound generation around underfloor treatments and rear aerodynamic devices across a range of vehicle speeds and wind directions.
Proper orthogonal decomposition (POD) and spectral analysis will be used to identify dominant coherent flow structures, characterise their temporal behaviour and frequency content, and examine their relationship with pressure fluctuations and noise. Where appropriate, machine-learning surrogate models trained on the simulation results will enable efficient exploration of design alternatives and support aerodynamic and aeroacoustic optimisation.
Simulation predictions will be validated against experimental measurements of flow fields, surface pressures and radiated noise to assess the reliability of the numerical approach. Together, the computational and experimental findings will guide geometry modifications that reduce wind noise while maintaining low drag and suitable lift characteristics.
Collaboration with Jaguar Land Rover (JLR) will ensure that the research addresses practical vehicle-design challenges, with industrial input guiding the selection of configurations and operating conditions and the assessment of proposed design improvements.
Working alongside academic and industrial researchers, you will develop expertise in computational fluid dynamics, aeroacoustics, high-performance computing and data analysis. Your research will deliver experimentally supported design guidance for quieter, more efficient future vehicles.
Selected references:
Powell et al. (2013). Simulation of Underbody Contribution of Wind Noise in a Passenger Automobile.
Parnis and Angland (2025). Numerical Simulation of the Aerodynamics and Acoustics of a Wall-Mounted Spoiler.
Tran, J., Fukami, K., Inada, K., Umehara, D., Ono, Y., Ogawa, K. and Taira, K. (2024). Aerodynamics-guided machine learning for design optimization of electric vehicles. Communications Engineering, 3, Article 174. 10.1038/s44172-024-00322-0
Subject Areas
- Aerospace Engineering
- Mechanical Engineering
- Automotive Engineering
- Computational Fluid Dynamics
- Aeroacoustics and Noise Control
- Fluid Mechanics
- Computational Physics
- Applied Mathematics
- Turbulence
Required technical skills/academic qualifications/experience
Applicants should have a 2:1 or higher undergraduate degree or a master’s degree in engineering, physics, applied mathematics or a related discipline. Ideally, applicants will have a sound foundation in mathematics and an interest in fluid mechanics and computational modelling.
Basic experience of programming, numerical simulation or data analysis would be helpful. Previous experience of the lattice Boltzmann method, aeroacoustics or high-performance computing is not required, training in these areas will be provided during the PhD.
We particularly encourage applications from women and other groups under-represented in research. All appointments will be made on merit.
Mode of attendance
This project is only available on a full-time basis.
Supervisors
Project Partners
This project is hosted at the University of Bristol and supported by Jaguar Land Rover.
