Automating CFD using knowledge-based systems
Typical steady CFD is performed using the Reynolds-Averaged Navier-Stokes equations, however accuracy of the flowfield is strongly dependent on the turbulence model used and can be case dependent. Large Eddy Simulation produces accurate flow data consistently for a wide variety of complex flows. Although LES requires careful case setup and solution times are orders of magnitude larger than RANS, the accuracy, data detail and cost of LES relative to physical testing makes its use attractive for many applications. Modern Computational Fluid Dynamics (CFD) solvers utilise High Performance Computing (HPC) to reduce simulation turnaround time. However, the pre-processing (mesh generation) and post-processing (data extraction + visualisation) have now become bottlenecks requiring significant manual intervention and time. Modelling of flow features such as separation in gas-turbine internal cooling ducts lend themselves towards automation.
The project would involve generation of modules to automate mesh generation, run steady and unsteady modelling of such flows, extract data, perform machine learning and link these within a knowledge-based system. This would allow LES to be consistently deployed with minimal human intervention for a range of flows. Applicants would benefit from experience in fluid dynamics, CFD, HPC, Fortran/C++/Python.
Future use of large eddy simulation in aero-engines (https://doi.org/10.1115/1.4029363)
How to apply
If you are interested in applying for the above PhD topic please follow the steps below:
- Contact the supervisor by email or phone to discuss your interest and find out if you woold be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
- Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
- Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.
This is a self funded topic
Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.