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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.

References

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:

  1. 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.
  2. 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.
  3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

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%.

Meet the Supervisor(s)


James Tyacke - As a Lecturer in Aerospace Engineering, I am primarily interested in Large Eddy Simulation (LES) of complex flows including Jet Aeroacoustics, Turbomachinery and Electronics Cooling. Multi-fidelity modelling underpins these areas, both in terms of turbulence modelling and geometry representation. Modern High Performance Computing (HPC) architectures are also being leveraged for both simulation and analysis of large data sets (Big Data), revealing unsteady flow physics. *** I am advertising an EPSRC funded Doctoral Training Partnership (DTP) PhD studentship for “Noise reduction of Urban Air Mobility Vehicles using CFD”, starting 1st October 2021.  Please contact me for further details. Application instructions here: Noise reduction of Urban Air Mobility Vehicles using CFD Deadline 12:00 Noon, Friday 11th June 2021. *** I am currently looking for students to complete a PhD under EPSRC DTP funding at Brunel University London or those who are self-funded.  A range of projects are available, focusing on multi-fidelity Computational Fluid Dynamics (CFD).  Potential application areas are electronics cooling, aerospace, turbomachinery, aeroacoustics and urban flows, among others.  Further interests include increasing CFD automation, including mesh generation and optimisation, solution analysis and feedback into knowledge-based systems using Machine Learning and AI. Example projects here: https://www.brunel.ac.uk/research/Research-degrees/PhD-Topics DTP funding details: https://www.brunel.ac.uk/research/Research-degrees/PhD-Studentships/Studentship?id=a2efbe35-b0b9-46a7-b284-3fd40ff05174 Research degree funding: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding External funding: https://www.brunel.ac.uk/study/postgraduate-fees-and-funding/other-funding