PhD Studentship in Fluid dynamics and Machine learning

Applications are invited for a research studentship in the field of Fluid dynamics and Machine learning, leading to the award of a PhD degree. The post is supported by a bursary and fees (at the UK student rate) provided by Rolls-Royce and the EPSRC (iCASE). Candidates should fulfil the eligibility criteria for the award.

A key driver for reducing the carbon footprint of commercial and business aviation is the ability to predict engine behaviour before manufacture. The ability to perform these predictions quickly and to a higher fidelity increases the amount of optimisation possible, as well as confidence levels in the predicted behaviour. The Vibration University Technology Centre at Imperial College London has for 2 decades developed a state-of-the-art prediction software toolkit that allows not only steady performance predictions but also dynamic fluid-structure interactions to be performed and has been used within Rolls-Royce to improve component design for performance, safety, and reliability.

The advent of machine learning (ML) has opened up opportunities to further improve prediction speed and facilitate larger and more complex analyses to be performed. This PhD looks at investigating how ML methods can be applied to existing Navier-Stokes prediction tools so that the turnover time for aeromechanics and performance predictions can be decreased by up to an order of magnitude. The work will focus on coupling methods for Reynolds Averaged Navier Stokes methods with ML algorithms.

You will carry out your work within the Rolls-Royce Vibration University Technology Centre in collaboration with Dr Ludovic Renson, Dr Sina Stapelfeldt, and in collaboration with other departments in the College as well as other UK and oversea universities.

You will be an enthusiastic and self-motivated person who meets the academic requirements for enrolment for the PhD degree at Imperial College London. You will have a 1st class honours degree in mechanical/aeronautic engineering or a related subject, and an enquiring and rigorous approach to research together with a strong intellect and disciplined work habits. An interest in computational methods and programming is essential.  Good team-working, observational and communication skills are essential.

To find out more about research at Imperial College London in this area, go to:

http://www.imperial.ac.uk/mechanicalengineering

For information on how to apply, go to:

http://www.imperial.ac.uk/mechanical-engineering/study/phd/how-to-apply/

For further details on the post contact Dr Ludovic Renson (l.renson@imperial.ac.uk). Interested applicants should send an up-to-date curriculum vitae to Dr Renson.  Suitable candidates will be required to complete an electronic application form at Imperial College London for their qualifications to be addressed by College Registry.

Closing date: until post filled

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