PhD Studentship: AI-Based Acceleration of Computational Fluid Dynamics for Automotive Applications

CFD is at the core of the design process for aerospace and automotive as it is used to reduce drag and hence CO2 emissions.

However, CFD prediction of industrial applications require large amounts of computational resource and is both time-consuming and expensive. Acceleration of this part of the design process would allow designs to be produced more quickly and with greater exploration of the design space.

This project will develop a technique using AI/ML and Reduced Order Models to predict flows based on the vehicle prior to carrying out full CFD calculations. This will involve developing techniques to encode geometrical information and evaluating the best Artificial Neural Networks or other techniques such as Reduced Order Models to reproduce the flow field and important performance parameters.

By limiting the scope to automotive shapes, the geometry and flows will have similar structures and can be relatively easily parameterised/characterised.

Funding

Tuition fees cover the cost of your teaching, assessment and operating University facilities such as the library, IT equipment and other support services. University fees and charges can be paid in advance and there are several methods of payment, including online payments and payment by instalment. Fees are reviewed annually and are likely to increase to take into account inflationary pressures.

The AACME Studentship competition offers the chance for UK and International applicants who are interested in undertaking a PhD to have their full-time studies paid for.

Applicants could receive full or partial funding for 3 years, including a tax-free stipend of £17,668 (2022/23 rates) per annum, and/or a tuition fee waiver.

Studentships will be awarded on a competitive basis to applicants who have applied to advertised projects within AACME with the reference ‘AACME-23-XXX'. Successful candidates will be notified by the end of March 2023.

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