PhD Studentship: AI-assisted robust aero-engine structural analysis due to fan blade-off

Supervisory Team: Dr. Jie Yuan, Dr. David Toal

PhD Supervisor: Jie Yuan

Project description:

Robust design is crucial to ensure the durability and reliability of aerospace components throughout their entire life cycle due to the variations arising from geometry, material properties and loads during the long-term operation. This leads to a growing need in model identification, calibration, and uncertainty quantification for robust structural design, particularly for complex aero-engine systems with limited experimental data. Recent work by the University of Southampton developed a novel data driven Bayesian inference framework for identifying complex aerospace systems combining with limited experimental data. It can be also used to quantify uncertainties from experimental testing, significantly improving the reliability of the prediction of structural performance.

This project aims to continue developing the stochastic inference framework by leveraging recent advances in artificial intelligence techniques and advanced sampling methods to bring a significant advancement in reducing high-fidelity runs to accelerate the engineering design, validation process and improve the robustness of the prediction. The project will work closely with engineers from Rolls Royce plc to demonstrate the benefits of the developed framework in a critical aero-engine fan blade off (FBO) test case including possible industrial placement. It will contribute to producing robust loads for sizing the structural components considering various FBO input uncertainties. This would also support the certification process to satisfy the need for the probability of occurrence of FBO responses.

Applications are invited for a fully funded PhD studentship (UK or EU or International) working on the project. The PhD student will join Rolls Royce Computational University Technology Centre being a part of  Computational Engineering and Design research group in Department of Aeronautics and Astronautics at the University of Southampton, which is deliciated to developing novel computational methods for design and optimization problems in turbomachinery with strong support from Rolls Royce plc. The student will be expected to closely work with Rolls Royce Engineer with possible supported secondment in company during the study.

The student should have a strong interest in mathematical science, software development, data driven modelling, structural mechanics and engineering background.

If you wish to discuss any details of the project informally, please contact: 

Dr. Jie Yuan, Computational Engineering and Design Research Group,

Email: j.yuan@soton.ac.uk

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date: 31 August 2025.  Applications will be considered in the order that they are received, the position will be considered filled when a suitable candidate has been identified.

Funding: We offer a range of funding opportunities for both UK and international students, including Bursaries and Scholarships.  For more information please visit PhD Scholarships | Doctoral College | University of Southampton  Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.

How To Apply

Apply online by clicking the 'Apply' button, above. 

Select programme type (Research), 2025/26, Faculty of Engineering and Physical Sciences, next page select “PhD Engineering & Environment (Full time)”.

In Section 2 of the application form you should insert the name of the supervisor Jie Yuan

Applications should include:

• Research Proposal
• Curriculum Vitae
• Two reference letters
• Degree Transcripts/Certificates to date

For further information please contact: feps-pgr-apply@soton.ac.uk

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