Novel Fluidic Topology Optimisation Methods For Gas Turbine Design

Hosted within the Rolls-Royce UTC for Computational Engineering, the following project aims to develop novel fluidic topology optimisation approaches for gas turbine component design by leveraging cutting edge machine/deep learning methods and computational fluid dynamics with the aim of revolutionising the solution of aerodynamic design problems.

Topology optimisation has grown in popularity over the past decade for structural problems but has yet to gain significant traction within the engineering community for aerodynamic problems. While controlling the porosity over a control volume offers considerable design freedom, the solution is limited to a local optimum and multiple constraints/objectives including, for example, manufacturability cannot be easily accounted for. This severely limits the application of such an approach within gas turbine design.

This project aims to develop a novel approach to fluidic topology optimisation by building upon the cutting edge machine/deep learning approaches for optimisation and geometry generation developed by the Rolls-Royce University Technology Centre (UTC) for Computational Engineering. By developing a unique feature-based fluidic topology optimisation process we aim to revolutionise aerodynamic design optimisation, producing more efficient designs in a greatly accelerated timeframe. While focused on gas turbine applications, for example, the optimisation of combustor, blade or seal features, this research has the potential to have a far reaching impact across engineering.

Entry Requirements

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

Essential:

– An appropriate degree in engineering, mathematics or computer science

– Experience of computer aided design e.g. Solidworks, NX, Catia etc.

– Experience of computational fluid dynamics e.g. Fluent, Star, OpenFOAM etc.

– Experience of programming e.g. Matlab, python, C/C++

Desirable:

– Experience of gas turbine design

– Experience of machine/deep learning tools e.g. pytorch, tensorflow etc.

Closing date: 31 July 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 nearmejobs.eu Doctoral College nearmejobs.eu 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:  Search for a Postgraduate Programme of Study (soton.ac.uk) Select programme type (Research), 2025/26, Faculty of Engineering and Physical Sciences, next page select “PhD Eng & Env. (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Dr. David Toal

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

The School of Engineering. is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.