Increasing the power density of traction motors is a critical challenge for the 
next generation of electric vehicles. Combining hairpin windings with direct oil 
cooling has emerged as a popular solution, but optimising the design of such 
systems requires a deep understanding of fluid dynamics and heat transfer. The 
formation of the oil film on windings is influenced by various factors, including 
jet parameters and winding geometry, making the design process complex and 
computationally expensive when relying on traditional high-fidelity Computational 
Fluid Dynamics (CFD) simulations. This PhD project aims to develop a data-driven 
framework that integrates experiments, CFD, and Machine Learning (ML) to co-
optimise the hairpin winding geometry and oil injector parameters for enhanced 
cooling performance. 

Funding
Funded by: China Scholarship Council
Candidate will need to secure a CSC scholarship.
Under the scheme, Queen Mary will provide scholarships to cover all tuition fees, 
whilst the CSC will provide living expenses and one return flight ticket to 
successful applicants.

Eligibility
The minimum requirement for this studentship opportunity is a good honours degree 
(minimum 2(i) honours or equivalent) or MSc/MRes in a relevant discipline.
For 2024-5, the UKRI and Queen Mary stipend rate is £21,237;
If English is not your first language, you will require a valid English 
certificate equivalent to IELTS 6.5+ overall with a minimum score of 6.0 in 
Writing and 5.5 in all sections (Reading, Listening, Speaking).
Candidates are expected to start in September (Semester 1).