CFD Jobs Database - Job Record #19433
Job Record #19433 |
Title | CSC Scholarship PhD Position in ML and CFD of Liquid Jets |
Category | PhD Studentship |
Employer | Queen Mary University of London |
Location | United Kingdom, London |
International | No, only national applications will be considered |
Closure Date | Wednesday, January 29, 2025 |
Description: |
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).
|
Contact Information: |
Please mention the CFD Jobs Database, record #19433 when responding to this ad. |
Name | Amin Paykani |
Email | a.paykani@qmul.ac.uk |
Email Application | No |
URL | https://www.sems.qmul.ac.uk/research/studentships/611/data-driven-optimisation-of-hairpin-winding-and-oil-cooling-in-traction-motors-for-improved-thermal-management |
Record Data: |
Last Modified | 17:54:16, Thursday, October 24, 2024 |
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