PhD Research Studentships
Data-Driven Optimisation of Hairpin Winding and Oil Cooling in Traction Motors for Improved Thermal Management
| Supervisor: | Amin PAYKANI |
| Apply by: | 29 January 2025 |
| Start in: | September (Semester 1) |
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. The research will proceed through the following steps:
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Experimental Validation: The initial phase will involve conducting experiments to capture data on oil jet behavior and cooling performance in hairpin windings. This experimental data will be used to validate the CFD models, ensuring accuracy in simulating the complex interactions between oil jets and winding surfaces.
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CFD Simulations: Following validation, a detailed 3D CFD setup will be used to simulate the system under various operating conditions. These simulations will form the foundation for generating the training dataset for the ML model. High-fidelity CFD results will be leveraged to understand the effects of different winding geometries and oil jet parameters on the formation of the oil layer and heat transfer efficiency.
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ML-Based Surrogate Modeling: Using the experimental and CFD data, a hybrid surrogate model will be developed with the help of advanced ML techniques. Bayesian optimisation will be applied to select the optimal ML hyperparameters, ensuring the model accurately predicts system performance while reducing computational costs.
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Design Optimisation: An optimisation technique will be employed to find the optimal combination of hairpin winding geometries and oil injector parameters, based on the ML surrogate model. This approach allows for efficient exploration of the design space, identifying configurations that maximise cooling effectiveness and power density.
The outcome of this project will provide a robust and computationally efficient framework for optimising complex cooling systems in traction motors, making it accessible to engineers without extensive expertise in machine learning.
Funding
Funded by: China Scholarship CouncilCandidate 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.
- 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 minimum score of 6.0 in each of Writing, Listening, Reading and Speaking).
- Candidates are expected to start in September (Semester 1).
Contact
For informal enquiries about this opportunity, please contact Amin PAYKANI.
Apply
Start an application for this studentship and for entry onto the PhD Mechanical Engineering full-time programme (Semester 1 / September start):
Please be sure to quote the reference "SEMS-PHD-611" to associate your application with this studentship opportunity.
| Related website: | https://www.sems.qmul.ac.uk/staff/a.paykani | |
| SEMS Research Centre: | ||
| Keywords: | Machine Learning, Aerospace Engineering, Automotive Engineering, Electrical Engineering, Fluid Mechanics, Mechanical Engineering, Data Analysis |