This project is one of a number that are in competition for funding from the University of Bath URSA competition.
Project
The real-time simulations of electrical machines are a computationally demanding task because of computing large systems of equations originated by electromagnetic field equations. The challenge to reduce computation time and provide feasible and reliable estimations is an ongoing aim to develop and employ mathematical models to reduce the substantial computational time, while improving accuracy. Hence, the researcher should be familiar with model order reduction methods, such as hybrid LPTN, proper orthogonal decomposition (POD), Gappy POD, feature engineering using physics-informed machine learning (FE-PIML) methods, discrete empirical interpolation method (DEIM), Fourier series, so on. Traditionally, 1-D analytical models, such as lumped-element thermal network (LPTN), are designed to model the thermal resistances based on the one-dimensional heat transfer equations, which will create unreliable predictions of the average temperatures when the thermal model extends to two dimensions and/or three dimensions. In this project, an innovative FE-PIML model will be developed to build mathematical relationships for nonlinear electromagnetic problems, particularly at critical temperatures. The PIML model will be developed for every working stage and condition for electrical machines to enable electromagnetic and thermal analyses in all-wheel-drive electric propulsion systems. The development of the PIML-based model will transform the performance of electrical machines employed in electric drives within electric propulsion systems for different electrified vehicles used in terrestrial (e.g. ground vehicles), airborne, and aquatic environments. The outcome of this research will support energy and thermal management units used in most electric propulsion systems, like ground electric vehicles, aircraft, and underwater vehicles, and change the way researchers managed the electrical machines using rule-based control and management strategies nowadays. The aim is that the novel PIML-based model will perform fast and reliably in estimating the accurate nonlinear behaviour of the electrical machine/s under critical temperatures, especially the average and maximum temperatures of the active and end windings. The hybrid thermal model is applied to totally enclosed forced convection permanent magnet synchronous machines. The innovative analytical-based model will be validated by high-fidelity numerical techniques, such as 3-D FEA and CFD methods, and potentially experimental tests.
Candidate Requirements
Applicants should hold, or expect to receive, a First Class or good Upper Second Class Honours degree (or the equivalent). A master’s level qualification would also be advantageous. The ideal candidate holds a Masters degree in Electrical and Electronic Engineering, Control Engineering, and with good knowledge in Electric Propulsion Systems and Programming (i.e. Algorithms) using Python.
Non-UK applicants must meet our English language entry requirement by February 2023 in order to be considered.
Enquiries and Applications
Informal enquiries are encouraged! Direct these to Dr Pedram Asef - [Email Address Removed]
Please make a formal application should via the University of Bath’s online application form for a PhD in Electronic and Electrical Engineering
When completing the form, please identify your application as being for the URSA studentship competition in Section 3 Finance (question 2) and quote the project title and lead supervisor’s name in the ‘Your research interests’ section.
More information about applying for a PhD at Bath may be found on our website.
Funding Eligibility
To be eligible for funding, you must qualify as a Home student. The eligibility criteria for Home fee status are detailed and too complex to be summarised here in full; however, as a general guide, the following applicants will normally qualify subject to meeting residency requirements:
- UK nationals (living in the UK or EEA/Switzerland)
- Irish nationals (living in the UK or EEA/Switzerland)
- Those with Indefinite Leave to Remain
- EU nationals with pre-settled or settled status in the UK under the EU Settlement Scheme.
This is not intended to be an exhaustive list. Additional information may be found on our fee status guidance webpage, on the GOV.UK website and on the UKCISA website.
Equality, Diversity and Inclusion
We value a diverse research environment and strive to be an inclusive university, where difference is celebrated and respected. We encourage applications from under-represented groups. In particular, we are welcoming applications from candidates with Refugee, Asylum Seeker, or Humanitarian Protection in the UK to our Doctoral Sanctuary Studentship in Engineering and Design.
If you have circumstances that you feel we should be aware of that have affected your educational attainment, then please feel free to tell us about it in your application form. The best way to do this is a short paragraph at the end of your personal statement.
The Disability Service ensures that individuals with disabilities are provided the support that they need. If you state if your application that you have a disability, the Disability Service will contact you as part of this process to discuss your needs.
Keywords: Artificial Intelligence; Automotive Engineering; Communications Engineering; Control Systems; Electrical Engineering; Electronic Engineering; Energy Technologies; Internet Of Things; Machine Learning; Mechatronics
Continue with Facebook
