Background
The SuperMachine project funded by the Royal Academy of Engineering Chair in Emerging Technologies program will deliver a set of enabling technologies for the development of a high power density and high efficiency electrical machines using high temperature superconductors (HTS) and composite structural materials. Conventional machine topologies cannot meet the increasing demands of power density (W/kg) in future transport and energy applications. For electrical machines in aerospace it is predicted that the power density needs to be 40kW/kg. A paradigm shift in electrical machine technology is required with a radical change in materials used. In the SuperMachine project program copper, iron and permanent magnets, will be replaced with arrays of air-cored high temperature superconducting (HTS) coils, which fully exploit superconducting characteristics of high magnetic fields and high currents.
Methodology and Objectives.
In the SuperMachine concept arrays of air-cored HTS coils interact directly with the magnetic field, and thus will experience AC losses. The level and complexity of AC losses depends upon the frequency of the magnetic fields and hence rotational speed. The SuperMachine concept is being investigated for both low and high speed applications in wind energy and transport applications. The project will build upon recently completed PhD research (Zhang, 10.7488/era/1519), in which AC loss models have been developed for single HTS coils operating over a range of frequencies, taking into account the different materials within high temperature superconducting tape. The work will be expanded to investigate AC losses in an array of air-cored HTS coils under typical operating conditions expected in a low speed direct drive machine for wind energy or marine propulsion, or a high speed machine for aerospace applications. Loss reduction techniques will also be investigated, in particular at high electrical frequency. Loss modelling will be linked to thermal modelling to investigate the impact on the cryogenic cooling system. The application of machine learning techniques for AC loss estimation will be investigated so that AC losses can be included in rapid machine design tools. Experimental test rigs will be built to verify the modelling work within this PhD. The PhD student will have the opportunity to engage with industrial partners within the wind, transport and superconducting sectors.
For further information Prof Markus Mueller | School of Engineering (ed.ac.uk)
Further Information:
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Closing Date:
Principal Supervisor:
Eligibility:
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.
Prospective candidates will be judged according to how well they meet the following criteria:
• At least a 2.1 honours degree in Engineering, Physics, Materials Science or a closely related discipline.
• Excellent written and spoken English communication skills.
• Excellent analytical skills.
• Strong programming background (e.g. Matlab, Python).
• Enthusiasm for electrical machines and applied superconductivity.
• The ability to work within a team.
• Experience using high performance computing resources.
• Experience of using numerical modelling packages such as COMSOL.
Funding:
Tuition fees + stipend are available for Home/EU and International students
The studentships are funded by The Royal Academy of Engineering Chair in Emerging Technology and University of Edinburgh. Both stipend and tuition fees are covered for 3 years with one of the studentships covering overseas tuition fees. Applications from non-UK resident students are therefore welcome.
