Design of Modular Air-cored High Temperature Superconducting Machines using Machine Learning Techniques for Direct Drive Wind Energy Applications


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. In offshore wind, existing low speed (<10rpm) direct drive generators exhibit only 50-60W/kg, which needs to at least double to reduce nacelle mass for turbines greater than 15MW. 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. The combination of HTS materials with composite structural materials of low mass density will lead to significant increase in power density across a range of applications.

Methodology and Objectives.

Modelling the characteristics of superconductors and in particular at high frequency is very challenging requiring the use of multi-physics tools such as COMSOL. Detailed modelling for any minor design change is time consuming. No generic design tools exist for industry to use on a day-to-day basis to assess the technology. The SuperMachine platform based on arrays of air-cored HTS coils, lends itself to the use of analytical techniques such as the Biot-Savart method to obtain a first

order estimate of magnetic field distribution. Such an analytical method is useful as a sizing tool providing initial estimates of machine dimensions and performance, but characteristics of the superconducting materials are not included. A recent PhD at Edinburgh demonstrated the use of machine learning techniques for generic design of superconducting flux pumps (Wen et al 2022, COMSOL was used to generate a large dataset for rotating flux pumps and linear travelling wave flux pumps, and with the application of machine learning techniques it is now possible to generate designs in minutes rather than days.

In this PhD project the student will develop advanced integrated electromagnetic-structural-thermal design tools for the modular air-cored SuperMachine concept, combining COMSOL with Machine Learning techniques. The focus will be on low speed multi-MW direct drive wind turbines, with the aim of demonstrating designs at greater than 15MW with a generator power density of 200 W/kg or more. Funding is available to design, build and manufacture an air-cored superconducting module, which will be used to verify the design and modelling tools developed. The PhD student will have the opportunity to engage with industrial partners within the wind energy and superconducting sectors.

Informal enquiries are encouraged and should be addressed to Prof Markus Mueller at

Further Information: 

Prof Markus Mueller | School of Engineering (

The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here:

Closing Date: 

Monday, April 17, 2023

Principal Supervisor: 


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.

The following skills are desirable but not essential:

  • Experience using high performance computing resources.
  • Experience of using numerical modelling packages such as COMSOL.


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.

Further information and other funding options.

Informal Enquiries: