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Driving forward the future of magnetic materials through advanced micromagnetic simulation

  • Full or part time
  • Application Deadline
    Sunday, March 15, 2020
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

NOTE: This is an industrial CASE (Cooperative Awards in Science & Technology) award supported by Volkswagen (VW) and involves a three-month placement at their Research Centre in Wolfsburg, Germany.

The demand for high performance permanent magnets for clean energy applications such as the power systems in automotive applications, have increased enormously in recent years. These technologies require conversion of electricity to motion via a motor, as in hybrid or all-electric vehicles. These are usually sintered from powders to form solids that contain crystal grains ranging from sub-micrometre to low-micrometre in size. Materials based on the Nd-Fe-B class are usually used (90% permanent magnet market share) as these have the highest magnetic energy density (512 kJ.m-3). These materials typically contain a significant proportion of ‘rare earth’ elements. While these do improve the performance of the magnets, are expensive and have fluctuated in price significantly over recent years.

In many applications the magnetic materials are required to operate at elevated temperature. Though NdFeB shows excellent properties at room temperature, its Curie temperature is much lower than the alternative SmCo, and as such NdFeB based magnets’ anisotropy, and thus efficiency, falls quickly. The addition of some rare earth elements increase the operating range of the magnet, but typically any applications above 175°C would require a SmCo based magnet.

This project will build up our in-house state-of-the-art microstructural generation software allowing fast generation of randomised, but controllable, realistic three-dimensional granular type structures. It provides us the ability to create microstructural features such as irregular shaped grains each with a core-shell structure as well as secondary phases, roughness, and porosity. This allows replication of realistic morphologies to be solved for their magnetic behaviour using either finite element modelling (FEM) and/or GPU accelerated finite difference packages, computation of the magnetisation dynamics can be simulated by solving of the Landau–Lifshitz–Gilbert equation.

The focus of this work will look at the simulating the response from different magnetic materials and include effects such as diffusion, grain boundaries and grain shape. Furthermore, we shall combine the results with machine learning in order to map out possible optimised structures with a focus on volume fraction, geometrical configuration and desired material properties, in order to provide design criteria. Finally, we shall look towards implementing larger, coarser scale simulations, using the results gather from the smaller, detailed ones, to identify at how these local properties effect in-situ performance. The PhD is strongly aligned to other PhD students in our group, also supported by VW, who will be providing experimental support. They will be able to generate key input data for the models as well as test any predictions and design criteria the simulations generate results of which can be fed back into the models.

Funding Notes

This studentship will pay tuition fees in full and a stipend for living expenses for four years. This stipend will be at the RCUK minimum which for the 2019/20 academic year is £15,009pa, plus an enhancement of £2,500.

Funding covers home tuition fees and annual maintenance payments of at least the Research Council minimum for eligible UK and EU applicants. EU nationals must have lived in the UK for 3 years prior to the start of the programme to be eligible for a full award (fees and stipend).

How good is research at University of Sheffield in Electrical and Electronic Engineering, Metallurgy and Materials?
Materials Science and Engineering

FTE Category A staff submitted: 34.80

Research output data provided by the Research Excellence Framework (REF)

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