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Multiscale modelling of particle breakage and milling

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  • Full or part time
    Dr Stefanos Papanicolopulos
  • Application Deadline
    Applications accepted all year round
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

The Granular Mechanics and Industrial Infrastructure research group, at the School of Engineering of the University of Edinburgh, conducts fundamental research on the mechanics of granular materials and their interaction with industrial infrastructure, with broad applications in silo design, bulk solids handling, paste rheology, fluidisation and natural hazard mitigation. We are currently looking for candidates for the following funded PhD positions:

Multiscale modelling of particle breakage and milling

Milling is an important process to achieve desirable particle-size reduction in many industries including construction, food, pharmaceutical, chemical and mining industries. In order to control product properties, optimise energy use and prolong device life, we propose to develop a predictive toolbox for industrial milling process. We focus on coupling fracturing at particle level with realistic particle dynamics in industrial mills; and on upscaling from discrete to continuum and to statistical behaviour. Development in discrete element method and population balance models will be calibrated using relevant experimental data.

Contact: Dr Jin Sun ([email protected])


Applicants must be of outstanding academic merit and hold (or be expected to gain) either a first class honours degree (or the international equivalent) or an MSc with distinction (or the international equivalent). Enthusiastic and self-motivated candidates are sought with a solid background in civil, mechanical, chemical engineering, or in physics and mechanics. A good grasp of mechanics and experience in programming and computational modelling would be advantageous.

How good is research at University of Edinburgh in General Engineering?
(joint submission with Heriot-Watt University)

FTE Category A staff submitted: 91.80

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

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