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FindA University Ltd Featured PhD Programmes
FindA University Ltd Featured PhD Programmes

TRANSCEND: Advanced ultrasonic characterisation of slurry flows

  • Full or part time
  • Application Deadline
    Thursday, October 31, 2019
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

We present an opportunity for STEM graduates to be a part of an exciting new consortium in nuclear waste management and decommissioning, with a fully funded PhD in advanced ultrasonic characterisation of waste slurry flows, for remote process control of nuclear waste transfers.

This project is based within the School of Chemical and Process Engineering at the University of Leeds. The UK government is committed to nuclear energy having an important role in delivering secure, low-carbon and affordable energy, with their new build programme and life extension of the existing fleet. However, a significant barrier to this vision is the nuclear legacy from past reactor programmes.

Recently, the EPSRC and UK industry have funded a brand new consortium in Transformative Science and Engineering for Nuclear Decommissioning (TRANSCEND) to solve these critical issues from past generations of nuclear power, providing a clean nuclear future. The consortium comprises key industry partners and leading academic researchers from 11 research intensive universities across the UK, to investigate various aspects of nuclear waste management, processing, decommissioning and site remediation.

This PhD award will be fully integrated into the TRANSCEND programme. Specifically, we will seek in this project to develop transformative process analytical abilities, based on ultrasonic backscatter techniques, to fully characterise waste concentration and particle size non-intrusively online.

The aim will be to integrate innovative acoustical modelling into software tools for real-time feedback of radioactive particle levels and their state of aggregation, for remote process control as they are transported as slurries. This work will include the use of novel machine learning techniques for acoustic data analysis and pattern matching, whereby the influence of a number of acoustic parameters on the backscatter signal can simultaneously be assessed using artificial neural networks (ANN), for example.

It is noted that the successful applicant will be able to link directly to industry (e.g. by undertaking placements) and engage with other PhD students in the consortium, through annual symposiums and work package meetings.

Funding Notes

A full standard studentship consists of academic fees (£4,500 in Session 2019/20), together with a maintenance grant (£15,009 in Session 2019/20) paid at standard Research Council rates.

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