Details
Project reference code: MEC-04-Krynkin
This is the opportunity for a keen research student to become involved in an exciting programme of field and laboratory experiments to advance our understanding of river flows through new methods of remote sensing. The student will also have the opportunity to study advanced machine learning algorithms and apply them to make sense of the collected data. There are opportunities for the appointed person to travel extensively across the UK and to visit our overseas partners in Europe, North America and Australia. This cross-disciplinary project is funded by the Engineering and Physical Sciences Research Council (EPSRC) and supported by leading multinational companies in water and digital industries.
Accurate flow measurement in rivers is vital to monitor extreme events and to build well calibrated, reliable hydraulic models to predict accurately the depths, velocities, and the extent of floods. This information is also essential to provide the data needed for effective management of water resources in a river catchment. This project will develop new science and technology for non-contact monitoring of river flows and floods with a range of acoustic sensors and machine learning methods. This technology will require no contact with the water surface that makes it robust and flexible enough to measure a wide range of flow velocities and depths, under a wide range of environmental conditions. It is well known that the accurate characterization of the river free surfaces is key to effective flood prevention becoming an ever-important issue in light of climate change (UKCP18). This also links to the River and Coastal Maintaining Programme by the Environment Agency that identifies the requirement to monitor river system performance regularly and accurately predict water flows in rivers.
The properties of water waves measured by means of scattered electromagnetic or acoustic signals have been used to monitor currents, water depth, and wave spatial characteristics in the oceans, and surface flow velocities in rivers. This PhD project takes these ideas forward and is aimed at developing a robust mechanism to characterise surface and flow dynamics in challenging conditions including rivers. The appointed PhD student will have the opportunity to advance Machine learning methods including development of stochastic approaches (i.e. Markov Chain Monte Carlo) and build a platform for the surface characterisation technique with modelling acoustic problems in presence of the reflecting rough dynamic surface.
The project will involve laboratory and in-field measurements with the support of our industry partners. The key goal is to use acoustic waves to recover statistical properties of the dynamic water surface and underlying flow conditions.
Although the successful candidate will be based in the Department of Mechanical Engineering at the University of Sheffield, there will be plenty of opportunity to travel and work with our academic and non-academic partners across the globe. This PhD studentship also benefits from extensive inter-faculty collaboration available through the Pennine Water Group at Sheffield (https://www.sheffield.ac.uk/penninewatergroup), the Pipebots EPSRC Programme Grant (www.pipebots.ac.uk) and the UK Acoustic Network (https://acoustics.ac.uk/). The appointed candidate will be able to use two bespoke experimental facilities at Sheffield: UKCRIC’s National Distributed Water Infrastructure Facility (www.icair.ac.uk) and the Laboratory for Validation and Verification (www.lvv.ac.uk).
Applications should be made through the University of Sheffield on-line submission system and select code MEC-04-Krynkin https://www.sheffield.ac.uk/arpform/login.app?code=EPSRC
Interested candidates are strongly encouraged to contact the project supervisors to discuss your interest in and suitability for the project prior to submitting your application (contact Dr Anton Krynkin, [email protected]; Prof. Kirill Horoshenkov, [email protected]). Please refer to the EPSRC DTP webpage for detailed information about the EPSRC DTP and how to apply.
The successful candidate should have graduated (or expect to graduate before the start of this PhD project) with a good honours degree in Engineering, Physics, Applied Mathematics, Environmental Science or a related subject. They should be able to demonstrate an aptitude for research and a willingness to work flexibly, in a number of locations and environments. They will be expected to have good communication skills to publish their work and to present it to our partners and external peers.
Funding Notes
The award will fund the full (UK or Overseas) tuition fee and UKRI stipend (currently £18,622 per annum) for 3.5 years, as well as a research grant to support costs associated with the project.