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Details of Grant 

EPSRC Reference: EP/M021394/1
Title: Learning tidal currents
Principal Investigator: Adcock, Professor T
Other Investigators:
Osborne, Dr MA
Researcher Co-Investigators:
Project Partners:
E.ON E&P UK Ltd The UK Hydrographic Office
Department: Engineering Science
Organisation: University of Oxford
Scheme: First Grant - Revised 2009
Starts: 22 June 2015 Ends: 21 December 2016 Value (£): 98,208
EPSRC Research Topic Classifications:
Energy - Marine & Hydropower
EPSRC Industrial Sector Classifications:
Energy
Related Grants:
Panel History:
Panel DatePanel NameOutcome
25 Feb 2015 Engineering Prioritisation Panel Meeting 25 February 2015 Announced
Summary on Grant Application Form
Tides occur due to the changing gravitational movement of the Moon and Sun relative to the Earth. As astronomical movements are highly predictable the tides should also be predictable. This is one of the key advantages of tidal stream energy (a rapidly developing source of renewable energy). The existing methods which are used to predict tidal movements perform very well for predicting water levels and slow moving currents, but often perform very badly on fast flowing tidal streams of the type in which we areinteresting in placing tidal turbines. This project will address this by applying methods from the machine learning community to the analysis of fast flowing tidal streams. This will produce an algorithm which will allow users from the oceanographic and tidal energy community to greatly improve the prediction of tidal currents at any point indefinitely far into the future. Thus a robustprediction of the performance of tidal stream turbines can be obtained. In the rapidly growing area of tidal stream energy, accurate knowledge of the tidal currents is vital for: robust predictions of energy yield; for the calculation of loads and the design of the turbine; and to give confidence to investors.
Key Findings
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Summary
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Organisation Website: http://www.ox.ac.uk