Projects
Projects: Projects for Investigator |
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| Reference Number | EP/M021394/1 | |
| Title | Learning tidal currents | |
| Status | Completed | |
| Energy Categories | Renewable Energy Sources(Ocean Energy) 100%; | |
| Research Types | Basic and strategic applied research 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 100% | |
| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Dr T Adcock No email address given Engineering Science University of Oxford |
|
| Award Type | Standard | |
| Funding Source | EPSRC | |
| Start Date | 22 June 2015 | |
| End Date | 21 December 2016 | |
| Duration | 18 months | |
| Total Grant Value | £98,208 | |
| Industrial Sectors | Energy | |
| Region | South East | |
| Programme | NC : Engineering | |
| Investigators | Principal Investigator | Dr T Adcock , Engineering Science, University of Oxford (99.999%) |
| Other Investigator | Dr M A Osborne , Engineering Science, University of Oxford (0.001%) |
|
| Industrial Collaborator | Project Contact , E.ON E&P UK Ltd (0.000%) Project Contact , The UK Hydrographic Office (0.000%) |
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| Web Site | ||
| Objectives | ||
| Abstract | 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 | |
| Publications | (none) |
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| Final Report | (none) |
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| Added to Database | 20/07/15 | |
