Learning tidal currents

学习潮流

• 批准号: EP/M021394/1
• 负责人: Thomas Adcock
• 金额: $ 12.51万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FM021394%2F1
• 关键词: Learning; tidal; currents

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.

潮汐发生是由于月球和太阳相对于地球的重力运动而发生的。由于天文学运动是高度可预测的，因此潮汐也应该是可预测的。这是潮汐流能量（快速发展的可再生能源来源）的关键优势之一。用于预测潮汐运动的现有方法在预测水位和缓慢移动的电流方面表现良好，但通常在我们对放置潮汐涡轮机上的类型的快速流动潮流中的表现非常出色。该项目将通过将机器学习社区的方法应用于快速流动的潮汐流的分析来解决这一问题。这将产生一种算法，该算法将使来自海洋和潮汐能社区的用户能够无限期地无限期地改善潮流的预测。因此，可以获得潮汐涡轮机的性能的鲁棒性。在潮汐流的快速生长的地区，潮流的准确了解对于：能量产量的强大预测至关重要；用于计算负载和涡轮机的设计；并给投资者有信心。

项目成果

Analytical and computational modelling for wave energy systems: the example of oscillating wave surge converters.

• DOI: 10.1007/s10409-017-0683-6
• 发表时间: 2017
• 期刊: Acta mechanica Sinica = Li xue xue bao
• 作者: Dias F;Renzi E;Gallagher S;Sarkar D;Wei Y;Abadie T;Cummins C;Rafiee A
• 通讯作者: Rafiee A

Prediction of tidal currents using Bayesian machine learning

• DOI: 10.1016/j.oceaneng.2018.03.007
• 发表时间: 2018-06
• 期刊: Ocean Engineering
• 影响因子: 5
• 作者: Dripta Sarkar;Michael A. Osborne;T. Adcock

Spatiotemporal Prediction of Tidal Currents Using Gaussian Processes

• DOI: 10.1029/2018jc014471
• 发表时间: 2019-04
• 期刊: Journal of Geophysical Research: Oceans
• 作者: Dripta Sarkar;Michael A. Osborne;T. Adcock