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Consortium develops advanced wake modelling capability for the wind sector

The Centre for Modelling and Simulation (CFMS), a specialist in digitally enabled high value design capability, has today announced the results disseminated from its participation in the Innovate UK funded second Simulated Wake Effects Platform for Turbines (SWEPT2) collaborative project, led by DNV GL.

The United Kingdom is one of the best locations for wind power in the world, and is considered to be the best in Europe. Wind power contributed 11% of UK electricity generation in 2015, and 17% in December 2015. Allowing for the costs of pollution, onshore wind power is the cheapest form of energy in the United Kingdom. The use of computational fluid dynamics (CFD) to assess the wind energy resource for a prospective new site is an established method that has been used for many years, however the inclusion of wake interaction effects – particularly for larger arrays of turbines – is less mature.

With the availability of additional data sources – including SCADA and LIDAR – the wind energy sector is now looking to modernise and tune its simulation capability to provide greater confidence in the predicted yield and life-cycle of new onshore and offshore turbine arrays.

The Simulated Wake Effects Platform for Turbines Project (SWEPT2) consortium (led by DNV-GL, with SSE, ORE Catapult, STFC Hartree Centre, The Centre for Modelling and Simulation (CFMS), Zenotech and the University of Surrey, University of Strathclyde, University of Bristol and Imperial College) has been developing new tools to improve the utility and accuracy of CFD-based wake interaction modelling. Supported by Innovate UK, the UK’s innovation agency, and with a project value of £1.48 million, SWEPT2 began on 1st May 2015 and runs for three years, completing this year.

The SWEPT2 consortium is investigating the use of machine learning methods to infer the wake interaction patterns and consequent power production from sites, given sufficient training data. CFMS is leading the use of Artificial Intelligence (AI) technology to deliver insights from the large volumes of data being produced, in close collaboration with the University of Liverpool Institute for Risk and Uncertainty and the Warwick Centre for Predictive Modelling (supported by The KTN and Innovate UK). Datasets include a range of wind directions, terrain maps (including roughness parameters and tree canopies) and full CFD fields (millions of data-points, including velocity, pressure, temperature and turbulence quantities of interest).

The full Dissemination Report for SWEPT2 can be accessed, along with the SWEPT2 wind farm models which are available for external use and can be downloaded. Access to zCFD, which is available online on-demand via EPIC.

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