Impact of spatially varying flow conditions on the prediction of fatigue loads of a tidal turbine

Keywords: Blade Loading, Spatial Variation, Tidal Turbine

Abstract

Site development for tidal turbines relies upon a good understanding of the onset flow conditions, with disk averaged velocity typically used as a reference to define turbine power and mean loading. This work investigates the variation of onset flow conditions which occur for the same disk averaged velocity. Analysis builds upon data previously acquired during the measurement campaign conducted for the ReDAPT project using bed mounted ADCPs \cite{Sellar2018}. These measurements define the turbulence characteristics and vertical shear profiles over the rotor plane which are incorporated into an efficient blade element method for prediction of unsteady blade loads. This method allows efficient calculation of blade loading for multiple onset shear and turbulence profiles, each with the same disk average velocity, to determine the cyclic loading which contributes towards fatigue. Predictions of fatigue loads from measured profiles are compared with predictions from profiles predicted for the same location with a MIKE3 model \cite{Gunn2014}. Within the water depth two vertical positions are analysed, with vertical shear profiles from measurements and a multi-parameter model used to define the onset. For a near-bed location, use of the averaged predicted velocity profiles neglecting variation of turbulence intensity with flow-speed provides fatigue loads to within 1\% of predictions obtained using all measured profiles of velocity and corresponding turbulence intensity. For the near-surface location, the same approach under predicts fatigue loads by 16-19\%. This is partly due to the occurrence of a wider range of turbulence intensities. Since this is nearly constant with flow-speed a scaling factor is applied to load cycles from predicted profiles to estimate the aggregated fatigue load obtained using all measured conditions, providing confidence that accumulated fatigue loads can be predicted efficiently from velocity profiles obtained from shallow water models.

Author Biography

Tim Stallard, The University of Manchester

Professor of Offshore & Renewable Energy and Head of Department

Published
2022-06-21
How to Cite
Mullings, H., & Stallard, T. (2022). Impact of spatially varying flow conditions on the prediction of fatigue loads of a tidal turbine. International Marine Energy Journal, 5(1), 103-111. https://doi.org/10.36688/imej.5.103-111
Section
EWTEC 2021 Special issue papers (Part 1)