Synthesis and characterisation of turbulent flows to predict fatigue loading of tidal turbines in arrays

The onset flows used for tidal turbine design are, at present, based on simplifications of the naturally occurring tidal flow, and design standards consider in-wake loading based on an effective turbulence parameter. This work uses a quasi-steady blade element momentum model to assess alternative unsteady onset flow characterisation methods for prediction of the fatigue loading of turbines, when operating in the complex onset flows that occur in an array. Fatigue loads are evaluated for each turbine within a two-row array using onset turbulent flow from both a large eddy simulation and synthesised by superposition of shear, an empirical wake model and a spectral turbulence model. This is shown to provide blade damage equivalent loads within 1% for a turbine operating in the highest transverse shear region when compared to the direct use of the Large Eddy Simulation (LES) flow fields. Fatigue loads vary with turbulence and onset shear. Approaches for characterising such flows and resultant loads are evaluated including based on effective turbulence and on the signal to noise ratio (SNR) of the relative velocity to the blade at a radial position close to the centroid of the load. In isolation each approach provides loading to within 20% of the predicted trend for only up to 50% of cases. However, a combined approach considering the SNR when greater than 0.2 enables the SNR method to calculate to within 20% of the predicted trend for 97% of cases. For the cases where the SNR value is less than 0.2, the predicted trend from the relationship between effective turbulence and normalised damage equivalent load, can predict the loads to within 20% for 88% of the cases.

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Publication Details

Journal
Journal of Fluids and Structures
Published
2025-04-24
DOI
https://doi.org/10.1016/j.jfluidstructs.2025.104321
Citations
4
Primary Topic
Wind Energy Research and Development
Type
article
Field-Weighted Citation Impact
19.97

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article

Synthesis and characterisation of turbulent flows to predict fatigue loading of tidal turbines in arrays

Tim Stallard, Pablo Ouro, Hannah Mullings
4 citations
Journal of Fluids and Structures
Wind Energy Research and Development
19.97
article

Synthesis and characterisation of turbulent flows to predict fatigue loading of tidal turbines in arrays

Tim Stallard, Pablo Ouro, Hannah Mullings
article en
4 citations

Abstract

The onset flows used for tidal turbine design are, at present, based on simplifications of the naturally occurring tidal flow, and design standards consider in-wake loading based on an effective turbulence parameter. This work uses a quasi-steady blade element momentum model to assess alternative unsteady onset flow characterisation methods for prediction of the fatigue loading of turbines, when operating in the complex onset flows that occur in an array. Fatigue loads are evaluated for each turbine within a two-row array using onset turbulent flow from both a large eddy simulation and synthesised by superposition of shear, an empirical wake model and a spectral turbulence model. This is shown to provide blade damage equivalent loads within 1% for a turbine operating in the highest transverse shear region when compared to the direct use of the Large Eddy Simulation (LES) flow fields. Fatigue loads vary with turbulence and onset shear. Approaches for characterising such flows and resultant loads are evaluated including based on effective turbulence and on the signal to noise ratio (SNR) of the relative velocity to the blade at a radial position close to the centroid of the load. In isolation each approach provides loading to within 20% of the predicted trend for only up to 50% of cases. However, a combined approach considering the SNR when greater than 0.2 enables the SNR method to calculate to within 20% of the predicted trend for 97% of cases. For the cases where the SNR value is less than 0.2, the predicted trend from the relationship between effective turbulence and normalised damage equivalent load, can predict the loads to within 20% for 88% of the cases.

Journal of Fluids and StructuresVol. 136
University of Manchester (GB)
Engineering and Physical Sciences Research Council
Affordable and clean energy
Openalex Percentile: Top 1%
Wind Energy Research and Development
19.97
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Synthesis and characterisation of turbulent flows to predict fatigue loading of tidal turbines in arrays — Tim Stallard, Pablo Ouro, et al. · Journal of Fluids and Structures (2025) | TGRS Research Map | TGRS