An enhanced and validated performance and cavitation prediction model for horizontal axis tidal turbines. (September 2017) - Kaufmann, N.; Carolus, T.H.; Starzmann, R.
Highlights: A performance and cavitation prediction model for tidal stream turbines is presented. The model is based on the blade element momentum theory and refined by submodels. Cavitation inception is predicted for a wide range of operating conditions. The experimental data from two different turbines was used for validation. The predictions agreed well with the experimental data. Abstract: Tidal energy represents a promising resource for the future energy mix. For harnessing tidal currents free stream horizontal axis turbines have been investigated for some years. The acting physics is very similar to the one of horizontal axis wind turbines, with the additional phenomenon of cavitation, which causes performance reduction, flow induced noise and severe damages to the turbine blade and downstream structures. The paper presents an enhanced semi-analytical model that allows the prediction of the performance characteristics including cavitation inception of horizontal axis tidal turbines. A central component is the well-known blade element momentum theory which is refined by various submodels for hydrofoil section lift and drag as a function Reynolds number and angle of attack, turbine thrust coefficient, blade hub and tip losses and cavitation. Moreover, the model is validated by comparison with comprehensive experimental data from two different turbines. Predicted power and thrust coefficient characteristics were found to agree well with the experimental results for a wide operational range and different inflow velocities. Discrepancies were observed only at low tip speed ratios where major parts of the blades operate under stall conditions. The predicted critical cavitation number is somewhat larger than the measured, i.e. the prediction is conservative. As an overall conclusion the semi-analytical model developed seems to be so fast, accurate and robust that it can be integrated in a future workflow for optimizing tidal turbines.