Channel-scale optimisation and tuning of large tidal turbine arrays using LES with adaptive mesh. (February 2016)

• Record Type: Journal Article
• Authors: Divett, Tim; Vennell, Ross; Stevens, Craig
• Is Part Of: Renewable energy. Volume 86(2016)
• Page Start: 1394
• Page End: 1405
• Publication Date: 2016-02
• Subjects: Tidal power -- Tidal energy arrays -- Power capture optimisation -- Adaptive mesh modelling -- Array tuning; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2015.09.048
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Large arrays of tidal turbines are critical to realise the potential of tidal current power. This study is a systematic exploration of large tidal array optimisation in channels with numerically modelled array layouts in 2-D. Crucially, flow along channels is driven by head loss leading to significantly more realistic results than previous models which assume constant velocity. The 2-D adaptive mesh approach bridges the gap between large- and small-scale array models. Hundreds of layouts and turbine tunings have been simulated using LES of turbulent flow in tidally reversing currents to explore channel-scale optimisation and tuning of large arrays. Simulations show that total power capture increases as rows are added to the array although there are diminishing returns on additional turbines. Each turbine in 1 (7), optimally blocked row in a small channel captures 2.5 × ( 0.5 × ) the power of an isolated turbine. There is an optimum blockage for maximum power per turbine which decreases linearly from 1.0 as the number of rows increases. As array size increases individual turbine wakes become less important than stepped head loss across each row. Free-stream velocity reduces linearly with total power capture, with the gradient increasing with channel size. Highlights: We explore large tidal array optimisation for maximum power capture in channels. We model arrays using 2-D adaptive mesh LES with individual turbines represented. One highly blocked row captures the most power per turbine, agreeing with previous models. Optimum blockage decreases linearly from 1.0 for 1 row, to 0.4 for 7 rows. Free-stream velocity increases linearly with total power capture as array size increases.