Objective representative flow field selection for tidal array layout design

The representation of flow across influential spatiotemporal scales introduces a challenge when micro-siting tidal stream turbine arrays. Robust representative approximations could accelerate design optimisation, yet there is no consensus on what defines the most appropriate flow conditions. We summarise existing approaches to representative flow field selection for array optimisation and propose an objective-driven process. The method curates a subset of flow fields that best captures relevant dynamics, enabling the streamlined representation of tidal cycles. To demonstrate the method, we consider flow modelling data in the Inner Sound of the Pentland Firth, Scotland, UK. We examine the impact of flow field inputs to array design through comparative analyses using a heuristic array optimisation process. Results indicate notable sensitivity of the turbine layout to the flow conditions selected. For the case study presented, our method led to 4%–5% energy yield prediction improvements relative to use of simple time-interval based approaches and up to 2% improvement against using peak flow fields; these can be pivotal margins to secure feasibility by developers. We also find that using the data associated with a single monitored point across the array for flow field selection can lead to sub-optimal results, emphasising the need for accurate spatiotemporal representation. • Flow fields used in optimisation impact array shape and degree of wake interaction. • Novel statistical approach is applied for an 86 MW array in the Pentland Firth, UK. • Leads to 2%–5% higher micro-sited array productivity over empirical approaches. • Transferrable to device-scale modelling and representative cycle selection studies.

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Publication Details

Journal
Renewable Energy
Published
2024-09-20
DOI
https://doi.org/10.1016/j.renene.2024.121381
Citations
2
Primary Topic
Wind Energy Research and Development
Type
article
Field-Weighted Citation Impact
2.60

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article

Objective representative flow field selection for tidal array layout design

Joseba Andoni Agirre, Connor Jordan, Athanasios Angeloudis
2 citations
Renewable Energy
Wind Energy Research and Development
2.60
article

Objective representative flow field selection for tidal array layout design

Joseba Andoni Agirre, Connor Jordan, Athanasios Angeloudis
article en
2 citations

Abstract

The representation of flow across influential spatiotemporal scales introduces a challenge when micro-siting tidal stream turbine arrays. Robust representative approximations could accelerate design optimisation, yet there is no consensus on what defines the most appropriate flow conditions. We summarise existing approaches to representative flow field selection for array optimisation and propose an objective-driven process. The method curates a subset of flow fields that best captures relevant dynamics, enabling the streamlined representation of tidal cycles. To demonstrate the method, we consider flow modelling data in the Inner Sound of the Pentland Firth, Scotland, UK. We examine the impact of flow field inputs to array design through comparative analyses using a heuristic array optimisation process. Results indicate notable sensitivity of the turbine layout to the flow conditions selected. For the case study presented, our method led to 4%–5% energy yield prediction improvements relative to use of simple time-interval based approaches and up to 2% improvement against using peak flow fields; these can be pivotal margins to secure feasibility by developers. We also find that using the data associated with a single monitored point across the array for flow field selection can lead to sub-optimal results, emphasising the need for accurate spatiotemporal representation. • Flow fields used in optimisation impact array shape and degree of wake interaction. • Novel statistical approach is applied for an 86 MW array in the Pentland Firth, UK. • Leads to 2%–5% higher micro-sited array productivity over empirical approaches. • Transferrable to device-scale modelling and representative cycle selection studies.

Renewable EnergyVol. 236
University of Edinburgh (GB)
Horizon 2020 Framework Programme, Engineering and Physical Sciences Research Council, H2020 Societal Challenges
Climate action
Openalex Percentile: Top 8%
Wind Energy Research and Development
2.60
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