10.1007/s40095-022-00519-z

Comparison of dense optical flow and PIV techniques for mapping surface current flow in tidal stream energy sites

  1. Environmental Research Institute, North Highland College, University of the Highlands and Islands, Thurso, KW14 7EE, GB
  2. Swansea University, Swansea, GB
  3. Bangor University, Bangor, GB
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Published in Issue 2022-09-02

How to Cite

McIlvenny, J., Williamson, B. J., Fairley, I. A., Lewis, M., Neill, S., Masters, I., & Reeve, D. E. (2022). Comparison of dense optical flow and PIV techniques for mapping surface current flow in tidal stream energy sites. International Journal of Energy and Environmental Engineering, 14(3 (September 2023). https://doi.org/10.1007/s40095-022-00519-z

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Abstract

Abstract Marine renewable energy site and resource characterisation, in particular tidal stream energy, require detailed flow measurements which often rely on high-cost in situ instrumentation which is limited in spatial extent. We hypothesise uncrewed aerial vehicles (UAV) offer a low-cost and low-risk data collection method for tidal stream environments, as recently techniques have been developed to derive flow from optical videography. This may benefit tidal and floating renewable energy developments, providing additional insight into flow conditions and complement traditional instrumentation. Benefits to existing data collection methods include capturing flow over a large spatial extent synchronously, which could be used to analyse flow around structures or for site characterisation; however, uncertainty and method application to tidal energy sites is unclear. Here, two algorithms are tested: large-scale particle image velocimetry using PIVlab and dense optical flow. The methods are applied on video data collected at two tidal stream energy sites (Pentland Firth, Scotland, and Ramsey Sound, Wales) for a range of flow and environmental conditions. Although average validation measures were similar (~ 20–30% error), we recommend PIVlab processed velocity data at tidal energy sites because we find bias (underprediction) in optical flow for higher velocities (> 1 m/s).

Keywords

  • Tidal stream,
  • Remote sensing,
  • Energy,
  • Drones,
  • UAV,
  • Optical flow

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