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Drone-based large-scale particle image velocimetry applied to tidal stream energy resource assessment
Iain Fairley,
Benjamin J. Williamson,
Jason McIlvenny,
Nicholas King,
Ian Masters 
,
Matthew Lewis,
Simon Neill,
David Glasby,
Daniel Coles ,
Ben Powell,
Keith Naylor,
Max Robinson,
Dominic E. Reeve,
Keith Naylor,
Dominic Reeve
,
Matthew Lewis,
Simon Neill,
David Glasby,
Daniel Coles ,
Ben Powell,
Keith Naylor,
Max Robinson,
Dominic E. Reeve,
Keith Naylor,
Dominic Reeve
Renewable Energy, Volume: 196, Pages: 839 - 855
Swansea University Authors:
Iain Fairley, Nicholas King, Ian Masters , David Glasby, Max Robinson, Keith Naylor, Dominic Reeve
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DOI (Published version): 10.1016/j.renene.2022.07.030
Abstract
Resource quantification is vital in developing a tidal stream energy site but challenging in high energy areas. Drone-based large-scale particle image velocimetry (LSPIV) may provide a novel, low cost, low risk approach that improves spatial coverage compared to ADCP methods. For the first time, thi...
| Published in: | Renewable Energy |
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| ISSN: | 0960-1481 |
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Elsevier BV
2022
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa60456 |
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<?xml version="1.0"?><rfc1807><datestamp>2022-10-31T20:20:42.0063184</datestamp><bib-version>v2</bib-version><id>60456</id><entry>2022-07-12</entry><title>Drone-based large-scale particle image velocimetry applied to tidal stream energy resource assessment</title><swanseaauthors><author><sid>568e6f260489dc8139afe77757553513</sid><firstname>Iain</firstname><surname>Fairley</surname><name>Iain Fairley</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>2305b5d9c024cf97a2feeb832af9228b</sid><firstname>Nicholas</firstname><surname>King</surname><name>Nicholas King</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>6fa19551092853928cde0e6d5fac48a1</sid><ORCID>0000-0001-7667-6670</ORCID><firstname>Ian</firstname><surname>Masters</surname><name>Ian Masters</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>5e4b6c78d6d29689f87fb0a76c646bc6</sid><firstname>David</firstname><surname>Glasby</surname><name>David Glasby</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>1ce8bc2a4ca70d8acbf95fb99b1a75d9</sid><firstname>Max</firstname><surname>Robinson</surname><name>Max Robinson</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>8dfb698508f44995bc4db200b62d7431</sid><firstname>Keith</firstname><surname>Naylor</surname><name>Keith Naylor</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>3e76fcc2bb3cde4ddee2c8edfd2f0082</sid><ORCID>0000-0003-1293-4743</ORCID><firstname>Dominic</firstname><surname>Reeve</surname><name>Dominic Reeve</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2022-07-12</date><deptcode>FGSEN</deptcode><abstract>Resource quantification is vital in developing a tidal stream energy site but challenging in high energy areas. Drone-based large-scale particle image velocimetry (LSPIV) may provide a novel, low cost, low risk approach that improves spatial coverage compared to ADCP methods. For the first time, this study quantifies performance of the technique for tidal stream resource assessment, using three sites. Videos of the sea surface were captured while concurrent validation data were obtained (ADCP and surface drifters). Currents were estimated from the videos using LSPIV software. Variation in accuracy was attributed to wind, site geometry and current velocity. Root mean square errors (RMSEs) against drifters were 0.44 m s−1 for high winds (31 kmh) compared to 0.22 m s−1 for low winds (10 kmh). Better correlation was found for the more constrained site (r2 increased by 4%); differences between flood and ebb indicate the importance of upstream bathymetry in generating trackable surface features. Accuracy is better for higher velocities. A power law current profile approximation enables translation of surface current to currents at depth with satisfactory performance (RMSE = 0.32 m s−1 under low winds). Overall, drone video derived surface velocities are suitably accurate for “first-order” tidal resource assessments under favourable environmental conditions.</abstract><type>Journal Article</type><journal>Renewable Energy</journal><volume>196</volume><journalNumber/><paginationStart>839</paginationStart><paginationEnd>855</paginationEnd><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0960-1481</issnPrint><issnElectronic/><keywords>ocean energy; resource mapping; unmanned aerial vehicles; surface velocimetry; oceanography; remote sensing</keywords><publishedDay>1</publishedDay><publishedMonth>8</publishedMonth><publishedYear>2022</publishedYear><publishedDate>2022-08-01</publishedDate><doi>10.1016/j.renene.2022.07.030</doi><url/><notes/><college>COLLEGE NANME</college><department>Science and Engineering - Faculty</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>FGSEN</DepartmentCode><institution>Swansea University</institution><apcterm>SU Library paid the OA fee (TA Institutional Deal)</apcterm><funders>The authors would like to acknowledge the financial support of the EPSRC Supergen ORE Hub (EP/S000747/1) funded V-SCORES project. The financial support of the Selkie Project is also acknowledged. The Selkie Project is funded by the European Regional Development Fund through the Ireland Wales Cooperation programme. We also acknowledge the support of SEEC (Smart Efficient Energy Centre) at Bangor University, part-funded by the European Regional Development Fund (ERDF), administered by the Welsh Government. M Lewis also wishes to acknowledge the EPSRC fellowship METRIC: EP/R034664/1. D Coles acknowledges the financial support of the EPSRC fellowship which is co-financed by the European Regional Development Fund through the Interreg France (Channel) England Programme.</funders><projectreference/><lastEdited>2022-10-31T20:20:42.0063184</lastEdited><Created>2022-07-12T10:01:49.6872191</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Engineering and Applied Sciences - Uncategorised</level></path><authors><author><firstname>Iain</firstname><surname>Fairley</surname><order>1</order></author><author><firstname>Benjamin J.</firstname><surname>Williamson</surname><order>2</order></author><author><firstname>Jason</firstname><surname>McIlvenny</surname><order>3</order></author><author><firstname>Nicholas</firstname><surname>King</surname><order>4</order></author><author><firstname>Ian</firstname><surname>Masters</surname><orcid>0000-0001-7667-6670</orcid><order>5</order></author><author><firstname>Matthew</firstname><surname>Lewis</surname><order>6</order></author><author><firstname>Simon</firstname><surname>Neill</surname><order>7</order></author><author><firstname>David</firstname><surname>Glasby</surname><order>8</order></author><author><firstname>Daniel</firstname><surname>Coles</surname><orcid>0000-0002-5676-4849</orcid><order>9</order></author><author><firstname>Ben</firstname><surname>Powell</surname><order>10</order></author><author><firstname>Keith</firstname><surname>Naylor</surname><order>11</order></author><author><firstname>Max</firstname><surname>Robinson</surname><order>12</order></author><author><firstname>Dominic E.</firstname><surname>Reeve</surname><order>13</order></author><author><firstname>Keith</firstname><surname>Naylor</surname><order>14</order></author><author><firstname>Dominic</firstname><surname>Reeve</surname><orcid>0000-0003-1293-4743</orcid><order>15</order></author></authors><documents><document><filename>60456__24815__7dc4330af60047c98075c494253c0386.pdf</filename><originalFilename>60456.pdf</originalFilename><uploaded>2022-08-03T11:38:48.8972972</uploaded><type>Output</type><contentLength>6622495</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>© 2022 The Authors. 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2022-10-31T20:20:42.0063184 v2 60456 2022-07-12 Drone-based large-scale particle image velocimetry applied to tidal stream energy resource assessment 568e6f260489dc8139afe77757553513 Iain Fairley Iain Fairley true false 2305b5d9c024cf97a2feeb832af9228b Nicholas King Nicholas King true false 6fa19551092853928cde0e6d5fac48a1 0000-0001-7667-6670 Ian Masters Ian Masters true false 5e4b6c78d6d29689f87fb0a76c646bc6 David Glasby David Glasby true false 1ce8bc2a4ca70d8acbf95fb99b1a75d9 Max Robinson Max Robinson true false 8dfb698508f44995bc4db200b62d7431 Keith Naylor Keith Naylor true false 3e76fcc2bb3cde4ddee2c8edfd2f0082 0000-0003-1293-4743 Dominic Reeve Dominic Reeve true false 2022-07-12 FGSEN Resource quantification is vital in developing a tidal stream energy site but challenging in high energy areas. Drone-based large-scale particle image velocimetry (LSPIV) may provide a novel, low cost, low risk approach that improves spatial coverage compared to ADCP methods. For the first time, this study quantifies performance of the technique for tidal stream resource assessment, using three sites. Videos of the sea surface were captured while concurrent validation data were obtained (ADCP and surface drifters). Currents were estimated from the videos using LSPIV software. Variation in accuracy was attributed to wind, site geometry and current velocity. Root mean square errors (RMSEs) against drifters were 0.44 m s−1 for high winds (31 kmh) compared to 0.22 m s−1 for low winds (10 kmh). Better correlation was found for the more constrained site (r2 increased by 4%); differences between flood and ebb indicate the importance of upstream bathymetry in generating trackable surface features. Accuracy is better for higher velocities. A power law current profile approximation enables translation of surface current to currents at depth with satisfactory performance (RMSE = 0.32 m s−1 under low winds). Overall, drone video derived surface velocities are suitably accurate for “first-order” tidal resource assessments under favourable environmental conditions. Journal Article Renewable Energy 196 839 855 Elsevier BV 0960-1481 ocean energy; resource mapping; unmanned aerial vehicles; surface velocimetry; oceanography; remote sensing 1 8 2022 2022-08-01 10.1016/j.renene.2022.07.030 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University SU Library paid the OA fee (TA Institutional Deal) The authors would like to acknowledge the financial support of the EPSRC Supergen ORE Hub (EP/S000747/1) funded V-SCORES project. The financial support of the Selkie Project is also acknowledged. The Selkie Project is funded by the European Regional Development Fund through the Ireland Wales Cooperation programme. We also acknowledge the support of SEEC (Smart Efficient Energy Centre) at Bangor University, part-funded by the European Regional Development Fund (ERDF), administered by the Welsh Government. M Lewis also wishes to acknowledge the EPSRC fellowship METRIC: EP/R034664/1. D Coles acknowledges the financial support of the EPSRC fellowship which is co-financed by the European Regional Development Fund through the Interreg France (Channel) England Programme. 2022-10-31T20:20:42.0063184 2022-07-12T10:01:49.6872191 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Iain Fairley 1 Benjamin J. Williamson 2 Jason McIlvenny 3 Nicholas King 4 Ian Masters 0000-0001-7667-6670 5 Matthew Lewis 6 Simon Neill 7 David Glasby 8 Daniel Coles 0000-0002-5676-4849 9 Ben Powell 10 Keith Naylor 11 Max Robinson 12 Dominic E. Reeve 13 Keith Naylor 14 Dominic Reeve 0000-0003-1293-4743 15 60456__24815__7dc4330af60047c98075c494253c0386.pdf 60456.pdf 2022-08-03T11:38:48.8972972 Output 6622495 application/pdf Version of Record true © 2022 The Authors. This is an open access article under the CC BY license. true eng http://creativecommons.org/licenses/by/4.0/ |
| title |
Drone-based large-scale particle image velocimetry applied to tidal stream energy resource assessment |
| spellingShingle |
Drone-based large-scale particle image velocimetry applied to tidal stream energy resource assessment Iain Fairley Nicholas King Ian Masters David Glasby Max Robinson Keith Naylor Dominic Reeve |
| title_short |
Drone-based large-scale particle image velocimetry applied to tidal stream energy resource assessment |
| title_full |
Drone-based large-scale particle image velocimetry applied to tidal stream energy resource assessment |
| title_fullStr |
Drone-based large-scale particle image velocimetry applied to tidal stream energy resource assessment |
| title_full_unstemmed |
Drone-based large-scale particle image velocimetry applied to tidal stream energy resource assessment |
| title_sort |
Drone-based large-scale particle image velocimetry applied to tidal stream energy resource assessment |
| author_id_str_mv |
568e6f260489dc8139afe77757553513 2305b5d9c024cf97a2feeb832af9228b 6fa19551092853928cde0e6d5fac48a1 5e4b6c78d6d29689f87fb0a76c646bc6 1ce8bc2a4ca70d8acbf95fb99b1a75d9 8dfb698508f44995bc4db200b62d7431 3e76fcc2bb3cde4ddee2c8edfd2f0082 |
| author_id_fullname_str_mv |
568e6f260489dc8139afe77757553513_***_Iain Fairley 2305b5d9c024cf97a2feeb832af9228b_***_Nicholas King 6fa19551092853928cde0e6d5fac48a1_***_Ian Masters 5e4b6c78d6d29689f87fb0a76c646bc6_***_David Glasby 1ce8bc2a4ca70d8acbf95fb99b1a75d9_***_Max Robinson 8dfb698508f44995bc4db200b62d7431_***_Keith Naylor 3e76fcc2bb3cde4ddee2c8edfd2f0082_***_Dominic Reeve |
| author |
Iain Fairley Nicholas King Ian Masters David Glasby Max Robinson Keith Naylor Dominic Reeve |
| author2 |
Iain Fairley Benjamin J. Williamson Jason McIlvenny Nicholas King Ian Masters Matthew Lewis Simon Neill David Glasby Daniel Coles Ben Powell Keith Naylor Max Robinson Dominic E. Reeve Keith Naylor Dominic Reeve |
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Journal article |
| container_title |
Renewable Energy |
| container_volume |
196 |
| container_start_page |
839 |
| publishDate |
2022 |
| institution |
Swansea University |
| issn |
0960-1481 |
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10.1016/j.renene.2022.07.030 |
| publisher |
Elsevier BV |
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Faculty of Science and Engineering |
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Faculty of Science and Engineering |
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| description |
Resource quantification is vital in developing a tidal stream energy site but challenging in high energy areas. Drone-based large-scale particle image velocimetry (LSPIV) may provide a novel, low cost, low risk approach that improves spatial coverage compared to ADCP methods. For the first time, this study quantifies performance of the technique for tidal stream resource assessment, using three sites. Videos of the sea surface were captured while concurrent validation data were obtained (ADCP and surface drifters). Currents were estimated from the videos using LSPIV software. Variation in accuracy was attributed to wind, site geometry and current velocity. Root mean square errors (RMSEs) against drifters were 0.44 m s−1 for high winds (31 kmh) compared to 0.22 m s−1 for low winds (10 kmh). Better correlation was found for the more constrained site (r2 increased by 4%); differences between flood and ebb indicate the importance of upstream bathymetry in generating trackable surface features. Accuracy is better for higher velocities. A power law current profile approximation enables translation of surface current to currents at depth with satisfactory performance (RMSE = 0.32 m s−1 under low winds). Overall, drone video derived surface velocities are suitably accurate for “first-order” tidal resource assessments under favourable environmental conditions. |
| published_date |
2022-08-01T04:18:35Z |
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1763754235845935104 |
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11.013148 |