{"id":{"repo_id":"carleton","oai_identifier":"oai:carleton.scholaris.ca:20.500.14718/39000"},"canonical_url":"https://search.dev.ndltd.org/etd/carleton/oai:carleton.scholaris.ca:20.500.14718/39000","repository":{"repo_id":"carleton","name":"Carleton University","base_url":"https://carleton.scholaris.ca/server/oai/request"},"display":{"title":"Predicting Interference Patterns Between Offshore Wind Farms through Wake Analysis","abstract":"This research is to characterize the wake effect from a cluster of large-scale offshore wind turbines with the purpose of optimizing spacing between separate farms to avoid wake interference. Placing wind turbines within a field of disturbed flow decreases power output, and in some cases, threatens structural integrity. Unsteady loading and fluxes in wind turbine power generation are born out of turbulence and can further lead to a reduction in the overall energy production of a wind farm. As the offshore wind industry exhibits rapid growth, an immediate need for standards that enable optimization of farm placement for offshore development exists. Wake characterization was accomplished in this research through computational fluid dynamics (CFD) modelling.","abstract_html":"This research is to characterize the wake effect from a cluster of large-scale offshore wind turbines with the purpose of optimizing spacing between separate farms to avoid wake interference. Placing wind turbines within a field of disturbed flow decreases power output, and in some cases, threatens structural integrity. Unsteady loading and fluxes in wind turbine power generation are born out of turbulence and can further lead to a reduction in the overall energy production of a wind farm. As the offshore wind industry exhibits rapid growth, an immediate need for standards that enable optimization of farm placement for offshore development exists. Wake characterization was accomplished in this research through computational fluid dynamics (CFD) modelling.","abstract_has_math":false,"creators":["Matis, Melissa"],"institution":"Carleton University","degree_name":"Master of Applied Science (M.App.Sc.)","degree_level":"Master's","degree_discipline":"Engineering, Sustainable Energy","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017","date_published":"2017","updated_at":"2026-07-24T01:34:41Z","subjects":[],"languages":["en"],"rights":["Copyright © 2017 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. 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Unsteady loading and fluxes in wind turbine power generation are born out of turbulence and can further lead to a reduction in the overall energy production of a wind farm. As the offshore wind industry exhibits rapid growth, an immediate need for standards that enable optimization of farm placement for offshore development exists. Wake characterization was accomplished in this research through computational fluid dynamics (CFD) modelling."]},{"key":"dc:title","label":"Title","values":["Predicting Interference Patterns Between Offshore Wind Farms through Wake Analysis"]}]}],"canonical_facts":{"dc:creator":["Matis, Melissa"],"dc:date.accessioned":["2025-04-08T19:33:53Z"],"dc:date.available":["2025-04-08T19:33:53Z"],"dc:date.issued":["2017"],"dc:description.abstract":["This research is to characterize the wake effect from a cluster of large-scale offshore wind turbines with the purpose of optimizing spacing between separate farms to avoid wake interference. 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