Validation of a wake model for vertical-axis wind turbine farms

dc.contributor.authorVallbo, Gustav
dc.contributor.departmentChalmers tekniska högskola / Institutionen för mekanik och maritima vetenskapersv
dc.contributor.departmentChalmers University of Technology / Department of Mechanics and Maritime Sciencesen
dc.contributor.examinerNilsson, Håkan
dc.contributor.supervisorCorniglion, Rémi
dc.date.accessioned2023-11-14T08:26:19Z
dc.date.available2023-11-14T08:26:19Z
dc.date.issued2023
dc.date.submitted2023
dc.description.abstractAn analytical wake model can be used to predict the power generated by a vertical-axis wind turbine farm. The accuracy of a specific wake model is investigated in this study. In particular, the velocity field within the farm is analyzed since it determines the power that can be generated by each turbine. On a two-turbine layout, the velocity field obtained with the wake model is compared to that given by a computational fluid dynamics (CFD) simulation. The two-dimensional CFD simulation is performed with an actuator line model of the turbine blades and Reynolds-averaged Navier-Stokes turbulence modeling. One of the major findings is that the wake model results are highly dependent on the choice of combination model used to superpose wakes of multiple turbines. The results also show that one of the combination models has a better agreement with the CFD simulation. However, this comparison is limited by the accuracy of the turbulence modeling within the CFD simulation. Despite this limitation, this study is a step towards accurately modeling a vertical-axis wind farm.
dc.identifier.coursecodeMMSX30
dc.identifier.urihttp://hdl.handle.net/20.500.12380/307356
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectvertical-axis wind turbine (VAWT)
dc.subjectwake model
dc.subjectwind farm
dc.titleValidation of a wake model for vertical-axis wind turbine farms
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeEngineering mathematics and computational science (MPENM), MSc

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