IR thermographic flow visualization for the quantification of boundary layer flow disturbances due to the leading edge condition
Veröffentlichungsdatum
2019-08
Zusammenfassung
The aerodynamic performance of wind turbine rotor blades is influenced by the leading edge condition. Contamination and erosion cause increased surface roughness, unevenness or defects, which affect the boundary layer flow and, thus, reduce lift and increase drag. Current approaches used to determine the disturbed boundary layer flow are based on invasive flow probes with limited spatial resolution; therefore, a non-invasive, camera-based measurement of the boundary layer flow disturbances on wind turbines in operation is proposed using thermographic flow visualization. The actual and the undisturbed laminar-turbulent transition positions are determined in the thermographic images and a subsequent assignment to the rotor blade geometry obtains chord-based information. The normalized difference of both transition positions can be used as a metric to describe the extent of the disturbed boundary layer flow. The approach is demonstrated on a multi-MW horizontal axis wind turbine with a laminar flow reduction of up to 90.4 %. Furthermore, the measurement results allow the estimation of the annual energy production loss due to the leading edge condition, which enhances the industrial standard of simply comparing clean and tripped aerodynamic polars. For the investigated wind turbine, the annual energy production loss amounts to 4.7 % at 6 m/s average wind speed.
Schlagwörter
IR thermographic flow visualization
;
Boundary layer measurement
;
Wind turbine rotor blades
;
Leading edge contamination
;
Leading edge erosion
;
Annual energy production
Verlag
Elsevier Science
Institution
Dokumenttyp
Artikel/Aufsatz
Zeitschrift/Sammelwerk
Band
138
Startseite
709
Endseite
721
Zweitveröffentlichung
Ja
Dokumentversion
Postprint
Sprache
Englisch
Dateien![Vorschaubild]()
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Name
Dollinger et al_IR thermographic flow visualization for the quantification of boundary layer_2019_accepted-version.pdf
Size
2.92 MB
Format
Adobe PDF
Checksum
(MD5):42824c51b0633fd342bb1a8a38f1fb53