Extracting quantitative data from tuft flow visualizations on utility scale wind turbines

Vey, Stefan; Lang, Henning; Nayeri, Christian Navid; Paschereit, Christian Oliver; Pechlivanoglou, George

doi:10.1088/1742-6596/524/1/012011

Cited by 23 publications

(17 citation statements)

References 5 publications

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“…These patterns are referred to as stall cells, and create complicated vortex patterns on and behind the airfoil. Even more complicated still are the separation patterns on wind turbine blades due to the changes of airfoil shape, twist and chord length (various surface visualizations can be found in Manolesos (2014); Lennie et al (2018); Vey et al (2014)). Wind turbines uniquely experience very high angles of attack; where the spatial patterns complicate further (Skrzypi et al, 2014;Skrzypinski, 2012;Gaunaa et al, 2016;Lennie et al, 2018).…”

Section: So How Are These Variations Treated?mentioning

confidence: 99%

Cartographing dynamic stall with machine learning

Lennie

Steenbuck

Noack

et al. 2019

Preprint

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Abstract. Airfoil stall is bad for wind turbines. Once stall has set in, lift collapses, drag increases and then both of these forces will fluctuate strongly. The result is higher fatigue loads and lower energy yield. In dynamic stall, separation first develops from the trailing edge up the leading edge, eventually the shear layer rolls up and then a coherent vortex forms and then sheds downstream with it’s low pressure core causing a lift spike and moment dump. When 50+ experimental cycles of lift or pressure values are averaged, this process appears clear and coherent in flow visualizations. Unfortunately, stall is not one clean process, but a broad collection of processes. This means that the analysis of separated flows should be able to detect outliers and analysis cycle to cycle variations. Modern data science/machine learning can be used to treat separated flows. In this study, a clustering method based on dynamic time warping is used to find different shedding behaviors. This method captures that secondary and tertiary vorticity vary strongly and in static stall with surging flow; the flow can occasionally reattach. A convolutional neural network was used to extract dynamic stall vorticity convection speeds and phases from pressure data. Finally, bootstrapping was used to provide best practices regarding the number of experimental repetitions required to ensure experimental convergence.

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Section: So How Are These Variations Treated?mentioning

confidence: 99%

Cartographing dynamic stall with machine learning

Lennie

Steenbuck

Noack

et al. 2019

Preprint

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“…This phenomenon is called a separation bubble. Bubbles are a sensitive phenomenon and small changes to boundary conditions can make them disappear completely (Ward, 1963). Inflow turbulence, leading-edge surface erosion, fouling or ice will often cause forced transition (Pires et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Inflow turbulence, leading-edge surface erosion, fouling or ice will often cause forced transition (Pires et al, 2018). Earlier transition will tend to reduce or remove bubbles (Ward, 1963). Even without outside influences, bubbles are an unstable phenomena due to shear-layer disturbances which lead to transition and eventual reattachment or bursting (Kirk and Yarusevych, 2017).…”

Section: Introductionmentioning

confidence: 99%

Cartographing dynamic stall with machine learning

et al. 2020

View full text Add to dashboard Cite

Abstract. Once stall has set in, lift collapses, drag increases and then both of these forces will fluctuate strongly. The result is higher fatigue loads and lower energy yield. In dynamic stall, separation first develops from the trailing edge up the leading edge. Eventually the shear layer rolls up, and then a coherent vortex forms and then sheds downstream with its low-pressure core causing a lift overshoot and moment drop. When 50+ experimental cycles of lift or pressure values are averaged, this process appears clear and coherent in flow visualizations. Unfortunately, stall is not one clean process but a broad collection of processes. This means that the analysis of separated flows should be able to detect outliers and analyze cycle-to-cycle variations. Modern data science and machine learning can be used to treat separated flows. In this study, a clustering method based on dynamic time warping is used to find different shedding behaviors. This method captures the fact that secondary and tertiary vorticity vary strongly, and in static stall with surging flow the flow can occasionally reattach. A convolutional neural network was used to extract dynamic stall vorticity convection speeds and phases from pressure data. Finally, bootstrapping was used to provide best practices regarding the number of experimental repetitions required to ensure experimental convergence.

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“…Hierfür wird eine berührungslose und nichtinvasive Messtechnik benötigt, um die Strömungszustände an den Rotorblättern untersuchen zu können. Die konventionellen Messverfahren zur flächenhaften Strömungsvisua-lisierung an laufenden Windenergieanlagen beruhen auf der Anbringung von Wollfäden [17] oder der Präparation mit einem Öl-Partikel-Gemisch [12]. Zur Erkennung von abgelösten Strömungen werden darüber hinaus sogenannte Stall Flags eingesetzt [1].…”

Section: Introductionunclassified

Hochauflösende thermografische Strömungsvisualisierung bei Windenergieanlagen im Betrieb

Dollinger

Sorg

Fischer

et al. 2017

Tm - Technisches Messen

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The application of thermographic flow visualization on wind turbines in operation differs from the well-established application in wind tunnel experiments. The large distance of up to 400 m between the rotor blade and the thermographic camera, mainly due to the height of the wind turbine, results in a poor spatial resolution and a small numerical aperture. Furthermore, the required temperature difference between the rotor blade and the flow only relies on the absorbed solar radiation, which often leads to a low signal-to-noise ratio. The fundamental effects on the measurement uncertainty for the localisation of a laminar-turbulent transition are presented and validated for a measurement on a 1.5 MW wind turbine. As a result, the standard uncertainty of the flow transition position amounts to ±0.21 pixel, which corresponds to ±0.3 % chord length. The measurement uncertainty is currently not limited by the measurement system, but by flow induced temperature fluctuations.

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Extracting quantitative data from tuft flow visualizations on utility scale wind turbines

Cited by 23 publications

References 5 publications

Cartographing dynamic stall with machine learning

Cartographing dynamic stall with machine learning

Cartographing dynamic stall with machine learning

Hochauflösende thermografische Strömungsvisualisierung bei Windenergieanlagen im Betrieb

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