Thermographic detection of turbulent flow separation on rotor blades of wind turbines in operation

Oehme, Felix; Gleichauf, Daniel; Suhr, Janick; Balaresque, Nicholas; Sorg, Michael; Fischer, Andreas

doi:10.1016/j.jweia.2022.105025

Cited by 10 publications

(2 citation statements)

References 34 publications

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“…Using IRT on wind turbines, the CNR of the thermographic images is limited due to the field conditions (for instance low solar radiation) or a small sensitivity of the measurement effect (as seen in the detection of static [14] or dynamic stall [15], or when using smallsignal evaluation approaches such as differential images tested on an airfoil [16] and rotor blades of wind turbines [17]). Gleichauf et al improved the contrast with averaging methods, i.e., non negative Matrix factorization [18] and principle component analysis [19].…”

Section: State Of the Artmentioning

confidence: 99%

Bayesian-Based Approach for the Thermographic Measurement of Flow Transition on Wind Turbine Rotor Blades

Dieckmann,

Dorszewski,

Balaresque

et al. 2024

Applied Sciences

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The position of the laminar–turbulent flow transition affects the aerodynamic efficiency of wind turbine rotor blades. An established diagnostic tool is infrared thermography, which enables flow visualization on in-service wind turbines, including the detection of the flow transition position. For the first time, the capabilities of a Bayesian-based image evaluation on the basis of previous knowledge are investigated for maximizing the measurement quality in particular for those weather conditions with a low contrast-to-noise ratio. The Bayesian framework is assessed using simulated and measured thermographic images, incorporating a probability distribution of the transition position. Results indicate that utilizing previous knowledge, especially when normally distributed around the true transition position with a standard deviation of 3 px, significantly reduces uncertainty for thermographic images with a contrast-to-noise ratio <7. Additionally, the Bayesian framework enhances the visualization of transition progression along the radial blade axis, yielding a less noisy result. Previous experimental data can be used to reduce uncertainty for erroneous transition position detections. In conclusion, the integration of high-quality previous knowledge through Bayesian inference proves to be effective in lowering the uncertainty of the position measurement of the laminar–turbulent transition on wind turbine rotor blades, with no compromise of the spatiotemporal resolution.

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Section: State Of the Artmentioning

confidence: 99%

Bayesian-Based Approach for the Thermographic Measurement of Flow Transition on Wind Turbine Rotor Blades

Dieckmann,

Dorszewski,

Balaresque

et al. 2024

Applied Sciences

Self Cite

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show abstract

“…For the first time, we recently achieved an IRT-based detection of static stall at the rotor blade root of an operating wind turbine by evaluating the thermodynamic response to the unsteady inflow conditions at wind turbines (Oehme et al, 2022). However, since the proposed signal processing approaches for the measurement conditions on wind turbines are based on a time-averaged evaluation of surface temperature fluctuations, they are not suitable for a time-resolved detection of flow separation.…”

Section: State Of the Artmentioning

confidence: 99%

Thermographic detection and localisation of unsteady flow separation on rotor blades of wind turbines

et al. 2022

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A thermographic detection and localization of unsteady flow separation on an operating wind turbine of type GE1.5sl is presented and verified by means of tufts flow visualisation. Unsteady flow separation phenomena such as dynamic stall are an undesired flow state as it causes fatigue failures, limits the turbine efficiency and increases noise emissions from the rotor blades. In comparison to available methods for stall detection on wind turbines, the presented infrared thermographic measurement approach is non-invasive, in-process capable and provides a high spatial resolution. On the basis of the thermodynamic response behaviour of the surface temperature in case of unsteady flow events, a two-step signal processing approach is proposed, to achieve the highest possible spatio-temporal resolution in the detection and localisation of stall. First, the identification of distinct maxima of the spatial standard deviation of difference images, enables to determine potential stall events in time. In the subsequent combined image evaluation with a transient approach and a principal component analysis, unsteady flow separation is detected during the occurrence of a strong wind gust with the maximum time resolution (image exposure time) as well as the maximum spatial resolution (image resolution), respectively, despite the limited signal-to-noise ratio compared to wind tunnel experiments. In addition, a geometric assignment of the image data to the rotor blade geometry is conducted, which enables a localization of the separation point on the 3 days rotor blade geometry with a maximal uncertainty of 2.3% of the chord length.

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AQUADA PLUS: Automated damage inspection of cyclic-loaded large-scale composite structures using thermal imagery and computer vision

Xiao

Sheiati

Shihavuddin

2023

Composite Structures

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Thermographic detection of turbulent flow separation on rotor blades of wind turbines in operation

Cited by 10 publications

References 34 publications

Bayesian-Based Approach for the Thermographic Measurement of Flow Transition on Wind Turbine Rotor Blades

Bayesian-Based Approach for the Thermographic Measurement of Flow Transition on Wind Turbine Rotor Blades

Thermographic detection and localisation of unsteady flow separation on rotor blades of wind turbines

AQUADA PLUS: Automated damage inspection of cyclic-loaded large-scale composite structures using thermal imagery and computer vision

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