Thermographic Transition Detection for Low-Speed Wind-Tunnel Experiments

Grawunder, M.; Reß, R.; Breitsamter, C.

doi:10.2514/1.j054490

Cited by 13 publications

(7 citation statements)

References 5 publications

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“…Grawunder et al (2016) used this method to automatically detect transition on a preheated model in a wind tunnel. In complex geometries such as turbine rigs, preheating the object of interest is not feasible and might lead to unwanted changes in aerodynamic behavior.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, to surface hot film measurements, the proposed data analysis approach was compared to a second post-processing method. As mentioned in the introduction, Gartenberg and Wright (1994) and later Grawunder et al (2016) developed a method based on differential infrared thermography. To detect boundary layer transition, they subtracted the raw sensor intensities of two thermograms ΔI = I 1 − I 2 of subsequent…”

Section: Comparison Of Transition Measurement Techniquesmentioning

confidence: 99%

“…They proved the application of this technique on freestanding moving rotor blades and demonstrated that the movement of an unsteady transition could be detected. Grawunder et al (2016) preheated a helicopter model in a wind tunnel to measure the onset and end of transition by analyzing the difference between two normalized thermograms. This analysis approach was proposed by Gartenberg and Wright (1994).…”

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confidence: 99%

“…This analysis approach was proposed by Gartenberg and Wright (1994). In contrast to Raffel et al (2015) and Grawunder et al (2016), which use a constant heat source, a pulsed light source was used in this work to make this technique usable for the analysis of fast rotating devices. Furthermore, the present paper refines the idea of the patent document by proposing a beneficial combination of measurement method and physically based post-processing to detect laminar-turbulent transition.…”

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confidence: 99%

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Temperature decline thermography for laminar–turbulent transition detection in aerodynamics

et al. 2017

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a post-processing method was developed and qualified to determine a quantity proportional to the heat transfer coefficient into the flow. By plotting this quantity for each pixel of the surface, a qualitative, two-dimensional heat transfer map was obtained. The results clearly depicted the areas of onset and end of transition over the full span of the model and agreed with the expected behavior based on the respective flow condition. To validate the approach, surface hotfilm measurements were conducted simultaneously on the same NACA profile. Both techniques showed excellent agreement. The temperature decline method allows to visualize laminarturbulent transitions on static or moving parts and can be applied on a very broad range of scales-from tiny airfoils up to large airplane wings.

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Section: Discussionmentioning

confidence: 99%

Section: Comparison Of Transition Measurement Techniquesmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Temperature decline thermography for laminar–turbulent transition detection in aerodynamics

et al. 2017

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“…By following a similar principle, Raffel and Merz (2014) have proposed a technique that provides high-contrast images using differential IR thermography between two successive time steps, which are suitable for automated processing. Later, Grawunder et al (2016) have extended differential IR thermography approach for low-enthalpy flows where the temperature difference between laminar and turbulent regions are low, while Simon et al (2016) and Wolf et al (2019) have made such an extension for unsteady flow measurements. The major drawback in these attempts is the need for multiple IR thermography measurements to detect the flow region boundaries.…”

Section: Automatic Localisation Of Laminar-turbulent Flowmentioning

confidence: 99%

Automatic separation of laminar-turbulent flows on aircraft wings and stabilisers via adaptive attention butterfly network

2022

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Many of the laminar-turbulent flow localisation techniques are strongly dependent upon expert control even-though determining the flow distribution is the prerequisite for analysing the efficiency of wing & stabiliser design in aeronautics. Some recent efforts have dealt with the automatic localisation of laminar-turbulent flow but they are still in infancy and not robust enough in noisy environments. This study investigates whether it is possible to separate flow regions with current deep learning techniques. For this aim, a flow segmentation architecture composed of two consecutive encoder-decoder is proposed, which is called Adaptive Attention Butterfly Network. Contrary to the existing automatic flow localisation techniques in the literature which mostly rely on homogeneous and clean data, the competency of our proposed approach in automatic flow segmentation is examined on the mixture of diverse thermographic observation sets exposed to different levels of noise. Finally, in order to improve the robustness of the proposed architecture, a self-supervised learning strategy is adopted by exploiting 23.468 non-labelled laminar-turbulent flow observations.

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