Laminar-Turbulent Transition Localization in Thermographic Flow Visualization by Means of Principal Component Analysis

• Published in: Applied sciences 2021-06, Vol.11 (12), p.5471
• Main Authors: Gleichauf, Daniel, Oehme, Felix, Sorg, Michael, Fischer, Andreas
• Format: Article
• Language: English
• Subjects: Aerodynamics; Approximation; Boundary layer flow; Boundary layers; Datasets; Error analysis; Error reduction; Evaluation; Experiments; Flow visualization; Fluid dynamics; Fourier transforms; Image contrast; Image enhancement; Image processing; Laminar flow; Local flow; Localization; measurement error; Methods; Noise; Noise reduction; principal component analysis; Principal components analysis; Random errors; Rotor blades; Rotor blades (turbomachinery); Signal processing; Signal to noise ratio; Solar energy; Steady flow; Systematic errors; Temperature gradients; thermographic flow visualization; Thermography; Turbines; Turbulent flow; Visualization; Wind measurement; Wind power; Wind tunnels
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11125471

Thermographic flow visualization is a contactless, non-invasive technique to visualize the boundary layer flow on wind turbine rotor blades, to assess the aerodynamic condition and consequently the efficiency of the entire wind turbine. In applications on wind turbines in operation, the distinguishability between the laminar and turbulent flow regime cannot be easily increased artificially and solely depends on the energy input from the sun. State-of-the-art image processing methods are able to increase the contrast slightly but are not able to reduce systematic gradients in the image or need excessive a priori knowledge. In order to cope with a low-contrast measurement condition and to increase the distinguishability between the flow regimes, an enhanced image processing by means of the feature extraction method, principal component analysis, is introduced. The image processing is applied to an image series of thermographic flow visualizations of a steady flow situation in a wind tunnel experiment on a cylinder and DU96W180 airfoil measurement object without artificially increasing the thermal contrast between the flow regimes. The resulting feature images, based on the temporal temperature fluctuations in the images, are evaluated with regard to the global distinguishability between the laminar and turbulent flow regime as well as the achievable measurement error of an automatic localization of the local flow transition between the flow regimes. By applying the principal component analysis, systematic temperature gradients within the flow regimes as well as image artefacts such as reflections are reduced, leading to an increased contrast-to-noise ratio by a factor of 7.5. Additionally, the gradient between the laminar and turbulent flow regime is increased, leading to a minimal measurement error of the laminar-turbulent transition localization. The systematic error was reduced by 4% and the random error by 5.3% of the chord length. As a result, the principal component analysis is proven to be a valuable complementary tool to the classical image processing method in flow visualizations. After noise-reducing methods such as the temporal averaging and subsequent assessment of the spatial expansion of the boundary layer flow surface, the PCA is able to increase the laminar-turbulent flow regime distinguishability and reduce the systematic and random error of the flow transition localization in applications where no artificial increase in the contrast is poss