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

Abstract

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 possible. The enhancement of contrast increases the independence from the amount of solar energy input required for a flow evaluation, and the reduced errors of the flow transition localization enables a more precise assessment of the aerodynamic condition of the rotor blade.

Infrared thermography techniques for boundary layer state visualisation

“…Dollinger et al [15] demonstrated how temporal standard deviation or Fourier coefficient amplitude from 30 images can enhance the SNR for studying wind turbine blades. For the same application Gleichauf et al [16] applied principle component analysis on up to 10 000 images, showing improved robustness compared to using an averaging or standard deviation.…”

Contrast enhancement in thermographic flow visualization on wind turbines in operation

“…Hence, PCA allows to separate the desired flow information from the non-desired image information or interference. In measurements with low contrast, the method was able to significantly increase the distinguishability and reduce the measurement error of the flow transition localization [7].…”

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

“…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]. Nevertheless, without including previous knowledge, the available CNR directly limits the achievable measurement uncertainty for the determination of the laminar-turbulent transition position on the rotor blade of a wind turbine.…”