differential infrared thermography for transition...

17th International Symposium on Applications of Laser Techniques to Fluid Mechanics Lisbon, Portugal, 07-10 July, 2014

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Differential Infrared Thermography for Transition Detection

on Rotor Blades

Till Schwermer*, Christoph B. Merz, Kai Richter, Dominik Frieling, Markus Raffel

Institute of Aerodynamics and Flow Technology, German Aerospace Center, Göttingen, Germany

* Correspondent author: [email protected] Abstract Differential Infrared Thermography (DIT) was applied and investigated for the detection of unsteady boundary layer transition locations on a pitching rotor blade airfoil and on a rotating blade under cyclic pitch. The paper demonstrates the unsteady transition detection with a high framing rate infrared camera and the differential infrared thermography method. DIT is based on image intensity differences between two subsequently recorded images. A pitching NACA0012 airfoil served as the first test object. The recorded images were used in order to investigate and to further improve evaluation strategies for periodically moving boundary layer transition lines. The gained knowledge has then been used for the optical measurement of unsteady transition locations on helicopter rotor blade models under cyclic pitch and rotation for the first time. Image de-rotation for tracking the blade was employed using a rotating mirror to increase exposure time without causing motion blur. The paper describes the challenges that occurred during the recording and evaluation of the data in detail. The results obtained were discussed and found to be encouraging to further improve the method towards the measurement of unsteady boundary layer transition lines on helicopter rotor models in forward flight condition without the need of blade instrumentation. Notation A, B measurement images c chord, m cp specific heat, J/K f frequency, Hz facq camera acquisition frequency, Hz fm rotational mirror frequency, Hz fr rotor frequency, Hz h heat transfer coefficient, W/(m²K) k reduced frequency (k=πfc/U∞) ktip blade tip reduced frequency (ktip=πfrc/Utip) Mtip blade tip Mach number N N-factor used for the eN-Method r radial coordinate, m R radius, m Rtip blade tip radius, m Rex local Reynolds number Retip blade tip Reynolds number St Stanton number (St=h/( U∞ρcp)) t time, s texp camera exposure time, s T temperature, °C T period, s U∞ freestream velocity, m/s Utip blade tip speed, m/s x chord-wise coordinate, m xtr transition location, m


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α angle of attack, deg αA, αB instantaneous angles of attack, deg αCFD angle of attack in the CFD computation, deg αExp angle of attack in the experiment, deg αmax maximum angle of attack, deg αmean mean angle of attack, deg αmin minimum angle of attack, deg ρ density, kg/m³ ΔT temperature difference, K Δt time delay, s 1. Introduction Boundary layer transition plays an important role in the aerodynamics of helicopter main rotors. Recent design strategies try to increase the amount of laminar flow on the rotor blades to achieve low power consumption, and the transition position has become an important design parameter. Once the design is concluded, the real transition position on the blade is of great interest during its operation. The knowledge of the transition position is needed for the performance assessment, for the validation of the design, and for the calibration of numerical transition prediction tools. Several measurement techniques have been developed in the past to investigate the laminar-turbulent boundary layer transition. Initially developed for fixed-wing applications in a non-rotating environment, most techniques have been adapted for the measurement on rotating blades, but the applicability of these techniques varies with the experimental environment. For rotating blades in steady flow conditions, the sublimation technique was used on full-scale helicopter rotors in hover (Tanner and Yaggy, 1966; Rohardt, 1986), oil flow interferometry was applied in wind tunnel experiments to full-scale tilt rotor blades (Wadcock et al, 1999) and to high-speed model propeller blades (Schülein et al, 2012). Temperature sensitive paint was also applied on high-speed model propeller blades (Yorita et al, 2012). Most recently, transition detection by high-speed infrared thermography was successfully demonstrated on model and full-scale helicopter rotor blades (Richter and Schülein, 2014). For unsteady flow conditions, the transition detection is aerodynamically and technically more complex. The detection of the unsteady transition locations was demonstrated by the use of hot-film anemometry on pitching airfoils (Lorber and Carta, 1992; Chandrasekhara and Wilder, 2003; Richter et al, 2014) and on a model scale helicopter rotor in hover and forward flight conditions (Raffel et al, 2011). Hot-film anemometry allows transition detection with high temporal resolution, however the measurement is limited to single sensors that have to be applied to the model surface, leading to a low spatial resolution. The measurement setup is complex and sensors, anemometers and a data acquisition system are needed. The application of an optical method for the detection of unsteady transition would have many advantages, as for example the high spatial resolution and a relative ease of use. The current study uses differential infrared thermography (DIT) (Raffel and Merz, 2014), which is a new optical measurement strategy based on infrared thermography, for the unsteady transition detection on a pitching rotor blade airfoil and, for the first time, on a rotating blade. This measurement technique is based on the difference between two thermal measurement images, and neither a special treatment of the rotor blade surface nor an application of sensors is needed. Following the first detailed description of DIT applied to a pitching airfoil (Merz et al, 2014), this study investigates different DIT analysis methodologies for the pitching rotor blade airfoil. The first DIT application on a rotating blade with cyclic pitching motion is presented and the results are discussed. 2. Differential infrared thermography Transition detection by infrared thermography relies on the measurement of the differences in the surface temperatures existing between regions of laminar and turbulent flow. These are either caused by the different heat transfer coefficients between the flow and the surface of laminar and turbulent boundary layers or by


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friction. In steady flow conditions different temperatures can develop on the surfaces which are large enough to be measured by an infrared camera. This can even be performed on rotor blades of a helicopter flying in hover as demonstrated by Richter and Schülein (2014). For helicopters in forward flight, the rotor blades perform an additional sinusoidal pitching motion over one complete revolution of the rotor. Due to this cyclic variation of the rotor blade angle of attack and the variation of the relative flow velocity and direction, the laminar-turbulent boundary layer transition is permanently moving upstream and downstream in the chord-wise direction of the blade. This permanent movement does not allow developing steady temperature differences between laminar and turbulent flow regions which are large enough to be measured by conventional infrared thermography. The DIT technique has been developed to obtain information about a moving transition location with the use of infrared imaging, where it is difficult or even impossible to extract the instantaneous transition location from an individual image. The technique is based on the subtraction of two consecutively recorded thermal images at different angles of attack and with different locations of the boundary layer transition. Figure 2.1 depicts an exemplary angle of attack variation versus time and Fig. 2.2 shows the qualitative development of the Stanton number St=h/(U∞ρcp) along an arbitrary airfoil surface for a laminar and a turbulent boundary layer state over the chord-wise Reynolds number Rex. The Stanton number is proportional to the heat transfer coefficient h between the airfoil surface and the air, and different levels of the Stanton number exist for laminar and turbulent flow. Compared to the laminar boundary layer state, the turbulent boundary layer has a higher heat transfer coefficient and thus a higher Stanton number as shown in Fig. 2.2. Additionally, two Stanton number distributions are depicted for boundary layer transition at two different angles of attack αA and αB. The onset (onset αA) and end (end αA) of transition for the lower angle of attack αA and the onset (onset αB) and end (end αB) of transition for the higher angle of attack αB are indicated. The area between onset and end of transition represents the transitional region. For the smaller angle of attack αA, transition occurs at higher Reynolds numbers Rex. If the temperatures of the airflow and the airfoil surface differ, the difference in the heat exchanged between the surface and the flow leads to different surface temperatures in the regions of laminar and turbulent boundary layers. This effect has been used in the experiments presented here, where the model surfaces were heated during the experiments and were therefore warmer than the air flow. Figure 2.3 shows the idealized temperature distribution T along the chord of the airfoil x/c for the two angles of attack. For a heated airfoil which has a higher temperature than the surrounding air, a higher Stanton number leads to a lower surface temperature. As the Stanton number increases with the change from the laminar to the turbulent boundary layer state, the observed surface temperature drops to a lower level when transition occurs. The dash-dotted line indicates the resulting difference after subtracting the two temperature distributions ΔT=TαA-TαB. Depending on the transition location in both images the differential temperature curve will have a positive or negative peak value. While the absolute temperature distributions would allow the detection of the onset and the end of the boundary layer transition for both angles of attack for steady flow conditions, the plotted temperature difference allows the detection of the onset of transition for B and the end of transition for A also in unsteady flow cases. To get a sufficient signal-to-noise ratio after subtraction, the transition positions in the measurement image A and the reference image B must differ. The differential temperature distribution can consist of two peaks (not shown in Fig. 2.3), if the angle of attack difference between the subtracted images is too large and the transitional regions do not overlap. Evaluating the difference of the temperature distribution at αA with another temperature distribution at a lower angle of attack, it is possible to detect the onset of transition for A as well. Similarly, for the end of transition at αB, a temperature distribution at a higher angle of attack is needed. Therefore, the onset and end of transition can in principle be evaluated for all angles of attack except for the two inflection points at αmin and αmax.


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Fig. 2.1 Angle of attack versus time including the angles of attack of the two images A, B (αA, αB) and the reference images at αmin and αmax.

Fig. 2.2 Stanton number versus local Reynolds number with the two onset and end locations of the transition for the images A and B.

Fig. 2.3 Sketch of the measured temperature and resulting temperature difference versus chord-wise position on the rotor blade with the onset and end locations of the transition.

Different strategies for the selection of the two measurement images can be chosen. The basic method is to subtract successive images as demonstrated by Merz et al. (2014) on a pitching airfoil. This approach is useful, if the change in angle of attack between two successive images is sufficient for a notable change in the transition location. Otherwise, this procedure can be adapted by using an offset greater than one picture between the two subtracted images. A disadvantage of this principle is that the method fails whenever the transition location is approximately constant in both images, which is the case near the inflection points of the angle of attack oscillations. In such case, the signal-to-noise ratio drops significantly and the detection is impossible. One possibility to overcome this problem is to use two fixed reference images instead of a moving reference image. These can be chosen, for instance, at the minimum angle of attack and at the maximum angle of attack, depicted with αmin and αmax in Fig. 2.1. Taking the differences between the measurement images around the maximum angle of attack, where the detection fails with the moving reference image, with the reference image at minimum α results in a sufficient signal-to-noise ratio. However, when using the reference image αmin, only the onset of transition can be extracted for the individual images and when using αmax, only the end of transition can be detected. A detection of the end of transition when using the reference image αmin and the onset of transition when using αmax is not possible because no reference images are available that contain an end of transition location further downstream or an onset of transition location further upstream, respectively. In contrast to the moving reference evaluation method only half of the transition location information can be gathered when using a fixed reference image. The detected transition location on one side of the peak remains constant for all measurement images


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because of the constant transition location in the fixed reference image. Nevertheless, both approaches complement each other and results from both methods are presented in this study. Figures 2.4a and 2.4b show instantaneous infrared images of the rotor blade upper surface at two different angles of attack. The blade is rotating clockwise around the rotor hub located outside the pictures on the right. Flow is therefore from top to bottom. Black dots visible in the infrared images are used as reference markers for the mapping of the raw images. The grayscale images show brighter areas which have higher temperatures than the darker areas. A transition dot was applied to the leading edge and the turbulent wedge is clearly detectable as a dark area at the middle in the instantaneous images. This proves the presence of a laminar flow region on the rotor blade. However, it is difficult to distinguish between the effects of increased heat transfer due to the change in the boundary layer state and due to the mechanical structure of the rotor blade underneath the surface (e.g. spars). Except for the turbulent wedge the unsteady transition positions cannot be detected directly in the instantaneous measurement images shown in Figures 2.4a and 2.4b. Therefore, differential infrared images are used for the evaluation and visible infrared patterns that do not change in the instance of time between the two images cancel out during subtraction. Figure 2.4c shows the differential infrared image as result of the subtraction of image B from image A. Areas with signal intensity higher or lower than the mean intensity belong to flow structures that changed during the time span between the two images. The turbulent wedge is still visible because its wake differs at different rotor blade angles of attack. Additionally, the difference in the transition location of the two images becomes evaluable. The dark shaded area indicates the region were the transition has moved between the two images. The differential image also reveals the change of the transition location along the rotor blade span, which is a major advantage of optical methods for transition measurements on rotating blades. It can be seen that the transition location moves significantly downstream when moving inboard on the rotor blade. Figure 2.5 shows the extracted intensity distribution along the blue line shown in the differential picture in Fig. 2.4c, with the rotor blade leading edge on the left and the trailing edge on the right side. Black dots indicate the raw data of the extracted differential intensities. The data shows values around zero for flow regions that are unchanged in both images. A negative peak is visible where the transition position changes between the two infrared images shown in Figures 2.4a and 2.4b. The sign of the peak depends on the transition locations in the measurement image and in the reference image (see Fig. 2.1 and 2.3). The variation of the transition location along the span prohibits the use of span-wise averaging to improve the signal-to-noise ratio. However, some smoothing is necessary to facilitate the further evaluation and to make the algorithm more robust. The smoothed distribution is represented by the magenta line in Fig. 2.5. For an automated detection of the transition onset and end locations, first the peak is detected and a suitable threshold of the peak has to be chosen, for example 25% as depicted in Fig. 2.5. The red dot and blue dot mark the onset of transition in image A and the end of transition in image B, respectively. Lower thresholds can lead to significantly more outliers than higher thresholds. In contrast to the ideal case shown in Fig. 2.3, a threshold has to be chosen that represents a compromise between accuracy and robustness for an automatic analysis over the entire pitching cycle. The positions of the onset and the end of transition detected with a certain threshold are therefore located slightly downstream of the real onset position and slightly upstream of the real end position, respectively. This means that both locations are within the transitional region existing on the airfoil and the length of the detected transition region is in practice slightly smaller.

(a) (b) (c)

Fig. 2.4 Instantaneous raw infrared images a) and b) (αA > αB). Differential infrared image as result of the subtraction of image A minus image B in c). The blue line indicates the cut for intensity data extraction shown in Fig. 2.5.


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Fig. 2.5 Extracted intensity data versus chord along the blue line in Fig. 2.4c. Black dots indicate the raw data while the smoothed distribution is represented by the magenta line. The red dot marks the onset of transition in image A and the blue dot marks the end of transition in image B for a chosen threshold of 25% peak height.

3. Application on a pitching airfoil 3.1 Experimental setup A pitching NACA0012 airfoil in an open test section of a closed loop wind tunnel was used to demonstrate the principle of differential infrared thermography (see Raffel and Merz, 2014; Merz et al, 2014). The model had a chord length of c=0.3m and was made from carbon fiber reinforced plastic with a high surface emissivity. The model surface on the suction side was constantly heated by a 2kW halogen lamp and was cooled by the oncoming flow of the wind tunnel as shown by the sketch in Fig. 3.1. This resulted in a surface temperature of about 40°C compared to the air flow temperature of approximately 20°C. A FLIR SC7750L infrared MCT-camera, sensitive in the 7.85µm to 9.5µm wavelength range was used. The camera was mounted above the suction side of the airfoil and recorded the temperature distribution in an area around mid-span of the model from the leading edge to approximately 38% of the chord. The readout area was reduced to 350x480 pixels to achieve a frame rate of facq=210Hz at an exposure time of texp=200µs. The pitching motion was set to α(t)=6.7°+2.0°sin(2πft). In total, four pitching frequencies (f=1, 2, 4 and 8 Hz) were measured with a free stream velocity of U∞=50m/s. In the previous studies, the evaluation was based on the method of taking differences of successively taken measurement images for the cases at f=4Hz and f=8Hz. In the current study, the two evaluation approaches – moving references and fixed references – have been applied to the case at f=1Hz. The flow in the mid-span section of the airfoil is nearly two-dimensional. The differential intensity distributions showed no variation of the transition locations in the span-wise direction over a significant width of the measurement area. Therefore, averaging of the differential intensity distributions is possible and an average over 80 pixels in span-wise direction has been used to improve the signal-to-noise ratio. In general, the peaks in the differential intensities are significantly sharper for the pitching airfoil than for the rotating blade shown in Fig. 2.5. This allows for a larger threshold, making the automatic evaluation more robust without sacrificing much of the accuracy. A threshold of 80% has been used for the evaluation of the moving reference images for the pitching airfoil.


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Fig. 3.1 Sketch of the experimental setup for the pitching airfoil.

3.2 Computational setup Two-dimensional unsteady CFD simulations of the pitching NACA0012 were carried out using the DLR-TAU code. A Spalart-Allmaras turbulence model was used, together with an eN-Method (N=9) for the transition prediction. This “quasi-steady” transition prediction is applicable for the pitching rates in this study (see Richter et al, 2011; Heister, 2013). Mach and Reynolds numbers as well as the reduced frequency k=πfc/U∞ were chosen to match the experimental conditions. A wind tunnel correction was applied to the angle of attack of the wind tunnel model. Junger and Gardner (2013) showed that a correction of αCFD= αExp/1.5 is needed for the open test section that was used for these experiments. Therefore, the pitching motion was set to α(t)=4.5°+1.3°sin(2πft). One oscillation period was resolved with 400 time steps with 300 inner iterations per time step. After three oscillation periods, convergence was reached. For more details on the CFD setup see Merz et al (2014). 3.3 Results and discussion Figure 3.2 shows the results of the experiment, obtained with the moving reference evaluation with an offset of one picture (Δt/T≈0.005) for a pitching frequency of f=1Hz. The data is phase-averaged over four pitching cycles. The transition onset and end locations are normalized by the chord length and are plotted over one oscillation period. The angle of attack is depicted as well. It is shown that this evaluation method provides realistic values during upstroke and downstroke but exhibits a strong scatter near the inflection points of the airfoil motion. The data near the inflection points has therefore been invalidated as indicated by the open symbols. The evaluation method with the fixed references can provide information about the transition onset at the maximum angle of attack and the transition end at the minimum angle of attack. It is possible to obtain a more complete data set by using the valid data points of the moving reference evaluation supplemented with the data near the inflection points of the fixed reference evaluation, as described in Chapter 2. The results of the two methods combined are shown in Fig. 3.3. Note that there still remain gaps for the transition onset at αmin and for the transition end at αmax. Nevertheless, with valid data for most of the cycle it is now possible to perform a fit of the experimental data. Here, second order Fourier series are used to fit the transition onset and end locations as indicated by the dashed lines. The raw data as well as the fit show a clear difference in the size of the transitional region depending on the angle of attack. For higher angles of attack, the transitional region (the distance between transition onset and end) is smaller than for lower angles of attack. This phenomenon can be explained by a reduced adverse pressure gradient for smaller angles of attack. In that case the boundary layer transition from laminar to fully turbulent occurs over a longer distance. The fitted curves show a small phase shift between the transition onset and end which seems to be an artifact of the measurement noise. A comparison between the transition onset of the experimental results and the CFD calculations is shown in Fig. 3.4. The CFD results are depicted as open circles. A second order Fourier series was fitted through the CFD data as well, indicated by the green line. It is shown that this type of fit is suitable for the motion of the transition location for this test case. There is a good agreement between the CFD and the experimental


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results concerning the general trend of the transition location. However, the CFD predicts overall a slightly earlier transition than the measurements. The difference between the two ranges from little more than 1% of the chord to about 4% of the chord. Around αmax, the transition location in both experiment and CFD show a wider peak than around the minimum angle of attack. The reason for this is a non-linear dependence between the transition location and the angle of attack.

Fig. 3.4 Experimental transition onset locations and CFD results for the pitching airfoil.

4. Application on a rotor 4.1 Experimental setup The rotor experiments took place at the new Rotor Test Facility (RTG) at the DLR in Göttingen. The rotor is equipped with four rectangular untwisted blades consisting of a modified NACA0015 airfoil. They are made from glass-fiber reinforced plastic, and have a chord of c=54mm and a blade tip radius of Rtip=510mm. The rotor head is equipped with a swashplate that allows the adjustment of the collective and cyclic blade pitch angles. The stationary mount of the swashplate can be rotated to sweep through the pitch range at one azimuthal position of the rotor. This allows for the investigation of the pitch angle variation of the rotating blade in a fixed field of view. The sinusoidal cyclic pitch was set to a geometric angle of attack of α(t)=11.5°+1.4°sin(2πfrt). The rotation frequency was fr=14Hz resulting in a blade tip speed of Utip=44.9m/s, a blade tip Mach number of Mtip=0.13, a chord based Reynolds number at the tip of Retip=1.6x105 and a reduced frequency of ktip= πfrc/Utip = 0.05.

Fig. 3.2 Relative transition onset and end locations evaluated with the moving reference method for the pitching airfoil.

Fig. 3.3 Relative transition onset and end locations using a combination of the two evaluation strategies for the pitching airfoil.


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The measurement setup is depicted in Fig. 4.1. The rotor blade was radiatively heated by two 2kW halogen lamps to obtain a temperature difference between the blade surface and the air flow, which had a temperature of approximately 15°C. This was done to achieve a higher temperature difference between the rotor blades and the surrounding air and thus to increase the signal to noise ratio. The FLIR SC7750L infrared camera was used above the rotor blade upper side with a spatial resolution of 640x512 pixels and an exposure time of texp=200µs. The measurements were carried out with two different fields of view. The first field of view was set between r/R=0.65 and r/R=0.85 of the rotor radius and the second was between r/R=0.8 of the rotor radius and the blade tip. Results are shown for the first field of view. The camera was operated with a frame rate of facq=7Hz. For one complete sinusoidal pitch variation, i.e. one revolution of the swashplate, 497 images were recorded in a measurement time of t=71s. A rotating mirror was used in on-axis configuration to avoid image blurring, as described in detail by Raffel and Heineck (2014). A postprocessing of the thermal images was performed using the DLR-software ToPas. Both the flapping and lead-lag of the blade and an imperfect synchronization between the rotating mirror and the rotor caused small non-periodic movements of the blade within the field of view. For the correct subtraction of the thermal images, the images were moved and de-warped based on markers on the blade surface. The markers on the rotor blade are visible as black dots in Figures 2.4a and 2.4b.

Fig. 4.1 Sketch of the experimental setup for the rotor in the RTG. The blue and the red lines indicate the radial positions discussed in Chapter 4.2.

4.2 Results and discussion The evaluation of the thermal images measured on the rotating blade was performed similar to the analysis of the images obtained on the pitching airfoil model. However, instead of using successive images for the subtraction, an offset of 30 pictures (Δt/T=0.06) between the measurement and the moving reference image was used. With smaller offsets, the signal-to-noise ratio of the differential image was too low. The positions of the approximate onset and end of the transitional region were detected by using a threshold of 40% of the peak in the intensity difference distribution. A threshold of 40% was chosen to achieve a robust automatic analysis over the entire pitching cycle, whereas lower thresholds resulted in significantly more outliers. As discussed in Chapter 2, the measurement therefore underpredicts the extent of the chord-wise transition region slightly. For the DIT analysis of the rotor blade images, a combination of the evaluation with fixed and moving reference images was used to obtain as much information as possible during the entire pitching cycle. The results obtained at a radial position of r/R=0.82 of the rotor radius are shown in Fig. 4.2 by the detected onset and end locations of transition xtr/c versus the non-dimensional time t/T for one cycle. The red symbols represent the onset location of the transition and the blue symbols mark the end location, both evaluated with a moving reference image. Transition locations provided by the fixed reference evaluation method are used to fill up gaps near the inflection points where valid data is missing with the moving reference evaluation. The onset of transition obtained with a fixed reference evaluation is represented by the magenta symbols and the end of transition by the green symbols. The r/R=0.82 radial position is indicated by the red lines in


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Fig. 2.4c and Fig. 4.1. Similar to the pitching airfoil model, the rotating blade with cyclic pitch motion also shows a strong movement of the transition over the blade upper surface, as can be seen by both the onset and the end locations. Compared to the airfoil results, transition locations detected on the rotor exhibit more noise. Compared to the pitching airfoil case, the number of outliers is significantly higher. This leads to larger parts of the period where no valid transition positions could be detected. In contrast to the sinusoidal transition motion on the airfoil, the motion on the rotor blade seems to be more complex and fragmented in three nearly linear parts for both the upstroke and the downstroke. The most upstream location of the onset of transition is located around xtr/c =0.22 and the most downstream location which can be detected is around xtr/c=0.61. Starting from t/T=0 to approximately t/T=0.07 there is a rapid upstream motion of the transition onset location. A sudden change appears at t/T=0.07 when the transition location moves upstream at a significantly reduced speed. At t/T=0.25, the transition onset starts to move downstream at a similar rate than the upstream motion before. Then, between t/T=0.40 to t/T=0.48, the transition onset rapidly moves downstream from xtr/c=0.3 to xtr/c=0.48. This motion is followed by a moderate rate of change in the transition onset location from xtr/c=0.48 to xtr/c=0.61 over the interval 0.48≤t/T≤0.65. No data is available from t/T=0.65 to t/T=0.88 because of the lack of a reference image in which the transition is located further downstream. This problem was discussed in Chapter 2 and also occurs for the pitching airfoil. Comparing the results from t/T=0.25 until t/T=0.75 with the results from t/T=0.75 to t/T=1 and t/T=0 to t/T=0.25, the movement of the transition onset position between the upstroke and the downstroke appears approximately symmetrical. While the detected onset position has missing data points primarily at the downstream end of the movement, the detected end position of the transition shows missing data points at the upstream end of the movement. Therefore, between t/T=0.13 and t/T=0.32 no information about the end of transition can be given. In the region in between t/T=0.32 and t/T=0.72 the end position moves linearly downstream on the downstroke and similarly on the upstroke from t/T=0.75 to t/T=1 and t/T=0 to t/T=0.13. A fragmentation of the movement into different regions as existing for the onset location cannot be seen for the end location. The most upstream point of the end of transition that can be detected is located at xtr/c=0.43, the most downstream location is at xtr/c=0.78. Note that the transition end location evaluated with the moving reference image method is nicely completed with data obtained from an evaluation with a fixed reference image in the downstream part of the movement. The gap that has to be filled is significantly smaller for the upstream part of the onset of transition. Nevertheless, the fixed reference data points are in good accordance with the onset transition location gathered from the moving reference evaluation method.

Fig. 4.2 Relative transition onset and end locations at a radial position of r/R=0.82.

The transition results obtained at r/R=0.67 are shown for the same test case in Fig. 4.3. The position of this section on the rotor is indicated by the blue lines in Fig. 2.4c and Fig. 4.1. The onset of transition is again represented by the red symbols and the end of transition by the blue symbols. Data points obtained from a fixed reference evaluation are represented by magenta and green symbols. A movement of both the onset and the end of transition was detected also at this radial section but the extent of the movement is significantly


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smaller than at r/R=0.82. The transition also generally occurs much further downstream, which can also be seen in the thermal images in Fig. 2.4. The noise level in the transition data is significantly higher at r/R=0.67 than at r/R=0.82 and the data contains more outliers due to lower and wider peaks in the differential images compared to the r/R=0.82 position. In contrast to the results at r/R=0.82, no sharp slope changes can be seen. Instead, a smoothly curved transition behavior is indicated with a nearly linear part for the onset of transition location between t/T=0.24 and t/T=0.49. For a significant part of the cycle, the end position of the transition lies almost at the trailing edge of the rotor blade (xtr/c=0.97). The transition movement is limited here, and the end position shows an approximately constant location between t/T=0.65 and t/T=0.95. Figure 4.4 shows the detected transition data for the r/R=0.67 case with all valid and invalid data points in order to illustrate the amount of outliers that are produced for this relatively noisy case compared for example with the pitching airfoil data depicted in Fig. 3.2. The onset of transition is again represented by the red symbols and the end of transition by the blue symbols. Invalid data points that are not shown in Fig. 4.3 are shown as open symbols. For this data points no distinct peak can be detected in the extracted differential intensity distributions. This is due to small peaks that are not significantly higher than the noise level or because of multiple other peaks with the same magnitude. This leads to randomly detected peak locations in chord-wise direction and therefore also the onset and end of transition are moving arbitrarily. For all valid data points the detection of the peak works fine as described in Chapter 2 and shown in Fig. 2.5.

Fig. 4.3 Relative transition onset and end locations at a radial position of r/R=0.67.

Fig. 4.4 Relative transition onset and end locations at a radial position of r/R=0.67. Solid symbols indicate valid data points as shown in Fig. 4.3, open symbols represent invalid data points.

Compared to the application of DIT on the pitching airfoil, the current application on the rotor shows additional challenges both in the measurement and in the image analysis that were found to degrade the quality of the results. Firstly, the temperature difference established between the model surface and the air flow was significantly lower for the rotor tests since the effective heating was different. In case of the pitching airfoil it was possible to continuously heat the model surface during the whole pitching cycle. In the rotating case, the rotor blade rotated through the heated area only for small portions of the cycle. Heating the rotating blade over the entire cycle would only be possible with more halogen lamps. Secondly, the image postprocessing is more complex in the rotating case than in the pitching airfoil case. The image de-warping and the shifting of the raw images is more complicated due to the flapping and the lead-lag motion of the rotor blades especially when an undamped rotor is used as in this case. Thirdly, for the rotor test a rotating mirror had to be used, which was not needed in the airfoil test. The synchronization of the rotor and the rotating mirror is difficult and introduces an additional arbitrary blade movement in the infrared images due to synchronization imperfections of rotor and mirror. The mapping process needed for compensation always introduces additional noise to the data. Furthermore, span-wise averaging is possible on the two-dimensional airfoil to increase the signal-to-noise ratio. Since there is no two-dimensional flow existing on the rotor, span-wise averaging is difficult and has to be done with great care in order to not distort existing three-dimensional flow features.


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5. Conclusion and Outlook Optical measurements of the unsteady laminar-turbulent boundary layer transition locations were performed on a pitching airfoil and for the first time on an articulated model scale rotor. The differential infrared thermography (DIT) method has been evaluated and improved on a pitching NACA0012 airfoil. Two different evaluation strategies that use moving and fixed reference images were combined to gain as much information as possible and thus to minimize the gaps were transition data is missing. Both approaches complemented each other nicely. Furthermore, a good agreement between two-dimensional CFD simulations and the measured transition locations on the pitching airfoil was found. Additionally, the feasibility of the DIT technique in rotating frames has been verified on a model scale rotor in cyclic pitch. In spite of the fact that the evaluation is by far more challenging than for pitching airfoils, the high spatial resolution compared to other measurement techniques was demonstrated and span-wise differences in the unsteady transition locations were revealed. The results open the door for future investigations of the unsteady boundary layer transition of helicopter rotor blades in forward flight without the need of blade instrumentation. This could enable to investigate the influence of cyclic pitch, varying relative flow velocities, varying radial load distributions and crossflow. It might even allow the dynamic stall to be investigated on rotating blades by thermography. Further evaluations of the accuracy and improvements of the robustness, especially near the inflection points of the cyclic pitching motion, are planned. Acknowledgments The support of our colleagues A.D. Gardner, K. de Groot and S. Rafati is highly appreciated. Furthermore the authors would like to thank J. Sarfels and M. Hayk for making up-to-date infrared cameras available. References Chandrasekhara MS, Wilder MC (2003) Heatflux Gauge Studies of Compressible Dynamic Stall. AIAA Journal, Vol. 41, No. 5, pp. 757–762. doi:10.2514/2.2019 Heister CC (2013) Numerical Investigation of Laminar-Turbulent Transition Mechanisms for Helicopter Rotors in Forward Flight. Proceedings of the 69th Annual Forum, American Helicopter Society International, Phoenix, Arizona, USA. Junger C, Gardner AD (2013) Numerische Untersuchung des Ein-Meter-Windkanals mit offener und geschlossener Messstrecke. German Aerospace Center (DLR) Report IB 224-2013 A93. Lorber P, Carta F (1992) Unsteady Transition Measurements on a Pitching Three-Dimensional Wing. 5th Symposium on Numerical and Physical Aspects of Aerodynamic Flows, Long Beach, California, USA. Merz CB, Richter K, Raffel M (2014) Unsteady boundary layer transition measurements by differential infrared thermography. Proceedings of the 70th Annual Forum, American Helicopter Society International, Montréal, Québec, Canada. Raffel M, de Gregorio F, de Groot K, Schneider O, Sheng W, Gibertini G, Seraudie A (2011) On the Generation of a Helicopter Aerodynamic Database. The Aeronautical Journal, The Royal Aeronautical Society (RAeS), Vol. 115, No. 1164, pp. 103-112. Raffel M, Heineck JT (2014) Mirror-Based Image Derotation for Aerodynamic Rotor Measurements. AIAA J. doi:10.2514/1.J052836


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Raffel M, Merz CB (2014) Differential Infrared Thermography for Unsteady Boundary Layer Transition Measurements. AIAA Journal. Accepted for publication. Richter K, Le Pape A, Knopp T, Costes M, Gleize V, Gardner AD (2011) Improved Two-Dimensional Dynamic Stall Prediction with Structured and Hybrid Numerical Methods. Journal of the American Helicopter Society. doi:10.4050/JAHS.56.042007 Richter K, Koch S, Gardner AD (2013) Influence of oscillation amplitude and Mach number on the unsteady transition on a pitching rotor blade airfoil. Proceedings of the 69th Annual Forum, American Helicopter Society International, Phoenix, Arizona, USA. Richter K, Koch S, Gardner AD, Mai H, Klein A, Rohardt C-H (2014) Experimental investigation of unsteady transition on a pitching rotor blade airfoil. Journal of the American Helicopter Society, Vol. 59, No. 1. doi: 10.4050/JAHS.59.012001 Richter K, Schülein E (2014) Boundary Layer Transition Measurements on Hovering Helicopter Rotors by Infrared Thermography. Experiments in Fluids. Accepted for publication. Rohardt CH (1986) Strömungssichtbarmachung an Hubschrauberrotorblättern mittels Acenaphthen. German Aerospace Center (DLR) Report IB 129-86/18. Schülein E, Rosemann H, Schaber S (2012) Transition Detection and Skin Friction Measurements on Rotating Propeller Blades. Paper AIAA-2012-3202, 28th AIAA Aerodynamic Measurement Technology, Ground Testing and Flight Testing Conference, New Orleans, Louisiana, USA. doi: 10.2514/6.2012-3202 Tanner WH, Yaggy PF (1966) Experimental Boundary Layer Study on Hovering Rotors. Journal of the American Helicopter Society, Vol. 11, No. 3, pp. 22-37. Wadcock AJ, Yamauchi GK, Driver DM (1999) Skin Friction Measurements on a Hovering Full-Scale Tilt Rotor. Journal of the American Helicopter Society, Vol. 44, No. 4, pp. 312-319. Yorita D, Asai K, Klein C, Henne U, Schaber S (2012) Transition Detection on Rotating Propeller Blades by means of Temperature-Sensitive Paint. Paper AIAA-2012-1187, 50th AIAA Aerospace Sciences Meeting, Nashville, Tennessee, USA. doi: 10.2514/6.2012-1187

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