Successful Application of Cryogenic Pressure Sensitive Paint Technique at ETW

Yorita, Daisuke; Klein, Christian; Henne, Ulrich; Ondrus, Vladimir; Beifuß, Uwe; Hensch, Ann-Katrin; Longo, Roberto C.; Guntermann, Peter; Quest, Jürgen

doi:10.2514/6.2018-1136

Cited by 17 publications

(3 citation statements)

References 10 publications

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“…Furthermore, PtTFPP, in combination with the highly O 2 -permeable polymer poly[1-(trimethylsilyl)-1-propyne] (PTMSP), is an excellent luminescent sensor for measurements under cryogenic conditions, which enable testing models at near-full-scale Reynolds numbers. With such paints, pressure distributions due to complex shock structures can be visualized near the leading edge region (Figure 22) [175][176][177][178]. Ruthenium-based PSPs have a shorter lifetime compared to PtTFPP-based PSPs, and both are suitable for intensity-and lifetime-based measurements using digital cameras.…”

Section: Pressure-sensitive Paintsmentioning

confidence: 99%

The Art of Fluorescence Imaging with Chemical Sensors: The Next Decade 2012–2022

Schäferling,

Ondrus

2024

Chemosensors

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Imaging methods by the means of optical sensors are applied in diverse scientific areas such as medical research and diagnostics, aerodynamics, environmental analysis, or marine research. After a general introduction to the field, this review is focused on works published between 2012 and 2022. The covered topics include planar sensors (optrodes), nanoprobes, and sensitive coatings. Advanced sensor materials combined with imaging technologies enable the visualization of parameters which exhibit no intrinsic color or fluorescence, such as oxygen, pH, CO2, H2O2, Ca2+, or temperature. The progress on the development of multiple sensors and methods for referenced signal read out is also highlighted, as is the recent progress in device design and application formats using model systems in the lab or methods for measurements’ in the field.

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Section: Pressure-sensitive Paintsmentioning

confidence: 99%

The Art of Fluorescence Imaging with Chemical Sensors: The Next Decade 2012–2022

Schäferling,

Ondrus

2024

Chemosensors

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“…The experimental investigations, which have been conducted by the ETW and the German Aerospace Center (DLR), included unsteady pressure sensitive paint [28,29] measurements, which are referred to as iPSP in the following. With the iPSP setup, upper wing surface buffet data acquisition was possible with a sampling frequency of 1000 Hz.…”

Section: Test Case: Airbus Xrf-1mentioning

confidence: 99%

Prediction of wing buffet pressure loads using a convolutional and recurrent neural network framework

2023

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In the present study, a hybrid deep learning reduced-order model (ROM) is applied for the prediction of wing buffet pressure distributions on a civil aircraft configuration. The hybrid model is compound of a convolutional variational neural network autoencoder (CNN-VAR-AE) and a long short-term memory (LSTM) neural network. The CNN-VAR-AE is used for the reduction of the high-dimensional flow field data, whereas the LSTM is applied to predict the temporal evolution of the pressure distributions. For training the neural network, experimental buffet data obtained by unsteady pressure sensitive paint measurement (iPSP), is applied. As a test case, the Airbus XRF-1 configuration is selected, considering two different experimental setups. The first setup is defined by a wind tunnel model with a clean wing, whereas the second setup includes an ultra high bypass ratio engine nacelle on each wing. Both configurations have been tested in the European Transonic Windtunnel, considering several transonic buffet conditions. Finalizing the training of the hybrid neural networks, the trained models are applied for the prediction of buffet flow conditions which are not included in the training data set. A comparison of the experimental results and the pressure distributions predicted by the hybrid ROMs indicate a precise prediction performance. Considering both aircraft configurations, the main buffet flow features are captured by the hybrid ROMs.

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“…A possible way to correct this bias is an in situ calibration of the PSP, where luminescence intensities on the model are correlated with appropriate reference measurements within a significant range (see e.g. Yorita et al 2018a). Alternatively, an offset correction of the results can be applied by "anchoring" the iPSP data with pressure transducer readings at the same location.…”

Section: Offset Correctionmentioning

confidence: 99%

Dynamic-stall measurements using time-resolved pressure-sensitive paint on double-swept rotor blades

et al. 2021

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The study presents an optimized pressure-sensitive paint (PSP) measurement system that was applied to investigate unsteady surface pressures on recently developed double-swept rotor blades in the rotor test facility at the German Aerospace Center (DLR) in Göttingen. The measurement system featured an improved version of a double-shutter camera that was designed to reduce image blur in PSP measurements on fast rotating blades. It also comprised DLR’s PSP sensor, developed to capture transient flow phenomena (iPSP). Unsteady surface pressures were acquired across the outer 65% of the rotor blade with iPSP and at several radial blade sections by fast-response pressure transducers at blade-tip Mach and Reynolds numbers of $$\mathrm {M}_\mathrm{tip} = 0.282-0.285$$ M tip = 0.282 - 0.285 and $$\mathrm {Re}_\mathrm{tip}= 5.84-5.95 \times 10^5$$ Re tip = 5.84 - 5.95 × 10 5 . The unique experimental setup allowed for scanning surface pressures across the entire pitch cycle at a phase resolution of $${0.225}\,{\mathrm{deg}}$$ 0.225 deg azimuth for different collective and cyclic-pitch settings. Experimental results of both investigated cyclic-pitch settings are compared in detail to a delayed detached eddy simulation using the flow solver FLOWer and to flow visualizations from unsteady Reynolds-averaged Navier–Stokes (URANS) computations with DLR’s TAU code. The findings reveal a detailed and yet unseen insight into the pressure footprint of double-swept rotor blades undergoing dynamic stall and allow for deducing “stall maps”, where confined areas of stalled flow on the blade are identifiable as a function of the pitch phase. Graphical abstract

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Successful Application of Cryogenic Pressure Sensitive Paint Technique at ETW

Cited by 17 publications

References 10 publications

The Art of Fluorescence Imaging with Chemical Sensors: The Next Decade 2012–2022

The Art of Fluorescence Imaging with Chemical Sensors: The Next Decade 2012–2022

Prediction of wing buffet pressure loads using a convolutional and recurrent neural network framework

Dynamic-stall measurements using time-resolved pressure-sensitive paint on double-swept rotor blades

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