Hypersonic boundary-layer separation detection with pressure-sensitive paint for a cone at high angle of attack

Running, Carson L.; Sakaue, Hirotaka; Juliano, Thomas J.

doi:10.1007/s00348-018-2665-2

Cited by 26 publications

(10 citation statements)

References 33 publications

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“…Each frame was registered to within 1/100 pixel accuracy of a reference image. The images were also mapped to a x − φ coordinate system based on an image processing code before input into QCALC (Running et al 2017). For these tests, each point was solved in cylindrical coordinates, and the local wall thickness and radius of curvature was taken into account.…”

Section: Experimental Data Reductionmentioning

confidence: 99%

“…QCALC solves the 1-D heat equation, neglecting lateral conduction. The thermal conductivity, k, density, ρ, and specific heat capacity, c p , of PEEK were taken to be 0.29 W m −1 K −1 , 1300 kg m −3 and 1026 J kg −1 K −1 , respectively (Running et al 2017). Reported values give a maximum range for these parameters of ±10 %, ±1.5 %, ±3.1 %, respectively (van der Vegt 1999).…”

Section: Experimental Data Reductionmentioning

confidence: 99%

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Analysis of the hypersonic cross-flow instability with experimental wavenumber distributions

2019

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Section: Experimental Data Reductionmentioning

confidence: 99%

Section: Experimental Data Reductionmentioning

confidence: 99%

Analysis of the hypersonic cross-flow instability with experimental wavenumber distributions

2019

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“…The PSP technique has received much attention, as it enables non-intrusively measuring pressure distribution. Indeed, the PSP technique has been applied to various fluid dynamic experiments: wind tunnel testing [5][6][7][8][9], rotating machineries [10][11][12], and micro devices [13][14][15][16][17]. In the PSP technique, pressure is measured by the variation of the luminescence intensity from the PSP coating with a CCD/CMOS camera [2,3].…”

Section: Introductionmentioning

confidence: 99%

Markerless Image Alignment Method for Pressure-Sensitive Paint Image

Suzuki

Inoue

Nagata

et al. 2022

Sensors

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We propose a markerless image alignment method for pressure-sensitive paint measurement data replacing the time-consuming conventional alignment method in which the black markers are placed on the model and are detected manually. In the proposed method, feature points are detected by a boundary detection method, in which the PSP boundary is detected using the Moore-Neighbor tracing algorithm. The performance of the proposed method is compared with the conventional method based on black markers, the difference of Gaussian (DoG) detector, and the Hessian corner detector. The results by the proposed method and the DoG detector are equivalent to each other. On the other hand, the performances of the image alignment using the black marker and the Hessian corner detector are slightly worse compared with the DoG and the proposed method. The computational cost of the proposed method is half of that of the DoG method. The proposed method is a promising for the image alignment in the PSP application in the viewpoint of the alignment precision and computational cost.

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“…[1][2][3][4] The pressure measurement using PSP is based on the oxygen quenching of the phosphorescence emitted from the dye included in the PSP layer. 2,3,5 Since the pressure distribution on a model can be obtained by measuring the emission intensity distribution by a camera, PSP has been applied to a variety of aerodynamic testing such as flows around wind tunnel models, 3,[6][7][8][9][10][11][12] around rotating objects, [13][14][15][16][17] in low-pressure range, [18][19][20] and inside a micro-duct. [21][22][23][24] As described in these literatures, demand for unsteady pressure measurement increases.…”

Section: Introductionmentioning

confidence: 99%

Data-driven approach for noise reduction in pressure-sensitive paint data based on modal expansion and time-series data at optimally placed points

et al. 2021

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We propose a noise reduction method for unsteady pressure-sensitive paint (PSP) data based on modal expansion, the coefficients of which are determined from time-series data at optimally placed points. In this study, the proper orthogonal decomposition (POD) mode calculated from the time-series PSP data is used as a modal basis. Based on the POD modes, the points that effectively represent the features of the pressure distribution are optimally placed by the sensor optimization technique. Then, the time-dependent coefficient vector of the POD modes is determined by minimizing the difference between the time-series pressure data and the reconstructed pressure at the optimal points. Here, the coefficient vector is assumed to be a sparse vector. The advantage of the proposed method is a self-contained method, while existing methods use other data such as pressure tap data for the reduction of the noise. As a demonstration, we applied the proposed method to the PSP data measuring the Kármán vortex street behind a square cylinder. The reconstructed pressure data is agreed very well with the pressures independently measured by pressure transducers.

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Hypersonic boundary-layer separation detection with pressure-sensitive paint for a cone at high angle of attack

Cited by 26 publications

References 33 publications

Analysis of the hypersonic cross-flow instability with experimental wavenumber distributions

Analysis of the hypersonic cross-flow instability with experimental wavenumber distributions

Markerless Image Alignment Method for Pressure-Sensitive Paint Image

Data-driven approach for noise reduction in pressure-sensitive paint data based on modal expansion and time-series data at optimally placed points

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