On image pre-processing for PIV of single- and two-phase flows over reflecting objects

Deen, NG Niels; Willems, P; Annaland, Martin van Sint; Kuipers, J.A.M.; Lammertink, Rob G.H.; Kemperman, A.J.B.; Weßling, Matthias; Meer, W.G.J. van der

doi:10.1007/s00348-010-0827-y

Cited by 33 publications

(22 citation statements)

References 9 publications

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“…Raw image pairs were pre-processed following the methodology of Deen et al [32] to reduce the effect of any hardware reflections that were not eliminated using the methods discussed above. A multi-pass method with an initial window size of 64 Â 64, and a final size of 16 Â 16 with a 50 percent overlap was used to calculate the velocity field.…”

Section: Piv Measurementsmentioning

confidence: 99%

Impact of chevron spacing and asymmetric distribution on supersonic jet acoustics and flow

Heeb

Gutmark

Kailasanath

2016

Journal of Sound and Vibration

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Section: Piv Measurementsmentioning

confidence: 99%

Impact of chevron spacing and asymmetric distribution on supersonic jet acoustics and flow

Heeb

Gutmark

Kailasanath

2016

Journal of Sound and Vibration

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“…In particular, as the laser lightsheet was parallel to and within a few millimeters of the rough surfaces, surface reflections became a debilitating issue. While smooth-wall measurements were not tainted by such effects, the complex nature of the surface roughness under consideration led to intense reflections of laser light that was directed in all directions Figure 6: Image after fixed objects subtraction using original algorithm of Deen et al [42]. (a) General view; (b) Zoom in on a former bright spot.…”

Section: Streamwise-spanwise Plane Stereo Piv Detailsmentioning

confidence: 99%

“…These background reflections unfortunately corrupt the PIV interrogation and induce strong bias in single-point statistics [(t/ 2 ) 4 " shown in figure 5(b)]. A solution to this type of background reflection problem was recently proposed by Deen et al [42]. The basic idea of this method is to subtract frame B from frame A of instantaneous PIV realizations, which in theory would render the intensity of the fixed objects zero since they would scatter similar light in both frames (compared to the random nature of the scattered light from the moving tracer particles).…”

Section: Streamwise-spanwise Plane Stereo Piv Detailsmentioning

confidence: 99%

YIP-Low-Order Representations of Irregular Surface Roughness and Their Impact on Wall Turbulence

Mejía-Álvarez¹,

Christensen²

2010

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REPORT DOCUMENTATION PAGEThe pubic reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathenng and maintaining the data needed, and oompietjng and reviewing the collection of information Send comments regardng this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to the Department of Defense. Executive Service Directorate (0704-0188), Respondents should be aware that notwithstanding any other provision of law. no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION. REPORT DATE (DD-MM-YYYY) 28 SPONSOR/MONITORS ACRONYM(S) AFOSR SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release SUPPLEMENTARY NOTES 2010091628014. ABSTRACT This effort explored the impact of topographical scales present within irregular surface roughness on a turbulent boundary layer. Low-order representations of a surface replicated from a turbine blade damaged by deposition of foreign materials were generated using singular value decomposition to decompose the complex topography into a set of basis functions of decreasing importance to the original ("full") surface. The low-order surface models were then formed by truncating the basis functions at the first 5 and 16 modes (containing 71 % and 95% of the full surface content, respectively), so that only the largest-scale topographical features were included. Physical replications of these surfaces were rapid prototyped and particle-image velocimetry was used to measure the flow over these surfaces in the streamwise-wall-normal plane and in a streamwise^spanwise plane deep within the roughness sublayer. A 16-mode model of the full surface faithfully reproduced most of the characteristics of flow over the full surface for both developing and developed flow conditions. However, both models failed to reproduce important details of the Reynolds-shear-stress-producing events within the roughness sublayer. The final report details these observations. This effort explored the impact of various topographical scales present within irregular surface roughness on a turbulent boundary layer. Low-order representations of a surface replicated from a turbine blade damaged by deposition of foreign materials were generated using singular value decomposition to decompose the complex topography into a set of topographical basis functions of decreasing importance to the original ("full") surface. The low-order surface models were then formed by truncating the full set of basis functions at the first 5 and 16 modes (containing 71% and 95% of the full surface content, respectively), so that only the largest-scale topographical features were included in the models while the finer-scale surface details were excluded. Physical rep...

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“…In these situations, one is obliged to combine the existing techniques, on a case-by-case basis, in complex image analysis schemes (e.g. [22,23]) or utilize temporal high pass filtering if the background noise source is temporally resolved, as is the case in most high-speed PIV experiments [24]. The final outcome is, in general, strongly dependent on the experience of the user [25].…”

Section: Introductionmentioning

confidence: 99%

POD-based background removal for particle image velocimetry

Mendez

Raiola

Masullo

et al. 2017

Experimental Thermal and Fluid Science

119

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State-of-art preprocessing methods for Particle Image Velocimetry (PIV) are severely challenged by time-dependent light reflections and strongly non-uniform background. In this work, a novel image preprocessing method is proposed. The method is based on the Proper Orthogonal Decomposition (POD) of the image recording sequence and exploits the different spatial and temporal coherence of background and particles. After describing the theoretical framework, the method is tested on synthetic and experimental images, and compared with well-known pre-processing techniques in terms of image quality enhancement, improvements in the PIV interrogation and computational cost. The results show that, unlike existing techniques, the proposed method is robust in the presence of significant background noise intensity, gradients, and temporal oscillations. Moreover, the computational cost is one to two orders of magnitude lower than conventional image normalization methods. A downloadable version of the preprocessing toolbox has been made available at http://seis.bris.ac.uk/~aexrt/PIVPODPreprocessing/.

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On image pre-processing for PIV of single- and two-phase flows over reflecting objects

Cited by 33 publications

References 9 publications

Impact of chevron spacing and asymmetric distribution on supersonic jet acoustics and flow

Impact of chevron spacing and asymmetric distribution on supersonic jet acoustics and flow

YIP-Low-Order Representations of Irregular Surface Roughness and Their Impact on Wall Turbulence

POD-based background removal for particle image velocimetry

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