A novel self-seeding method for particle image velocimetry measurements of subsonic and supersonic flows

Nematollahi, Omid; Samsam-Khayani, Hadi; Nili-Ahmadabadi, Mahdi; Yoon, Sang Youl; Kim, Kyung Chun

doi:10.1038/s41598-020-67680-5

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…In recent years, due to the spreading of cross-correlation image processing algorithms, a significant amount of papers has appeared using the seedless tracing -tracking structural elements, markers present in the flow itself. This "schlieren PIV" technique uses natural inhomogeneities and turbulent vortices in the flow as virtual "seeding particles" on which the velocity is measured [128][129][130][131].…”

Section: Pivmentioning

confidence: 99%

Methods for Panoramic Visualization and Digital Analysis of Thermophysical Flow Fields. A Review.

Znamenskaya¹

2021

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The paper presents a review of modern methods in the registration, processing, and analysis of dynamic processes in liquids, gases, plasmas, multiphase media, which are realized in researches and technology. The review author observes both the physical fundamentals of flow visualization and the basics of modern technologies for digital processing of recorded flow images. A brief analysis of the panoramic visualization methods progress history covers a period of one and a half centuries. In the works of the last decade, the focus is on the methods of computer processing, tools, technologies for analyzing and recognizing of panoramic thermophysical fields, which make it possible to obtain quantitative information about flows. The review contains an analysis of publications describing the main modern methods of visualization of flows: methods based on the phenomenon of refraction; electroluminescence; on digital tracing (particle image velocimetry), visualization of surface flows (PSP and TSP coatings, liquid crystals; oil coatings). Particular attention is paid to methods using crosscorrelation image processing algorithms. Those are: digital tracing (PIV), shadow background method (in English Background Oriented Schlieren -BOS), seedless shadow methods, thermographic PIV, velocity measurement in viscous coatings, micro, tomographic modifications of PIV, etc. The actual problem of digital data analyzing in a panoramic experiment is touched upon -the problem of big data. Examples of the machine learning use in the analysis of big data sets of shadow surveys are given. Some examples of numerical simulation data visualization (simulation of experimental flowfields) are considered.

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Section: Pivmentioning

confidence: 99%

Methods for Panoramic Visualization and Digital Analysis of Thermophysical Flow Fields. A Review.

Znamenskaya¹

2021

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Schlieren Image Velocimetry of Swirl Sprays

Machado¹,

Costa²,

Andrade³

et al. 2022

Flow Turbulence Combust

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Schlieren image velocimetry is based on light de ection through ow heterogeneities and image crosscorrelations. This is a low-cost and relatively low complexity technique that allows measurement of the droplet velocity eld in a large region of a spray. A Z-type Toepler schlieren system with a high-speed camera was used to determine mean vertical and horizontal droplet velocities, as well as the cone angles of sprays produced by a pressure swirl injector with characteristic geometric constant K = 2. Different LEDs and digital lters were evaluated for edge detection and improvement of image contrast. Open software was adopted for digital image processing and velocimetry. Interrogation windows and overlaps of different sizes were tested to obtain an appropriate correlation for determination of the velocity eld.The digital images were obtained with 5×10 3 fps and a resolution of 2.77 pixels/mm. Since the swirl sprays analysed presented instabilities, a number of 100 cross-correlations of images was required to reduce mean velocity uctuations. Injection pressures varied from 0.05 to 7 bar and mass ow rates varied from 1.389 to 13.89 g/s, using water as test uid. The wideband warm white LED with Laplacian or high-pass lters provided velocity data for a larger range of injection pressures. Mean axial velocities varied from 3.3 to 11.3 m/s, approximately, with mean horizontal velocities varying from around 0.17 to 3.3 m/s for pressures from 0.05 to 3.22 bar. The velocity data were compared to microscopic shadowgraphy results, showing a good agreement. Spray cone angles ranged from about 32.5 o to 69.5 o , for injection pressures from 0.05 to 7 bar, and results of triangulation with a blue LED were closer to semiempirical data.

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Real-gas effects: The state of the art of organic Rankine cycles

Nematollahi

Kim

2020

Journal of Cleaner Production

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A novel self-seeding method for particle image velocimetry measurements of subsonic and supersonic flows

Cited by 4 publications

References 23 publications

Methods for Panoramic Visualization and Digital Analysis of Thermophysical Flow Fields. A Review.

Methods for Panoramic Visualization and Digital Analysis of Thermophysical Flow Fields. A Review.

Schlieren Image Velocimetry of Swirl Sprays

Real-gas effects: The state of the art of organic Rankine cycles

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