Multicolor particle shadow accelerometry

McPhail, Michael; Krane, Michael; Fontaine, Arnold A.; Goss, L. P.; Crafton, Jim

doi:10.1088/0957-0233/26/4/045301

Cited by 6 publications

(1 citation statement)

References 29 publications

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“…This has been demonstrated by McPhail et al . 33 for their planar PSV experiments. However, having the full 3-D velocity we can also obtain the advected acceleration term u •∇ u .…”

Section: Resultsmentioning

confidence: 99%

Tomographic Particle Image Velocimetry using Smartphones and Colored Shadows

Aguirre-Pablo

Alarfaj

et al. 2017

Sci Rep

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We demonstrate the viability of using four low-cost smartphone cameras to perform Tomographic PIV. We use colored shadows to imprint two or three different time-steps on the same image. The backlighting is accomplished with three sets of differently-colored pulsed LEDs. Each set of Red, Green & Blue LEDs is shone on a diffuser screen facing each of the cameras. We thereby record the RGB-colored shadows of opaque suspended particles, rather than the conventionally used scattered light. We subsequently separate the RGB color channels, to represent the separate times, with preprocessing to minimize noise and cross-talk. We use commercially available Tomo-PIV software for the calibration, 3-D particle reconstruction and particle-field correlations, to obtain all three velocity components in a volume. Acceleration estimations can be done thanks to the triple pulse illumination. Our test flow is a vortex ring produced by forcing flow through a circular orifice, using a flexible membrane, which is driven by a pressurized air pulse. Our system is compared to a commercial stereoscopic PIV system for error estimations. We believe this proof of concept experiment will make this technique available for education, industry and scientists for a fraction of the hardware cost needed for traditional Tomo-PIV.Flow visualization and quantitative velocity measurements are the foundations of experimental fluid mechanics. They have, with the other pillars of theory and numerical simulations, built up our understanding of the dynamics of complex or turbulent flows. Since the invention of the CCD sensor the advances in digital cameras and computational power have gone hand-in-hand as their electronic fabrication methods have taken a parallel track. This is particularly true in the recent advances in smartphone technology, where a camera and a computational brain have been integrated into a rapidly developing combined device and the economics of scale have allowed the addition of advanced features into these mass-produced camera-sensors, at minimal cost. Indeed as smartphones have become ubiquitous in the general population, they are equipped with high quality sensors such as gyroscopes, accelerometers, GPS and one or two CMOS camera sensors. These features allow people to communicate, record vast amounts of information and even monitor their health at a fraction of the cost of similar commercial scientific cameras and other sensors used in industry and research, thereby, becoming a valuable tool in everyone's daily life. Herein we demonstrate that the recent very high pixel-count of a few of these camera sensors can be combined together for measuring three-dimensional velocity fields.Particle Image Velocimetry (PIV) is the most powerful modern technique to measure extended velocity fields 1-4 . This relies on seeding the flow-volume with small tracer particles which are illuminated with pulsed light and digital cameras capture images of their displacement with time. The velocity field is subsequently extracted through image correlation...

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“…This has been demonstrated by McPhail et al . 33 for their planar PSV experiments. However, having the full 3-D velocity we can also obtain the advected acceleration term u •∇ u .…”

Section: Resultsmentioning

confidence: 99%