Prediction of departure diameter and bubble frequency in nucleate boiling in uniformly superheated liquids

Saddy, M.; Jameson, Graeme J.

doi:10.1016/0017-9310(71)90046-9

Cited by 11 publications

(1 citation statement)

References 15 publications

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“…The application of photographic support to the better knowledge of the boiling process has been commonly used since the 1950s [ 3 , 4 ]. In the following decades, researchers studied the main aspects of nucleated boiling bubble size-time evolution [ 5 , 6 , 7 , 8 ], the nucleation mechanism [ 9 , 10 , 11 ], critical heat flux (CHF) [ 12 , 13 , 14 ] and studies about frequencies, diameters and trajectories [ 15 , 16 , 17 ] according to the technology of those years and, for some of them, with very interesting solutions, simultaneously employing more than one visualization method [ 18 , 19 ]. With the advances in high-speed photography, researchers commenced to use snapshots to formulate and validate models for different aspects of bubbling parameters, such as bubble dynamics and time evolution [ 20 , 21 , 22 , 23 , 24 , 25 ] and CHF and pattern analysis [ 26 , 27 , 28 , 29 ], as well as set down the rules for the first wall heat partitioning models.…”

Section: Introductionmentioning

confidence: 99%

On the Application of Image Processing Methods for Bubble Recognition to the Study of Subcooled Flow Boiling of Water in Rectangular Channels

Paz

Conde

Porteiro

et al. 2017

Sensors

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This work introduces the use of machine vision in the massive bubble recognition process, which supports the validation of boiling models involving bubble dynamics, as well as nucleation frequency, active site density and size of the bubbles. The two algorithms presented are meant to be run employing quite standard images of the bubbling process, recorded in general-purpose boiling facilities. The recognition routines are easily adaptable to other facilities if a minimum number of precautions are taken in the setup and in the treatment of the information. Both the side and front projections of subcooled flow-boiling phenomenon over a plain plate are covered. Once all of the intended bubbles have been located in space and time, the proper post-process of the recorded data become capable of tracking each of the recognized bubbles, sketching their trajectories and size evolution, locating the nucleation sites, computing their diameters, and so on. After validating the algorithm’s output against the human eye and data from other researchers, machine vision systems have been demonstrated to be a very valuable option to successfully perform the recognition process, even though the optical analysis of bubbles has not been set as the main goal of the experimental facility.

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