scholarly journals Numerical Simulation of a Radial Free Surface Liquid Jet Impinging on a Heated Surface

Author(s):  
Alok Khaware ◽  
Likitha S. Siddanathi ◽  
Patrick Sharkey ◽  
Amine Ben Hadj Ali ◽  
Vinay K. Gupta
2002 ◽  
Vol 45 (2) ◽  
pp. 307-314 ◽  
Author(s):  
Yaohua ZHAO ◽  
Takashi MASUOKA ◽  
Takaharu TSURUTA ◽  
Chong-Fang MA

2011 ◽  
Vol 23 (5) ◽  
pp. 052104 ◽  
Author(s):  
D. Maynes ◽  
M. Johnson ◽  
B. W. Webb

2011 ◽  
Vol 133 (6) ◽  
Author(s):  
Avijit Bhunia ◽  
C. L. Chen

Liquid microjet arrays have received a lot of research attention in recent years due to its high heat flux cooling capability. The microjets are generated from a jet head cavity with a liquid inlet port on one wall and an array of micro-orifices on another wall. An important, yet relatively less studied aspect of the topic is the pressure (also frequently referred to as the pressure drop) necessary to generate the jets and maintain certain jet velocity. In this study we investigate the pressure drop for17 different array patterns of liquid jet issuing in a surrounding gas (air) medium, i.e., a free surface liquid jet. The number of jets varies from 1 to 126, while the jet diameter ranges from 99 to 208 μm. The current results show more than 200% deviation from the existing correlations in the literature. Through a systematic experimental study we identify the functional dependence of pressure drop on the various geometric parameters. The results uncover the reasons behind the widespread disagreement between the current data and the existing correlations. Pressure drop shows a weak, nonlinear dependence on the orifice wall thickness, compared to the linear dependence used in the existing correlations. Furthermore, the depth of the jet head cavity is shown to be an important parameter dictating pressure drop, unlike the previous studies that inherently assume the cavity to be an infinite reservoir. A new dimensionless pressure drop parameter is proposed and its variation with the jet Reynolds number is correlated. The new correlation predicts all the experimental data within a ± 10% range.


2020 ◽  
Vol 154 ◽  
pp. 106389 ◽  
Author(s):  
Kuldeep Baghel ◽  
Arunkumar Sridharan ◽  
Janani Srree Murallidharan

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