期刊
INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER
卷 105, 期 -, 页码 84-106出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.icheatmasstransfer.2019.03.016
关键词
Nusselt number; Friction factor; Reynolds number; Heat transfer; Pressure drop; Circular; Turbulent; Quasi-turbulent; Transition; Laminar; Smooth tubes; Uncertainty
资金
- NRF from South Africa
- DST
Several well-known correlations to determine the heat transfer coefficients of quasi-turbulent and turbulent flow in smooth tubes are available in literature. However, when these correlations are compared with each other, the results vary over a considerable range. The purpose of this study was therefore to conduct heat transfer and pressure drop experiments in the quasi-turbulent and turbulent flow regimes and to develop an accurate heat transfer correlation that can be combined with recently developed laminar and transitional flow correlations to obtain a single correlation that is valid for all flow regimes. A total of 1180 experimental data points were collected from careful experiments that were conducted ourselves using two different test section configurations. The first test section configuration consisted of a tube-in-tube test section on which the wall temperatures were obtained either indirectly using the Wilson plot method or by direct surface temperature measurements. The second test section configuration consisted of single tubes being electrically heated at a constant heat flux. Different test sections covering a range of tube diameters from 4 mm to 19 mm and a range of tube lengths from 1 m to 9.5 m, were used. Experiments were conducted from a Reynolds number of 2445, which corresponded to the start of the quasi-turbulent flow regime, up to 220,800, which was well into the turbulent flow regime. Water, as well as different concentrations of multi-walled carbon nanotubes, were used as the test fluid, which gave a Prandtl number range of 3-10. A new correlation was developed that could estimate 95% of all the experimental data points within 10% and an average deviation of < 5%. Furthermore, it was able to predict experimental data in literature with a Prandtl number range of 0.47-276 and Reynolds number range of 3000-401,600 with an average deviation of 14%.
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