期刊
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
卷 144, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijheatmasstransfer.2019.118599
关键词
Forced convection condensation; Two-phase flow; R134a; Conical pin fins; Selective laser melting
资金
- National Research Foundation, Singapore
- Prime Minister's Office, Singapore
This paper investigates the forced convection condensation heat transfer performance of R134a in circular tubes with three-dimensional conical pin fin structures. Five conical pin fin tubes of different circumferential fin pitch (p(c)) and longitudinal fin pitch (p(t)) were fabricated by Selective Laser Melting (SLM) with the aim of enhancing the internal forced convection condensation of R134a. Experiments were performed to characterize the condensation heat transfer coefficients (h(ref)) and pressure drops (Delta P) across these enhanced tubes. These experiments were conducted at the refrigerant mass fluxes (m(ref)) of 50 kg/m(2).s to 200 kg/m(2).s, average vapor qualities (x(ave)) from 0.2 to 0.8 and saturation pressure (P-sat) of 13.4 bar. The effects of x(ave), m(ref,) p(c) and (p)i on h(ref)( )and Delta(P) were determined and the results were compared against a commercial Al tube, a plain tube fabricated by SLM and two SLM fabricated enhanced tubes with domeshaped fins. It was found that h ref of the conical pin fin tubes increases with increasing x(ave), and m(ref) and these values are also significantly higher than those of the plain tubes. Both p(i) and p(c) were found to significantly affect the h(ref) values of the conical pin fin tubes whereas the Delta(P) values were affected only by the change in p(c). An efficiency index (eta(1)) is defined to evaluate the thermal-hydraulic performances of the enhanced tubes. The experimental results show that all the conical pin fin tubes demonstrated higher eta(1) than the dome-shaped fin tubes. Based on the boundary layer approach, a semi-empirical model is developed to predict the Nusselt numbers of the conical pin fin tubes. Reasonably accurate predictions were achieved with an overall mean absolute error (MAE) of 10.5%. (C) 2019 Elsevier Ltd. All rights reserved.
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