A modified heat transfer correlation for flow boiling in small channels based on the boundary layer theory

作     者：Zhang, Yang Tan, Hongbo Li, Yanzhong Shan, Siyu Liu, Yuman 

作者机构：Xi An Jiao Tong Univ Sch Energy & Power Engn Xian 710049 Shaanxi Peoples R China State Key Lab Multiphase Flow Power Engn Xian 710049 Shaanxi Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER》 (国际传热与传质杂志)

年 卷 期：2019年第132卷

页      面：107-117页

核心收录：

学科分类：080702[工学-热能工程] 08[工学] 0807[工学-动力工程及工程热物理] 0802[工学-机械工程] 0801[工学-力学（可授工学、理学学位）] 

基　　金：National Key Research and Development Program [2018YFB0904400] 

主　　题：Flow boiling Boundary layer Small channel Hydrocarbon 

摘      要：Flow boiling in small channels has attracted much attention due to its promising application in compact phase change heat exchangers. In this paper, a flow boiling heat transfer database of light hydrocarbons in horizontal small channels is established. It is found that the heat transfer coefficient (HTC) for propane in the channel with 1.0 mm diameter depends on heat and mass fluxes in either the low or high quality region, which is different from the corresponding HTC in conventional channels. To explore the special phenomenon in small channels, a new model is proposed based on boundary layer theory by considering the function of bubble growth in flow boiling. The relationship between the boundary layer thickness and the bubble diameter is emphatically discussed. Based on the theoretical model, a new dimensionless parameter (Rtd) is introduced to evaluate the effect of channel dimension on the flow boiling heat transfer. Rtd reveals that the bubble growth is enhanced in both the low and high quality regions due to the flow confinement of the small channel. With the aid of Rtd, a modified heat transfer correlation is developed based on the database. Within the whole database, the overall standard error and gamma(30) of the modified correlation are 18.9% and 91.3%, respectively. The prediction performance is superior to those of existing correlations. (C) 2018 Elsevier Ltd. All rights reserved.