个人信息Personal Information
教授
博士生导师
硕士生导师
性别:女
毕业院校:大连理工大学
学位:硕士
所在单位:能源与动力学院
办公地点:能源与动力学院718室
联系方式:13084159090
电子邮箱:lisuf@dlut.edu.cn
Numerical investigation on convective heat transfer to aviation kerosene flowing in vertical tubes at supercritical pressures
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论文类型:期刊论文
发表时间:2018-03-01
发表刊物:INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
收录刊物:SCIE、EI
卷号:118
页面范围:857-871
ISSN号:0017-9310
关键字:Supercritical pressure heat transfer; Aviation kerosene; Turbulence model; Buoyancy effect; Turbulent Prandtl number
摘要:Numerical simulations of convective heat transfer to aviation kerosene (China RP-3) flowing in vertical circular tubes at supercritical pressures are reported in this study. Firstly, performance of a variety of Reynolds-Averaged Navier-Stokes turbulence models in predicting the fluid-thermal behaviours under both forced and mixed convection conditions are evaluated. Under forced convection conditions, all models predict a gentler growth of wall temperature along the flow direction than experimental measurements. Under mixed convection conditions, the effect of buoyancy become significant and there are large discrepancies in the predicted wall temperature by different models. Only the low-Reynolds number k-a models are found to be able to qualitatively predict the flow laminarization and heat transfer deterioration. Profiles of thermal, flow and turbulence fields obtained using various models are studied to explain the differences in predictions. For mixed convection conditions, an examination on the turbulence production due to shear and density fluctuation indicates that the direct effect of buoyancy on the turbulence production is negligible compared with the indirect effect. Furthermore, the effect of turbulent Prandtl number on the predicted heat transfer is studied. It is found that turbulent Prandtl number has a significant influence on the simulation results. Under the conditions considered in the present study, the value of 1.0 for turbulent Prandtl number leads to a closest agreement with the experimental data. (C) 2017 Elsevier Ltd. All rights reserved.