New flow condensation heat transfer model for methane and ethane

Methane and ethane are the main components in liquefied natural gas (LNG) and they belong to the group of natural refrigerants with high thermodynamic performances. In addition, methane and ethane are also the main components in mixture Joule-Thomson low temperature refrigerators (MJTR). Thus, the intensive study on flow condensation heat transfer of methane and ethane is necessary for design and optimization of LNG industrial facilities and the low-temperature Joule-Thomson refrigerator. Based on the optimal selection of published models and the comparison with experimental data of methane and ethane, a new calculation model to predict condensation heat transfer coefficient based on flow patterns is presented. Furthermore, the comparisons of the new model and other models in literature were made to evaluate its accuracy in heat transfer prediction. INTRODUCTION Hydrocarbons are good substitute refrigerants because they have high thermodynamic performances in refrigeration cycles, and they have zero ozone depletion potential (ODP = 0) and insignificant direct global warming potential. Besides, methane and ethane are the main components in nature gas and important components of mixture refrigerants in mixture Joule-Thomson low temperature refrigerators (MJTR) [1], especially in the application area of cryogenic temperature zone (80-230 K). The flow condensation heat transfer data of methane and ethane are necessary for the designing and optimization of related heat exchangers. However, it is very difficult to find such measured flow condensation heat transfer data in open published literature, and the reliability of classic heat transfer correlations has not been confirmed when applied for methane and ethane, although the industrial applications of MJTR and LNG has been developed for several decades. NOMENCLATURE D [m] Inner diameter Frl [-] Liquid Froude number Frl=[G(1-x)] /(ρl gD) fl,i [-] Interfacial roughness g [m s] Gravitational acceleration G [kg m s] Mass flux Ga [-] Galileo number Ga=gρl (ρl-ρv)D /μl 2 h [W m K] Heat transfer coefficient p [MPa] Saturation pressure Hlv [J kg ] Latent heat Jal [-] Liquid Jakob number Jal=cp,l(Tsat-Tw)/Hlv Nu [-] Nusselt number Prl [-] Liquid Prandtl number Prl=μlcp,l/λl Rel [-] Liquid Reynolds number Rel=G(1-x)D/μl Revo [-] Vapor only Reynolds number Revo=GD/μv T [K] Temperature Ul [m s ] Liquid velocity Ul =G(1-x)/[ρl(1-ε)] Uv [m s ] Vapor velocity Uv =Gx/(ρvε) x [-] Vapor quality X [-] Lockhart-Martinelli parameter, X=[(1-x)/x](μl/μv)(ρv/ρl) We* [-] Modified Weber number by Soliman (1986) [2] Special characters δ [m] Film thickness δ= D/2(1-ε0.5) ε [-] Void fraction η [-] Percentage of points predicted within a Deviation bandwidth of ±30% θl [-] Angle subtended from the top of tube to the Liquid level λ [W m K] Thermal conductivity μ [Pa s] Dynamic viscosity ρ [kg m] Density σ [N m] Surface tension φl [-] Two-phase pressure drop multiplier Subscripts A Annular flow cal Calculated value exp Experimental value film Filmwise condensation forced Force-convective condensation l Liquid phase NA Non-annular flow sat Saturation state v Vapor phase w Wall The heat transfer coefficient in flow condensation is influenced by flow patterns. Recently, great number of flow condensation heat transfer models have been presented in the past decades. Ten correlations in literature were chosen in present work to make comparison with experimental data. It is a fact that many of them have good predicting result in special condition area and for special working fluid, but no model shows a good universal application for most substances in 13th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics