Abstract

AbstractA set of physical matching conditions at the liquid–vapor interface are considered and rigorously satisfied for getting reliable solutions related to the three-point boundary value problem on the laminar free convection film condensation of vapor–gas mixture. With the example on the laminar free convection film condensation of water vapor–air mixture, a system of the interfacial vapor saturation temperature \(T_{{s},\mathrm{int}}\) is found out, which only depends on the bulk vapor mass fraction for a special bulk temperature. The numerical solutions of the interfacial vapor saturation temperature \(T_{{s},\mathrm{int}}\) are further formulated into an equation for its reliable prediction. A system of rigorous numerical results is successfully obtained, including velocity and temperature fields of the condensate liquid film, as well as the velocity, temperature, and concentration fields of the vapor–gas mixture film. With increasing the vapor mass fraction (or decreasing the gas mass fraction) in the bulk, the condensate liquid film thickness, the condensate liquid velocity, and vapor–gas mixture velocity at the liquid–vapor interface will increase at an accelerative pace. It proved that the noncondensable gas in the vapor–gas mixture has a decisive effect on the laminar free convection film condensation from vapor–gas mixture. The wall temperature has also a decisive effect on the laminar free convection film condensation from vapor–gas mixture. With increasing wall temperature, the condensate liquid film thickness, the condensate liquid velocity, as well as velocity of the vapor–gas mixture at the liquid–vapor interface will decrease. However, with increasing the wall temperature, the thicknesses of the momentum, temperature, and concentration boundary layers of the vapor–gas mixture will increase.KeywordsWall TemperatureFilm CondensationConcentration Boundary LayerCondensate LiquidInterfacial Boundary ConditionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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