The Flow Boiling Heat Transfer Characteristics Of The Binary Non-azeotropic Mixture Working Fluid In The Tube

Due to the complexity of fluid flow,heat and mass transfer methods in multiphase flow,the related theoretical research is scarce.At present,the multi-phase flow boiling heat transfer process has become an important subject in the field of multiphase flow.By studying the flow boiling heat transfer characteristics of binary non-azeotropic mixtures in horizontal pipes,it can provide theoretical basis for the future development of alternative refrigerants and corresponding evaporators.In this paper,numerical simulation,neural network prediction analysis and the correlation calculation correlation are carried out for the binary non-azeotropic mixture of R123/R245fa(0.1/0.9)in horizontal pipes and micro-fin tube.The main conclusions are as follows:Firstly,the working conditions are represented by the mass flow rate of 300 kg/(m~2?s),the heat flux of 30 kW/m~2 and the evaporation temperature of 40°C.The mixture model for multiphase flow,the RNG k-?turbulent model and the component transport model were used to simulate the flow boiling heat transfer of refrigerant mixture.The results showed that:When the tube wall is heated by a constant heat flux density,the temperature of the working fluid in the smooth tube and micro-fin tube is mainly concentrated around 315K during the whole heat exchange process.Along the tube length,the temperature of the upper wall surface,the volume fraction of gas phase,and the flow velocity are gradually increased.Along the pipe diameter,the wall temperature above the pipe is higher than the mainstream temperature.The closer to the pipe wall,the greater the temperature gradient is,the higher the gas volume fraction is,and the flow velocity is.Since the frictional drag along the pipe wall is greater than that inside the pipe,the flow velocity near the pipe wall is less than the flow velocity in the pipe.Secondly,the influence of the mass flow rate,the heat flux and the evaporation temperature on the heat transfer performance in the flow of the working fluid was analyzed.The results showed that:When the heat flux and evaporating temperature are kept constant,the mass flow rates in the smooth tube are 265 kg/(m~2?s),300 kg/(m~2?s)and 375 kg/(m~2?s),with the increase of mass flow rate,heat transfer coefficient is increased.mass flow rates are 275 kg/(m~2?s),300 kg/(m~2?s)and350 kg/(m~2?s)in micro-fin tubes,with the increase of mass flow rate,heat transfer coefficient is increased in the low-dryness area.In the high-dryness zone,the heat transfer coefficient of 275kg/(m~2·s)fluctuates.The heat transfer coefficients of 300 kg/(m~2·s)and 350 kg/(m~2·s)still increase with the increase of the mass flow rate.When the mass flow rate and evaporating temperature are kept constant,the heat flux are 25kW/m~2,30kW/m~2 and 40kW/m~2,in the smooth tube,the heat transfer coefficient increases with the increase of heat flux in the low dryness area but decreases gradually in the high dryness area.In the micro-fin tube,the heat transfer coefficient gradually decreases with the increase of the heat flux density in the low dryness area.In the high dryness area,the heat transfer coefficient of 20 kW/m~2 fluctuates,while the heat transfer coefficient of 30kW/m~2 and 40 kW/m~2 increases with the increase of heat flux.When the mass flow rate and heat flux are kept constant,the heat transfer coefficient decreases gradually with the increase of evaporation temperature in the smooth tube and micro-fin tube.Finally,the RBF neural network prediction model of non-azeotropic refrigerant R123/R245fa(0.1/0.9)flow boiling in the horizontal smooth tube is established using the simulated data as sample data,and the network model prediction results is compared with the numerical simulation results and analyze the calculation accuracy of the five traditional correlations.The results showed that:After 140 and 104 iterations training,the set target error of 0.00001 is achieved.The mean square error of the training was 0.9427%and 1.1301%,respectively.The average error was 0.0096%and 0.0155%,respectively.And the absolute error was 0.5875%and 0.7623%,respectively.The mean square error of the prediction was 11.4294%and 1.3784%,respectively.The average error was-3.1468%and-0.1393%,respectively.And the absolute error was 4.3242%and 0.9168%,respectively.When the mass flow rate,evaporation temperature,and heat flux change,respectively,heat transfer coefficient obtained by RBF neural network is consistent with the change trend of the heat transfer coefficient obtained by numerical simulation and has a good goodness of fit.In the low-quality region,Chen's formula is similar to the RBF prediction value.In the high-dry region,the Kandlikar formula is similar to the RBF prediction value.For the whole heat transfer process,the error of Kandlikar formula is smaller than the Chen formula.