Numerical Simulation Study On Flow And Heat Transfer Characteristics Of Twisted Helical Tube

In order to meet the demands of miniaturization and integration of reactors,the volume and structural design of steam generators need to be more compact and have higher heat transfer efficiency.Improving the heat transfer efficiency of the heat transfer tubes and reducing the resistance caused by the flow of the working fluid in the pipes and equipment is particularly important for the miniaturization of reactors.The geometric structure of twisted helical tubes can better improve the turbulence degree of fluid flow and thus improve its flow and heat transfer performance.In this study,numerical simulation was conducted using computational fluid dynamics software to investigate the boiling heat transfer in twisted helical tubes and the convective heat transfer in the tube bundle region,and was compared with traditional helical tubes.The flow and heat transfer characteristics of two-phase flow in the tube and single-phase flow in the tube bundle region under different structural parameters were obtained.Finally,the Nusselt number and friction factor of the tube bundle region were predicted by using the neural network method.Firstly,based on the Euler two-fluid model,considering the mass,momentum and energy transfer between the two phases and coupled with the wall boiling model,a numerical analysis model of boiling heat transfer was established for the flow boiling phenomenon inside twisted helical tubes.The model was validated by flow boiling experiments inside helical tubes.Then,the geometric models of helical tubes and twisted helical tubes with different structural parameters were established to explore the differences in flow boiling heat transfer characteristics.The results showed that the flow inside the twisted helical tube was more complex,and its heat transfer performance was better than that of the traditional helical tube,especially in the boiling region.The structural parameters affected the axial and circumferential temperature,vapor fraction and heat transfer coefficient.The aspect ratio(A/B)mainly affected the heat transfer in the boiling region,and the heat transfer coefficient increased with the increase of A/B.The helix radius(R)mainly affected the heat transfer in the single-phase region,and the heat transfer coefficient decreased with the increase of R.The twisting period(N)had the weakest effect on heat transfer,and the heat transfer coefficient slightly increased with the increase of N.Secondly,the feasibility of the numerical simulation method for flow and heat transfer in the tube bundle region was verified by using twisted tube bundles for convective heat transfer experiments.Then,the geometric models of helical tube bundles and twisted helical tube bundles with different structural parameters were established to compare and analyze their flow and heat transfer characteristics,and to explore the influence of structural parameters on the flow and heat transfer characteristics in the tube bundle region.The results showed that the flow inside the twisted helical tube bundle was more complex,and the flow of fluid in the transverse direction was caused by the spiral twist of the tube itself when it swept across the tube bundle region.The Nusselt number(Nu)and friction factor(Eu)in the twisted helical tube bundle region were both smaller than those in the helical tube bundle region,and the overall heat transfer enhancement factor was greater than 1,which increased with the decrease of pitch and transverse tube spacing.The trends of Nu and Eu in the twisted helical tube bundle region were the same,increasing with the increase of pitch(P),transverse tube spacing(St)and twisting period(N),and decreasing with the increase of aspect ratio(A/B).Finally,a BP neural network model for the Nu and Eu of twisted helical tube bundle with complex structural characteristics was constructed based on machine learning methods,using the structural parameters as the input features and the Nu and Eu of the tube bundle region as the target values.The research results show that the predicted values of the neural network model are in good agreement with the actual values,with errors in the Nusselt number within5% and the friction factor within 10% of the actual values.The model can provide reliable support for subsequent studies of twisted helical tubes.