The experimental measurement of supercritical pressure carbon dioxide(sCO_(2))heat transfer in vertical downward flow was performed in a circular tube with inner diameter of 10 mm.Then,a three-dimensional numerical in...The experimental measurement of supercritical pressure carbon dioxide(sCO_(2))heat transfer in vertical downward flow was performed in a circular tube with inner diameter of 10 mm.Then,a three-dimensional numerical investigation of sCO_(2)heat transfer in upward and downward flows was performed in a vertical heated circular tube.The influence of heat flux,mass flux,and operating pressure on heat transfer under different flow directions were discussed.According to the"pseudo-phase transition"viewpoint to supercritical fluids,the analogy to the subcritical inverted-annular film boiling model,the physical model to sCO_(2)heat transfer was established,where fluid region at the cross-section of circular tube was divided into gas-like region covering heated wall and core liquid-like phase region.Then,the thermal resistance mechanism which comprehensively reflected the effect of multiple factors including the thickness of the gas-like film or liquid-like region,fluid properties and turbulence on heat diffusion was proposed.Surprisingly,thermal resistance variation in upward flow is well identical with that of wall temperature and heat transfer deterioration is predicted successfully.In addition,compared with thermal resistance in the core liquid-like region,gas-like film formation is determined to be the primary factor affecting heat transfer behavior.Results also show that total thermal resistance in upward flow is always larger than that in downward flow.The investigation can provide valuable guide to design and optimize sCO_(2)heaters.展开更多
Accurate prediction of supercritical CO_(2)(scCO_(2))heat transfer is important for heat exchanger design and safe operation of scCO_(2)power cycles.The main prediction method is empirical correlation.This paper demon...Accurate prediction of supercritical CO_(2)(scCO_(2))heat transfer is important for heat exchanger design and safe operation of scCO_(2)power cycles.The main prediction method is empirical correlation.This paper demonstrates an alternative way by artificial neural networks(ANN)model with two hidden layers.To assess widely cited correlations and newly developed ANN model,scCO_(2)heat transfer experiment in vertical tube with pressure up to 20.8 MPa was performed to extend experiment database,which includes 2674 runs.Compared with empirical correlations,the ANN model is promising for following advantages:(1)ANN model has much better prediction accuracy.The mean relative error,mean absolute relative error and the root-mean-square relative error between predicted and measured wall temperatures are eA=0.38%,eR=4.88%and eS=7.29%,respectively.(2)ANN model performs faster computation speed.(3)ANN model can accurately and speedily predict scCO_(2)heat transfer performance for both normal heat transfer and heat transfer deterioration modes.The trained ANN program is provided with this paper,which is a useful tool and can be directly applied in engineering of scCO_(2)heat transfer.展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2017YFB0601801)the National Natural Science Foundation of China(Grant No.51821004)the Fundamental Research Funds for the Central Universities(Grant No.2018ZD02)。
文摘The experimental measurement of supercritical pressure carbon dioxide(sCO_(2))heat transfer in vertical downward flow was performed in a circular tube with inner diameter of 10 mm.Then,a three-dimensional numerical investigation of sCO_(2)heat transfer in upward and downward flows was performed in a vertical heated circular tube.The influence of heat flux,mass flux,and operating pressure on heat transfer under different flow directions were discussed.According to the"pseudo-phase transition"viewpoint to supercritical fluids,the analogy to the subcritical inverted-annular film boiling model,the physical model to sCO_(2)heat transfer was established,where fluid region at the cross-section of circular tube was divided into gas-like region covering heated wall and core liquid-like phase region.Then,the thermal resistance mechanism which comprehensively reflected the effect of multiple factors including the thickness of the gas-like film or liquid-like region,fluid properties and turbulence on heat diffusion was proposed.Surprisingly,thermal resistance variation in upward flow is well identical with that of wall temperature and heat transfer deterioration is predicted successfully.In addition,compared with thermal resistance in the core liquid-like region,gas-like film formation is determined to be the primary factor affecting heat transfer behavior.Results also show that total thermal resistance in upward flow is always larger than that in downward flow.The investigation can provide valuable guide to design and optimize sCO_(2)heaters.
基金The study was supported by the National Key R&D Program of China(2017YFB0601801)the National Natural Science Foundation of China(51806065)Fundamental Research Funds for Central Universities(2020DF002).
文摘Accurate prediction of supercritical CO_(2)(scCO_(2))heat transfer is important for heat exchanger design and safe operation of scCO_(2)power cycles.The main prediction method is empirical correlation.This paper demonstrates an alternative way by artificial neural networks(ANN)model with two hidden layers.To assess widely cited correlations and newly developed ANN model,scCO_(2)heat transfer experiment in vertical tube with pressure up to 20.8 MPa was performed to extend experiment database,which includes 2674 runs.Compared with empirical correlations,the ANN model is promising for following advantages:(1)ANN model has much better prediction accuracy.The mean relative error,mean absolute relative error and the root-mean-square relative error between predicted and measured wall temperatures are eA=0.38%,eR=4.88%and eS=7.29%,respectively.(2)ANN model performs faster computation speed.(3)ANN model can accurately and speedily predict scCO_(2)heat transfer performance for both normal heat transfer and heat transfer deterioration modes.The trained ANN program is provided with this paper,which is a useful tool and can be directly applied in engineering of scCO_(2)heat transfer.
