Heat Transfer Prediction of Supercritical Carbon Dioxide in Vertical Tube Based on Artificial Neural Networks

Accurate prediction of supercritical CO2 (scCO2) heat transfer is important for heat exchanger design and safe operation of scCO2 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, scCO2 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 scCO2 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 scCO2 heat transfer.

[1]  Bengt Sundén,et al.  A brief review on convection heat transfer of fluids at supercritical pressures in tubes and the recent progress , 2016 .

[2]  Lin Jiang,et al.  Comparative evaluation of the predictability of neural network methods on the flammability characteristics of extruded polystyrene from microscale combustion calorimetry , 2019, Journal of Thermal Analysis and Calorimetry.

[3]  Yingru Zhao,et al.  Modeling convective heat transfer of supercritical carbon dioxide using an artificial neural network , 2019, Applied Thermal Engineering.

[4]  Igor Pioro,et al.  Experimental heat transfer in supercritical water flowing inside channels (survey) , 2005 .

[5]  Walter Ambrosini,et al.  RESULTS OF 4-EQUATION TURBULENCE MODELS IN THE PREDICTION OF HEAT TRANSFER TO SUPERCRITICAL PRESSURE FLUIDS , 2014 .

[6]  V. Gnielinski.  New equations for heat and mass transfer in turbulent pipe and channel flow , 1976 .

[7]  Wargha Peiman,et al.  Development of supercritical water heat-transfer correlation for vertical bare tubes , 2011 .

[8]  Sandra K. S. Boetcher,et al.  A review of heat transfer deterioration of supercritical carbon dioxide flowing in vertical tubes: Heat transfer behaviors, identification methods, critical heat fluxes, and heat transfer correlations , 2020 .

[9]  Jun Zhang,et al.  Experimental study on convection heat transfer of supercritical CO2 in small upward channels , 2019, Energy.

[10]  G. Ruocco,et al.  The Widom line as the crossover between liquid-like and gas-like behaviour in supercritical fluids , 2010 .

[11]  Jinliang Xu,et al.  Life Cycle Assessment Analysis and Comparison of 1000 MW S-CO2 Coal Fired Power Plant and 1000 MW USC Water-Steam Coal-Fired Power Plant , 2020, Journal of Thermal Science.

[12]  P. Jiang,et al.  Experimental investigation of convection heat transfer of CO2 at supercritical pressures in a vertical circular tube , 2010 .

[13]  Shuisheng He,et al.  Assessment of performance of turbulence models in predicting supercritical pressure heat transfer in a vertical tube , 2008 .

[14]  Wei Li,et al.  A brief review on the buoyancy criteria for supercritical fluids , 2018 .

[15]  Jinliang Xu,et al.  Key issues and solution strategies for supercritical carbon dioxide coal fired power plant , 2018, Energy.

[16]  Ronald Davis,et al.  Neural networks and deep learning , 2017 .

[17]  J. Yoo,et al.  Direct numerical simulation of turbulent supercritical flows with heat transfer , 2005 .

[18]  Oisik Das,et al.  Application of Adaptive Neuro-Fuzzy Inference System in Flammability Parameter Prediction , 2020, Polymers.

[19]  Y. Bae,et al.  Assessment of low-Reynolds number k-ε turbulence models against highly buoyant flows , 2017 .

[20]  Wei-Mon Yan,et al.  Measurement and Artificial Neural Network Modeling of Electrical Conductivity of CuO/Glycerol Nanofluids at Various Thermal and Concentration Conditions , 2018 .

[21]  Jinliang Xu,et al.  Critical supercritical-boiling-number to determine the onset of heat transfer deterioration for supercritical fluids , 2020 .

[22]  Jizhen Liu,et al.  Perspective of S−CO2 power cycles , 2019, Energy.

[23]  Jinliang Xu,et al.  The general supercritical heat transfer correlation for vertical up-flow tubes: K number correlation , 2020 .

[24]  Kaiqi Jiang.  An Experimental Facility for Studying Heat Transfer in Supercritical Fluids , 2015 .

[25]  Huixiong Li,et al.  Special heat transfer characteristics of supercritical CO2 flowing in a vertically-upward tube with low mass flux , 2018, International Journal of Heat and Mass Transfer.

[26]  Yan-ping Huang,et al.  Improvement of buoyancy and acceleration parameters for forced and mixed convective heat transfer to supercritical fluids flowing in vertical tubes , 2017 .

[27]  Ž. Knez,et al.  Industrial applications of supercritical fluids: A review , 2014 .

[28]  P. Gallo,et al.  Widom line and dynamical crossovers as routes to understand supercritical water , 2014, Nature Communications.

[29]  G. Tang,et al.  Correlation evaluation on circumferentially average heat transfer for supercritical carbon dioxide in non-uniform heating vertical tubes , 2019, Energy.

[30]  Ravinder Kumar,et al.  A Simulation Model to Predict Coal-Fired Power Plant Production Rate Using Artificial Neural Network Tool , 2020 .

[31]  L. Piazza,et al.  Analysis of forced convection heat transfer to supercritical carbon dioxide inside tubes using neural networks , 2003 .

[32]  Jinliang Xu,et al.  Connected-top-bottom-cycle to cascade utilize flue gas heat for supercritical carbon dioxide coal fired power plant , 2018, Energy Conversion and Management.

[33]  Saeed Zeinali Heris,et al.  Prediction of the pressure drop for CuO/(Ethylene glycol-water) nanofluid flows in the car radiator by means of Artificial Neural Networks analysis integrated with genetic algorithm , 2020 .

[34]  Peiwen Li,et al.  A systematic comparison of different S-CO2 Brayton cycle layouts based on multi-objective optimization for applications in solar power tower plants , 2018 .

[35]  M. Farid,et al.  Supercritical CO2 as heat transfer fluid: A review , 2017 .

[36]  Polikhronidi Nikolai,et al.  Supercritical CO2: Properties and Technological Applications - A Review , 2019, Journal of Thermal Science.

[37]  Dan Wu,et al.  Optimization of heat transfer coefficient correlation at supercritical pressure using genetic algorithms , 2009 .

[38]  Dong Eok Kim,et al.  Experimental investigation of heat transfer in vertical upward and downward supercritical CO2 flow in a circular tube , 2011 .

[39]  R. Winterton,et al.  Technical notes: where did the Dittus and Boelter equation come from , 1998 .