Modeling convective heat transfer of supercritical carbon dioxide using an artificial neural network

Abstract Supercritical carbon dioxide (sCO2) is an ideal working fluid for an energy conversion system, which can be used in a wide variety of power-generation applications with increased efficiency. However, abrupt variations in its thermo-physical properties in pseudo-critical region make it difficult to accurately predict the heat transfer characteristics of sCO2 using traditional analyzing methods, which to some extent hinders the development of sCO2 power conversion technology. Therefore, it is critical to explore feasible approaches to accurately predict the characteristics of sCO2 heat transfer. In this study, the performance of representative empirical correlations of sCO2 heat transfer has been assessed with a databank collected from previous publications. The results of the assessment indicate that the existing correlations are not effective enough to describe sCO2 heat transfer, especially when it is in the pseudo-critical region. An artificial neural network (ANN) is therefore proposed to model sCO2 heat transfer with experimental datasets. The ANN model shows a great learning ability and satisfactory generalization performance with a correlation coefficient of 0.99 and a mean absolute percent error of 0.97% in the test dataset. The results show that the proposed ANN model is a more effective and efficient method to predict the heat transfer characteristics of sCO2 than empirical correlations. The feasibility of the ANN model in the prediction of heat transfer with significant buoyancy force or flow acceleration are also tested and discussed.

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