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چکیده
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The heat transfer characteristics of carbon dioxide (R744) as a natural and safe refrigerant are quite different from the other refrigerants due to the CO2 unique physical properties. So, it is essential to develop accurate general models for predicting the heat transfer coefficient (HTC) of CO2 condensation inside smooth tubes. In this study, an extensive experimental databank for the CO2 refrigerant including 1,016 data samples was gathered from 13 published studies, covering a broad range of operating conditions and channel sizes. Ten earlier well-known HTC models for other refrigerants showed high deviations (average absolute relative deviations (AARDs) higher than 49%). To overcome this research gap in the literature, the machine learning methods, namely, multilayer perceptron (MLP), interpolating radial basis function (RBF), and Gaussian process regression (GPR), were used for developing the predictive models for two-phase Nusselt number. The GPR model showed the best outcomes among the studied models with AARD values of 1.5% and 7.32% for training and testing data points, respectively. In addition, a new general HTC correlation for condensation of CO2 was proposed by considering two surface tensions ranges, which showed good accuracy with an AARD of 17.61% for the entire database. It was found that the developed models are reliable for designing heat exchangers with different channel sizes and various flow patterns. Finally, a sensitivity analysis using the most accurate GPR model showed that the channel diameter is the most critical factor in controlling the HTC of CO2 when the Bond number is higher than 0.5.
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