Thermo-hydraulic performance prediction for offset-strip fin heat exchangers using artificial neural networks

Autor: Kim, Sangmin, Seo, Young Min, Yoon, Sang Youl, Kim, Seokho, Pandey, Sudhanshu, Ha, Man Yeong
Rok vydání: 2023
Předmět:
Zdroj: Kim, S, Seo, Y M, Yoon, S Y, Kim, S, Pandey, S & Ha, M Y 2023, ' Thermo-hydraulic performance prediction for offset-strip fin heat exchangers using artificial neural networks ', Journal of Mechanical Science and Technology, vol. 37, no. 5, pp. 2623-2638 . https://doi.org/10.1007/s12206-023-0436-3
ISSN: 1976-3824
1738-494X
Popis: In this study, an artificial neural network (ANN) model was applied to predict the thermo-hydraulic performance of the offset-strip fin heat exchanger. The ANN model was used for predicting the thermo-hydraulic performance to improve the prediction accuracy and extend the applicable range compared to the results of using the empirical equations reported in previous studies. The main parameters of these empirical equations were fin height (0.134 < α < 0.997), fin length (0.012 < δ < 0.048), fin width (0.041 < γ < 0.121), and Reynolds number (120 < Re 4). In addition, the Fanning friction factor f and the Colburn factor j were considered as the outputs of the ANN model. The impact of the parameters on the thermo-hydraulic performance of the heat exchanger was quantitatively evaluated, and the prediction accuracy was improved over a wide range for the thermo-hydraulic performance generated by the ANN model. Thus, the results obtained using the ANN model agreed well with the experimental data over a wider range than possible for the previous empirical correlations, showing extremely high accuracy and validity of the ANN model in comparison to the empirical equations.
Databáze: OpenAIRE
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