Abstract
This paper presents a numerical analysis of convective heat transfer enhancement of transverse ribs in circular tubes. Several CFD simulations are carried out for turbulent airflow to analyze heat transfer and pressure drop provided with transverse rectangular ribs. The rib height and pitch are widely varied along with the flow Reynolds number. The effect of each parameter is examined and discussed. To accurately predict major parameters (Nusselt number, friction factor, and thermal hydraulic performance parameter) a deep neural network is developed, trained, and tested by current CFD data. The result demonstrates that artificial neural network shows better performance compared to other methods of prediction (e.g. power-law approximation) and can offer an economical and powerful approach for modeling optimal heat enhancement parameters.
Published Version
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