ANN, numerical and experimental analysis on the jet impingement nanofluids flow and heat transfer characteristics in the micro-channel heat sink. (March 2019)

• Record Type: Journal Article
• Authors: Naphon, P.; Wiriyasart, S.; Arisariyawong, T.; Nakharintr, L.
• Is Part Of: International journal of heat and mass transfer. Volume 131(2019)
• Page Start: 329
• Page End: 340
• Publication Date: 2019-03
• Subjects: ANN -- Jet impingement -- Micro-channel heat sink -- Nanofluids; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Electronic journals; 621.4022
• DOI: 10.1016/j.ijheatmasstransfer.2018.11.073
• Languages: English
• ISSNs: 0017-9310

Abstract

Highlights: Jet impingement, nanofluids and micro-channel heat sink are three type heat transfer enhancement techniques which there are many papers presented the heat transfer enhancement with various techniques. In the present study, the application of computational fluid dynamic and artificial neural network to analyze the nanofluids jet impingement heat transfer and pressure drop in the micro-channel heat sink have been presented. For the ANN model, the Levenberg-Marquardt Backwardpropagation (LMB) training algorithm is applied to adjust errors for obtaining the optimal ANN model. For the numerical analysis, the Eulerian two-phase approach model has been used to analyze the problem. The results obtained from the ANN and CFD are verified with the measured data. Based on the optimal ANN model, the majority of the data falls within ±1.5% of the Nusselt number and pressure drop, respectively. While the maximum error for all cases between the measured data and the predicted results is 1.25%. The obtained optimal artificial neural network model and CFD have been applied to analyze the heat transfer and pressure drop the micro-channel heat sink with various configurations. Abstract: In the present study, the application of computational fluid dynamic and artificial neural network to analyze the nanofluids jet impingement heat transfer and pressure drop in the micro-channel heat sink have been presented. For the ANN model, the Levenberg-Marquardt Backwardpropagation (LMB) training algorithm is applied to adjust errors for obtaining the optimal ANN model. For the numerical analysis, the Eulerian two-phase approach model has been used to analyze the problem. The results obtained from the ANN and CFD are verified with the measured data. Based on the optimal ANN model, the majority of the data falls within ±1.5% of the Nusselt number and pressure drop, respectively. While the maximum error for all cases between the measured data and the predicted results is 1.25%. The obtained optimal artificial neural network model and CFD have been applied to analyze the heat transfer and pressure drop the micro-channel heat sink with various configurations.