Artificial neural network analysis the pulsating Nusselt number and friction factor of TiO2/water nanofluids in the spirally coiled tube with magnetic field. (March 2018) - Naphon, P.; Wiriyasart, S.; Arisariyawong, T.
Highlights: Heat transfer enhancement techniques are applied to improve the heat exchanger devices which the spirally fluted tube has been used as one of passive heat transfer enhancement techniques to facilitate the desire flow modification for augmenting heat transfer. The spirally fluted tubes are the most widely used tubes in several heat transfer applications. There are many papers presented the thermal devices analysis by using artificial neural network. However, there is not paper concerned the heat transfer characteristics and friction factor of the spirally coiled tube. The objective of this paper is to analyze and predict the pulsating nanofluids heat transfer coefficient and friction factor of the spirally coiled tube with magnetic field effect using artificial neural model. This paper is continuously performed of Naphon et al. [34], which it analyze the Nusselt number and friction factor of nanofluids pulsating flow in the spirally coiled tube with the presence magnetic field effect of 4, 6 magnet bars using artificial neural network. Four different training algorithms of Levenberg-Marquardt Backwardpropagation (LMB), Scaled Conjugate Gradient Backpropagation (SCGB), Bayesian Regulation Backpropagation (BRB), and Resilient Backpropagation (RB) are applied to adjust errors for obtaining the optimal ANN model. The optimal ANN approach has been applied to show its capability in the representation of thermal performance of the spirally coiled tube with magnetic field. Abstract: The application of artificial neural network to analyze the pulsating nanofluids heat transfer and pressure drop in the spirally coiled tube with magnetic field are presented. Four different training algorithms of Levenberg-Marquardt Backwardpropagation (LMB), Scaled Conjugate Gradient Backpropagation (SCGB), Bayesian Regulation Backpropagation (BRB), and Resilient Backpropagation (RB) are applied to adjust errors for obtaining the optimal ANN model. The results obtained from the artificial neural network are compared those from the present experiment. It is found that the Levenberg- Marquardt Backpropagation algorithm gives the minimum MSE, and maximum R as compared with other training algorithms. Based on the optimal ANN model, the majority of the data falls within ± 2.5%, ± 5% of the Nusselt number and friction factor, respectively. The obtained optimal ANN has been applied to predict the thermal performance of the spirally coiled tube with magnetic field.