Comparison and Prediction of the Experimental Data for Thermal Efficiency of a Double-Pipe Heat Exchanger with Fe3O4 Nanofluid Using Artificial Neural Networks

Subject Areas : Heat Transfer

محمد  اختری ¹ (موسسه غیر انتفاعی فخر رازی)
مجتبی  میرزایی ² (موسسه غیر انتفائی انرژی)
داریوش  خسروی مهد ³ (کارمند موسسه انرژی)

Received: 2017-12-30 Accepted : 2018-02-10 Published : 2018-05-05

Keywords: Artificial Neural Network, heat transfer, Double Pipe heat exchangers,

Abstract :

In this study, the thermal efficiency of a double-pipe heat exchanger with Fe3O4-water nanofluid in Reynolds numbers between 2000-21000 and volume fractions between (0.1-0.4% v / v) using artificial neural networks and correlation with experimental data has been evaluated and predicted. Iron oxide nanoparticles were about 20 nm in size. SEM photography of nanoparticles is provided to show the stability and homogeneity of suspension. Different Reynolds numbers and volume fractions of iron oxide nanofluid are used as the training data for ANN. A two-layer feed-forward neural network with back-propagation Levenberg-Marquardt learning algorithm (BP-LM) was used for heat transfer pre-parameters. Moreover, 70% of data were used in training set and 15% of data were used in evaluation set and remaining data were used as test data to prevent preprocess of network and to study the final efficacy of the network. In addition, based on the experimental data and the use of artificial neural network, data predicted by the neural network are in good agreement with experimental data measured by the double-pipe heat exchanger. The overall verification by the mean squared error (MSE) and correlation coefficient (R2) for the thermal efficiency of a double-pipe heat exchanger is 0.0001 and 0.996, respectively, indicating that prediction is successful.