Date of download: 10/10/2017 Copyright © ASME. All rights reserved.

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Date of download: 10/10/2017 Copyright © ASME. All rights reserved. From: Exergy Prediction Model of a Double Pipe Heat Exchanger Using Metal Oxide Nanofluids and Twisted Tape Based on the Artificial Neural Network Approach and Experimental Results J. Heat Transfer. 2015;138(1):011801-011801-10. doi:10.1115/1.4031073 Figure Legend: Schematic diagram of the experimental setup

Date of download: 10/10/2017 Copyright © ASME. All rights reserved. From: Exergy Prediction Model of a Double Pipe Heat Exchanger Using Metal Oxide Nanofluids and Twisted Tape Based on the Artificial Neural Network Approach and Experimental Results J. Heat Transfer. 2015;138(1):011801-011801-10. doi:10.1115/1.4031073 Figure Legend: Validation of plain tube experimental data for Nusselt number

Date of download: 10/10/2017 Copyright © ASME. All rights reserved. From: Exergy Prediction Model of a Double Pipe Heat Exchanger Using Metal Oxide Nanofluids and Twisted Tape Based on the Artificial Neural Network Approach and Experimental Results J. Heat Transfer. 2015;138(1):011801-011801-10. doi:10.1115/1.4031073 Figure Legend: Validation of plain tube with TTs and water for friction factor

Date of download: 10/10/2017 Copyright © ASME. All rights reserved. From: Exergy Prediction Model of a Double Pipe Heat Exchanger Using Metal Oxide Nanofluids and Twisted Tape Based on the Artificial Neural Network Approach and Experimental Results J. Heat Transfer. 2015;138(1):011801-011801-10. doi:10.1115/1.4031073 Figure Legend: Validation of plain tube with TTs and water for Nusselt number

Date of download: 10/10/2017 Copyright © ASME. All rights reserved. From: Exergy Prediction Model of a Double Pipe Heat Exchanger Using Metal Oxide Nanofluids and Twisted Tape Based on the Artificial Neural Network Approach and Experimental Results J. Heat Transfer. 2015;138(1):011801-011801-10. doi:10.1115/1.4031073 Figure Legend: Validation of plain tube with TTs and water for friction factor

Date of download: 10/10/2017 Copyright © ASME. All rights reserved. From: Exergy Prediction Model of a Double Pipe Heat Exchanger Using Metal Oxide Nanofluids and Twisted Tape Based on the Artificial Neural Network Approach and Experimental Results J. Heat Transfer. 2015;138(1):011801-011801-10. doi:10.1115/1.4031073 Figure Legend: Rational efficiency as a function of Reynolds number

Date of download: 10/10/2017 Copyright © ASME. All rights reserved. From: Exergy Prediction Model of a Double Pipe Heat Exchanger Using Metal Oxide Nanofluids and Twisted Tape Based on the Artificial Neural Network Approach and Experimental Results J. Heat Transfer. 2015;138(1):011801-011801-10. doi:10.1115/1.4031073 Figure Legend: Comparison of different nanoparticles effect on exergetic efficiency at φ = 1% and TR = 6

Date of download: 10/10/2017 Copyright © ASME. All rights reserved. From: Exergy Prediction Model of a Double Pipe Heat Exchanger Using Metal Oxide Nanofluids and Twisted Tape Based on the Artificial Neural Network Approach and Experimental Results J. Heat Transfer. 2015;138(1):011801-011801-10. doi:10.1115/1.4031073 Figure Legend: The correlation of exergetic efficiency between the experimental data and ANN prediction (all data)

Date of download: 10/10/2017 Copyright © ASME. All rights reserved. From: Exergy Prediction Model of a Double Pipe Heat Exchanger Using Metal Oxide Nanofluids and Twisted Tape Based on the Artificial Neural Network Approach and Experimental Results J. Heat Transfer. 2015;138(1):011801-011801-10. doi:10.1115/1.4031073 Figure Legend: The correlation of exergetic efficiency between the experimental data and ANN prediction (validation data)

Date of download: 10/10/2017 Copyright © ASME. All rights reserved. From: Exergy Prediction Model of a Double Pipe Heat Exchanger Using Metal Oxide Nanofluids and Twisted Tape Based on the Artificial Neural Network Approach and Experimental Results J. Heat Transfer. 2015;138(1):011801-011801-10. doi:10.1115/1.4031073 Figure Legend: Error of the determined exergetic efficiency values of the ACOR–ANN model from real values