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Research Papers: Heat Exchangers

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

[+] Author and Article Information
Mohammad Mmohammadiun

Department of Mechanical Engineering,
Shahrood Branch,
Islamic Azad University,
Shahrood, Iran

Forough Dashtestani

Department of Chemical Engineering,
Shahrood Branch,
Islamic Azad University,
Shahrood, Iran

Mostafa Alizadeh

Chemical Engineering Faculty,
Tarbiat Modares University,
P.O. Box 14155-143,
Tehran, Iran
e-mail: Mostafa.alizadeh@modares.ac.ir

1Corresponding author.

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF HEAT TRANSFER. Manuscript received August 9, 2014; final manuscript received May 22, 2015; published online August 11, 2015. Assoc. Editor: Giulio Lorenzini.

J. Heat Transfer 138(1), 011801 (Aug 11, 2015) (10 pages) Paper No: HT-14-1520; doi: 10.1115/1.4031073 History: Received August 09, 2014

In heat transfer area, researches have been carried out over several years for the development of convective heat transfer enhancement (HTE) techniques. For proper optimization of thermal engineering systems in terms of design and operation, not only the heat transfer has to be maximized but also the exegetic efficiency has to be minimized as well. Present study provides a theoretical, numerical, and experimental investigation of the exergy analysis in a double pipe heat exchanger. For this purpose, metal oxide-water nanofluids and twisted tapes (TTs) are considered as the model fluids and turbulators. Results are verified with well-known correlations. The results show that nanofluids and TTs can increase the exergetic efficiency by 30–100% compared to empty tube and water as a base fluid. In addition, the exergetic efficiency increases with increase in nanoparticles concentration and decreases in twist ratio. CuO nanofluid gives better enhancement in exergetic efficiency than others under the same condition. Since the prediction of exergetic efficiency from experimental process is complex and time-consuming process, an ant colony optimization–back propagation (ACOR–BP) artificial neural networks (ANN) model for identification of the relationship, which may exist between the thermal and flow parameters and exergetic efficiency, have been developed. The network input consists of 11 parameters (C,nf,Cbf,ρbf,ρnf,ϕ,kbf,knf,μbf,μnf,unf,ubf) that crucially dominate the heat transfer process. The results indicate that ACOR–BP ANN provides a high degree of accuracy and reliability. The proposed ANN model can be used to understand how key parameters affect exergetic efficiency without using extensive numerical modeling or experimental studies.

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Figures

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Fig. 1

Schematic diagram of the experimental setup

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Fig. 2

Validation of plain tube experimental data for Nusselt number

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Fig. 3

Validation of plain tube with TTs and water for friction factor

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Fig. 4

Validation of plain tube with TTs and water for Nusselt number

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Fig. 5

Validation of plain tube with TTs and water for friction factor

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Fig. 6

Rational efficiency as a function of Reynolds number

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Fig. 7

Comparison of different nanoparticles effect on exergetic efficiency at φ = 1% and TR = 6

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Fig. 8

The correlation of exergetic efficiency between the experimental data and ANN prediction (all data)

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Fig. 9

The correlation of exergetic efficiency between the experimental data and ANN prediction (validation data)

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Fig. 10

Error of the determined exergetic efficiency values of the ACOR–ANN model from real values

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