Prediction of the Transport Properties (Thermal Conductivity and Viscosity) of Nanofluids Using Artificial Neural Network (ANN) Method

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Moosavi Majid; Daneshvar Azadeh; Firoozi Rad Khadijeh

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Journal: Nashrieh Shimi va Mohandesi Shimi Iran Year:2019 | Volume:38 | Issue:3 (93) Page(s): 219-242

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Information Journal Paper

Title

Prediction of the Transport Properties (Thermal Conductivity and Viscosity) of Nanofluids Using Artificial Neural Network (ANN) Method

Author(s)

Moosavi Majid | Daneshvar Azadeh | Firoozi Rad Khadijeh | Issue Writer Certificate

Keywords

nanofluidQ1

Thermal conductivityQ1

ViscosityQ1

ANNQ1

Abstract

In this work, feed-forward back-propagation Artificial Neural Networks (ANNs) have been presented to predict the enhancement of the relative Thermal conductivity and Viscosity of a wide range of nanofluids with different base fluids and nanoparticles. The Thermal conductivity ratio of nanofluids with respect to the base fluids has been modeled using an ANN model. The model considers the effects of the Thermal conductivity of the base fluid, the Thermal conductivityof nanoparticles, nanoparticle volume fraction percent, temperature, and nanoparticle cluster average size. The total number of experimental data used to design the stated network is 483 from 18 different nanofluids. The (5-18-1) topology has been obtained as the best topology of the ANN model. The results of the AARD% for the train, validation, and test sets of data are 2. 6, 2. 2, and 2. 3, respectively. The Viscosity ratio of the nanofluids with respect to the base fluids has been modeled using the other ANN model. The Viscosity of the base fluid, density ratio of the base fluids with respect to the nanoparticle, nanoparticle volume fraction percent, temperature, and nanoparticle cluster average size have been selected as the inputs of ANN model. The 510 experimental data have been used to design the stated network. The (5-19-1) topology has been obtained as the best topology of the ANN model. The results of the AARD% for the train, validation, and test sets of data are 2. 9, 3. 1, and 3. 2, respectively. Accordingly, two studied ANN models are in good agreement with experimental data. A comparison between the predictions of the proposed ANN models and those predicted by some traditional models such as Maxwell and Bruggeman models shows that much better agreements can be obtained using the ANN model. This model also can able us to predict the relative Thermal conductivity and Viscosity of new nanofluids in different conditions.

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APA: Copy

Moosavi, Majid, Daneshvar, Azadeh, & Firoozi Rad, Khadijeh. (2019). Prediction of the Transport Properties (Thermal Conductivity and Viscosity) of Nanofluids Using Artificial Neural Network (ANN) Method. NASHRIEH SHIMI VA MOHANDESI SHIMI IRAN (PERSIAN), 38(3 (93) ), 219-242. SID. https://sid.ir/paper/388651/en

Vancouver: Copy

Moosavi Majid, Daneshvar Azadeh, Firoozi Rad Khadijeh. Prediction of the Transport Properties (Thermal Conductivity and Viscosity) of Nanofluids Using Artificial Neural Network (ANN) Method. NASHRIEH SHIMI VA MOHANDESI SHIMI IRAN (PERSIAN)[Internet]. 2019;38(3 (93) ):219-242. Available from: https://sid.ir/paper/388651/en

IEEE: Copy

Majid Moosavi, Azadeh Daneshvar, and Khadijeh Firoozi Rad, “Prediction of the Transport Properties (Thermal Conductivity and Viscosity) of Nanofluids Using Artificial Neural Network (ANN) Method,” NASHRIEH SHIMI VA MOHANDESI SHIMI IRAN (PERSIAN), vol. 38, no. 3 (93) , pp. 219–242, 2019, [Online]. Available: https://sid.ir/paper/388651/en

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