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Designing an Artificial Neural Network (ANN) to predict the viscosity of Silver/Ethylene glycol nanofluid at different temperatures and volume fraction of nanoparticles

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  • Toghraie, Davood
  • Sina, Nima
  • Jolfaei, Niyusha Adavoodi
  • Hajian, Mehdi
  • Afrand, Masoud

Abstract

In the current work, we investigate the dynamic viscosity of Ag/Ethylene glycol nanofluid within the temperature range of 25–55 ° C with volume fraction of nanoparticles range of 0.2%–2%. The experimental data includes 42 samples. At first, an Artificial Neural Network (ANN) is designed to predict the dynamic viscosity of this nanofluid and finally the results of ANN and correlation has been compared. The algorithm of generating the best architecture of ANN has been proposed and the best ANN has been used to predict the dynamic viscosity of Silver/Ethylene glycol nanofluid. It is found that the ANN can predict the viscosity of Ag/Ethylene glycol nanofluid with good precision compared to the correlation method. Also, in the correlation method, MSE is 0.0012, SSE is 0.0512 and the maximum value of error is 0.0858.

Suggested Citation

  • Toghraie, Davood & Sina, Nima & Jolfaei, Niyusha Adavoodi & Hajian, Mehdi & Afrand, Masoud, 2019. "Designing an Artificial Neural Network (ANN) to predict the viscosity of Silver/Ethylene glycol nanofluid at different temperatures and volume fraction of nanoparticles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
  • Handle: RePEc:eee:phsmap:v:534:y:2019:i:c:s0378437119312440
    DOI: 10.1016/j.physa.2019.122142
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    1. Hemmat Esfe, Mohammad & Hajmohammad, Hadi & Toghraie, Davood & Rostamian, Hadi & Mahian, Omid & Wongwises, Somchai, 2017. "Multi-objective optimization of nanofluid flow in double tube heat exchangers for applications in energy systems," Energy, Elsevier, vol. 137(C), pages 160-171.
    2. Ruhani, Behrooz & Barnoon, Pouya & Toghraie, Davood, 2019. "Statistical investigation for developing a new model for rheological behavior of Silica–ethylene glycol/Water hybrid Newtonian nanofluid using experimental data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 616-627.
    3. Reza Aghayari & Heydar Maddah & Mohammad Hossein Ahmadi & Wei-Mon Yan & Nahid Ghasemi, 2018. "Measurement and Artificial Neural Network Modeling of Electrical Conductivity of CuO/Glycerol Nanofluids at Various Thermal and Concentration Conditions," Energies, MDPI, vol. 11(5), pages 1-16, May.
    4. Ruhani, Behrooz & Toghraie, Davood & Hekmatifar, Maboud & Hadian, Mahdieh, 2019. "Statistical investigation for developing a new model for rheological behavior of ZnO–Ag (50%–50%)/Water hybrid Newtonian nanofluid using experimental data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 741-751.
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