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Predicting the effect of functionalized multi-walled carbon nanotubes on thermal performance factor of water under various Reynolds number using artificial neural network

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  • Alnaqi, Abdulwahab A.
  • Sayyad Tavoos Hal, Sina
  • Aghaei, Alireza
  • Soltanimehr, Mehdi
  • Afrand, Masoud
  • Nguyen, Truong Khang

Abstract

The aim of the current paper is modeling the thermal performance factor of water under influence of functionalized multi-walled carbon nanotubes. For this purpose, artificial neural networks (ANNs) have been employed. The thermal performance factor of the nanofluid is related to the drop in pressure and heat transfer. Therefore, usually the data associated with it does not follow a specific trend. In such cases, the neural network can be used well. Here, an optimal neural network was designed with 65 neurons, the inputs of which were Reynolds number and volume fractions. Moreover, 78 data were used for modeling, which included 62 data for training and 16 data for testing the model. With the aim of evaluation of the precision of the modeling by ANN, the experimental results were compared with neural network outputs in all cases. The results revealed that optimal ANN model has adequate accurateness to predict the complex trend of thermal performance factor.

Suggested Citation

  • Alnaqi, Abdulwahab A. & Sayyad Tavoos Hal, Sina & Aghaei, Alireza & Soltanimehr, Mehdi & Afrand, Masoud & Nguyen, Truong Khang, 2019. "Predicting the effect of functionalized multi-walled carbon nanotubes on thermal performance factor of water under various Reynolds number using artificial neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 493-500.
    • Handle: RePEc:eee:phsmap:v:521:y:2019:i:c:p:493-500
    DOI: 10.1016/j.physa.2019.01.057
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    5. Nafchi, Peyman Mirzakhani & Karimipour, Arash & Afrand, Masoud, 2019. "The evaluation on a new non-Newtonian hybrid mixture composed of TiO2/ZnO/EG to present a statistical approach of power law for its rheological and thermal properties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 1-18.
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    Cited by:

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    2. Arani, Ali Akbar Abbasian & Alirezaie, Ali & Kamyab, Mohammad Hassan & Motallebi, Sayyid Majid, 2020. "Statistical analysis of enriched water heat transfer with various sizes of MgO nanoparticles using artificial neural networks modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    3. Moghadam, Iman Panahi & Afrand, Masoud & Hamad, Samir M. & Barzinjy, Azeez A. & Talebizadehsardari, Pouyan, 2020. "Curve-fitting on experimental data for predicting the thermal-conductivity of a new generated hybrid nanofluid of graphene oxide-titanium oxide/water," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 548(C).
    4. Ahmadi, Mohammad Hossein & Baghban, Alireza & Sadeghzadeh, Milad & Hadipoor, Masoud & Ghazvini, Mahyar, 2020. "Evolving connectionist approaches to compute thermal conductivity of TiO2/water nanofluid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).

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