Applying Artificial Neural Networks (ANNs) for prediction of the thermal characteristics of water/ethylene glycol-based mono, binary and ternary nanofluids containing MWCNTs, titania, and zinc oxide

被引：58
|
作者：
Yang, Xiaowei [1 ]
Boroomandpour, Ahmadreza [2 ]
Wen, Shiwei [3 ]
Toghraie, Davood [2 ]
Soltani, Farid [4 ]
机构：
[1] Yancheng Teachers Univ, Coll Appl Chem & Environm Engn, Inst New Energy Chem Storage & Power Source, Yancheng 224007, Jiangsu, Peoples R China
[2] Islamic Azad Univ, Dept Mech Engn, Khomeinishahr Branch, Khomeinishahr, Iran
[3] Zhongshan Haohui Met Prod Co Ltd, Zhongshan 528400, Peoples R China
[4] Univ Kashan, Dept Mech Engn, Kashan, Iran
来源：
POWDER TECHNOLOGY | 2021年 / 388卷
基金：
中国国家自然科学基金;
关键词：
Artificial Neural Network; Thermal conductivity; Hybrid nanofluid; MWCNTs; Titania; Zinc oxide; CONDUCTIVITY; HYBRID; MODEL; VISCOSITY;
D O I：
10.1016/j.powtec.2021.04.093
中图分类号：
TQ [化学工业];
学科分类号：
0817 ;
摘要：
An Artificial Neural Network (ANN) was applied to model the thermal conductivity (k(nf)) inwater/ethylene glycol (80:20) based hybrid nanofluid containing MWCNTs-titania-Zinc oxide. The nanofluids were synthesized by a two-step method. The ternary hybrid nanofluids had a volume fraction of nanoparticles phi = 0.1% to 0.4%, as well as mono and binary hybrid nanofluids. The experiments were performed at temperatures T = 25 degrees C-50 degrees C. Then an ANN has been used to predict the knf. According to the results, the optimum neuron number was 26. The designed network has acceptable performance and the maximum absolute error was less than 0.018 in 102 data points. (C) 2021 Elsevier B.V. All rights reserved.
引用
收藏
页码：418 / 424
页数：7
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    Toghraie, Davood
    Gheisari, Ali
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    Alizadeh, As'ad
    [J]. CASE STUDIES IN THERMAL ENGINEERING, 2021, 26 (26)
  • [2] A comprehensive experimental investigation of thermal conductivity of a ternary hybrid nanofluid containing MWCNTs- titania-zinc oxide/water-ethylene glycol (80:20) as well as binary and mono nanofluids
    Boroomandpour, Ahmadreza
    Toghraie, Davood
    Hashemian, Mohammad
    [J]. SYNTHETIC METALS, 2020, 268
  • [3] Accurate prediction of thermal conductivity of ethylene glycol-based hybrid nanofluids using artificial intelligence techniques
    Jamei, Mehdi
    Pourrajab, Rashid
    Ahmadianfar, Iman
    Noghrehabadi, Aminreza
    [J]. INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER, 2020, 116 (116)
  • [4] MgO nanofluids: higher thermal conductivity and lower viscosity among ethylene glycol-based nanofluids containing oxide nanoparticles
    Xie, Huaqing
    Yu, Wei
    Chen, Wei
    [J]. JOURNAL OF EXPERIMENTAL NANOSCIENCE, 2010, 5 (05) : 463 - 472
  • [5] Prediction of thermal conductivity of ethylene glycol-water solutions by using artificial neural networks
    Kurt, Hueseyin
    Kayfeci, Muhammet
    [J]. APPLIED ENERGY, 2009, 86 (10) : 2244 - 2248
  • [6] Developing dissimilar artificial neural networks (ANNs) to prediction the thermal conductivity of MWCNT-TiO2/Water-ethylene glycol hybrid nanofluid
    Akhgar, Alireza
    Toghraie, Davood
    Sina, Nima
    Afrand, Masoud
    [J]. POWDER TECHNOLOGY, 2019, 355 : 602 - 610
  • [7] Prediction of thermal conductivity of alumina water-based nanofluids by artificial neural networks
    Ariana, M. A.
    Vaferi, B.
    Karimi, G.
    [J]. POWDER TECHNOLOGY, 2015, 278 : 1 - 10
  • [8] Surface Tension of Ethylene Glycol-Based Nanofluids Containing Three Types of Oxides: Zinc Oxide (ZnO), Magnesium Oxide (MgO) and Indium Oxide (In2O3)
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    Alotaibi, Sorour
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    Ahmadi, Mohammad Hossein
    Nabipour, Narjes
    Chau, Kwok-wing
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    Toghraie, Davood
    Izadpanahi, Niloufar
    Esfahani, Navid Nasajpour
    Karimipour, Arash
    Afrand, Masoud
    [J]. INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER, 2020, 116
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