Addition of MWCNT-Al2O3 nanopowders to water- ethylene glycol (EG) base fluid for enhancing the thermal characteristics: Design an optimum feed-forward neural network
Prediction the thermal conductivity of nanofluids has been subject of many researches. Artificial Neural Networks are used to obtain thermal conductivity of NAnofluids because not only this method is fast and acurate but also it can reduce the Lab costs. To predict the thermal conductivity of water-...
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| Published in | Case studies in thermal engineering Vol. 27; p. 101293 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.10.2021
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2214-157X 2214-157X |
| DOI | 10.1016/j.csite.2021.101293 |
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| Abstract | Prediction the thermal conductivity of nanofluids has been subject of many researches. Artificial Neural Networks are used to obtain thermal conductivity of NAnofluids because not only this method is fast and acurate but also it can reduce the Lab costs. To predict the thermal conductivity of water- EG/MWCNT-Al2O3 hybrid nanofluid (knf) a feed-forward neural network with different neuron numbers has been tested and the best network based on the performance is selected. The Levenberg Marquardt algorithm is used for training the network, which is one of the best algorithms in machine learning. Also, using a fitting method, a surface is used to illustrate the behavior of nanofluids based on the volume fraction of nanoparticles (ϕ) and temperature (T). ϕ=0, 0.001, 0.002, 0.004, 0.008, 0.0016 and T = 25, 30, 35, 40, 45, 50 °C are used.. The obtained results show that the ANN and Fitting results are close to the experimental datapoints, and both methods can predict knf accurately. As the results of these methods are very close, but the ANN method is better in predicting the behavior of this nanofluid. |
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| AbstractList | Prediction the thermal conductivity of nanofluids has been subject of many researches. Artificial Neural Networks are used to obtain thermal conductivity of NAnofluids because not only this method is fast and acurate but also it can reduce the Lab costs. To predict the thermal conductivity of water- EG/MWCNT-Al2O3 hybrid nanofluid (knf) a feed-forward neural network with different neuron numbers has been tested and the best network based on the performance is selected. The Levenberg Marquardt algorithm is used for training the network, which is one of the best algorithms in machine learning. Also, using a fitting method, a surface is used to illustrate the behavior of nanofluids based on the volume fraction of nanoparticles (ϕ) and temperature (T). ϕ=0, 0.001, 0.002, 0.004, 0.008, 0.0016 and T = 25, 30, 35, 40, 45, 50 °C are used.. The obtained results show that the ANN and Fitting results are close to the experimental datapoints, and both methods can predict knf accurately. As the results of these methods are very close, but the ANN method is better in predicting the behavior of this nanofluid. |
| ArticleNumber | 101293 |
| Author | Sina, Nima Hamedi, Sajad Fuxi, Shi Alizadeh, As'ad Hajian, Mehdi Toghraie, Davood Hekmatifar, Mabood |
| Author_xml | – sequence: 1 givenname: Shi surname: Fuxi fullname: Fuxi, Shi organization: College of Mechanical and Electronic Engineering Northwest A&F University, PR China – sequence: 2 givenname: Sajad surname: Hamedi fullname: Hamedi, Sajad organization: Department of Mechanical Engineering, Daneshpajoohan higher education institute, Iran – sequence: 3 givenname: Mehdi surname: Hajian fullname: Hajian, Mehdi organization: Faculty of Mechanics, Malek Ashtar University of Technology, Iran – sequence: 4 givenname: Davood surname: Toghraie fullname: Toghraie, Davood email: Toghraee@iaukhsh.ac.ir organization: Department of Mechanical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr, Iran – sequence: 5 givenname: As'ad surname: Alizadeh fullname: Alizadeh, As'ad organization: Department of Mechanical Engineering, College of Engineering, University of Zakho, Zakho, Iraq – sequence: 6 givenname: Mabood surname: Hekmatifar fullname: Hekmatifar, Mabood organization: Department of Mechanical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr, Iran – sequence: 7 givenname: Nima surname: Sina fullname: Sina, Nima organization: Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran |
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| Cites_doi | 10.1090/qam/10666 10.1007/s10765-020-02749-x 10.1080/10407782.2013.846196 10.1016/j.icheatmasstransfer.2014.07.018 10.1016/j.icheatmasstransfer.2016.05.029 10.1016/j.colsurfa.2020.125968 10.1007/s10973-020-09458-5 10.1016/j.egyr.2021.03.020 10.1016/j.solmat.2019.110323 10.1016/j.powtec.2020.09.011 10.1016/j.powtec.2017.10.025 10.1016/j.physa.2019.122142 10.14429/dsj.58.1682 10.1016/j.icheatmasstransfer.2013.05.009 10.1016/j.energy.2020.118760 10.1137/0111030 10.1016/j.physleta.2011.01.040 10.1007/s10973-016-5436-4 10.1016/j.powtec.2019.07.086 |
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| SubjectTerms | ANNs Levenberg marquardt algorithm Nanofluids Surface fitting method Thermal conductivity |
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| Title | Addition of MWCNT-Al2O3 nanopowders to water- ethylene glycol (EG) base fluid for enhancing the thermal characteristics: Design an optimum feed-forward neural network |
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