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A well-trained artificial neural network for predicting the rheological behavior of MWCNT–Al(2)O(3) (30–70%)/oil SAE40 hybrid nanofluid
In this study, the influence of different volume fractions ([Formula: see text] ) of nanoparticles and temperatures on the dynamic viscosity ([Formula: see text] ) of MWCNT–Al(2)O(3) (30–70%)/oil SAE40 hybrid nanofluid was examined by ANN. For this reason, the [Formula: see text] was derived for 203...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8408142/ https://www.ncbi.nlm.nih.gov/pubmed/34465796 http://dx.doi.org/10.1038/s41598-021-96808-4 |
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author | Esfe, Mohammad Hemmat Eftekhari, S. Ali Hekmatifar, Maboud Toghraie, Davood |
author_facet | Esfe, Mohammad Hemmat Eftekhari, S. Ali Hekmatifar, Maboud Toghraie, Davood |
author_sort | Esfe, Mohammad Hemmat |
collection | PubMed |
description | In this study, the influence of different volume fractions ([Formula: see text] ) of nanoparticles and temperatures on the dynamic viscosity ([Formula: see text] ) of MWCNT–Al(2)O(3) (30–70%)/oil SAE40 hybrid nanofluid was examined by ANN. For this reason, the [Formula: see text] was derived for 203 various experiments through a series of experimental tests, including a combination of 7 different [Formula: see text] , 6 various temperatures, and 5 shear rates. These data were then used to train an artificial neural network (ANN) to generalize results in the predefined ranges for two input parameters. For this reason, a feed-forward perceptron ANN with two inputs (T and [Formula: see text] ) and one output ([Formula: see text] ) was used. The best topology of the ANN was determined by trial and error, and a two-layer with 10 neurons in the hidden layer with the tansig function had the best performance. A well-trained ANN is created using the trainbr algorithm and showed an MSE value of 4.3e−3 along 0.999 as a correlation coefficient for predicting [Formula: see text] . The results show that an increase [Formula: see text] has a significant effect on [Formula: see text] value. As [Formula: see text] increases, the viscosity of this nanofluid increases at all temperatures. On the other hand, with increasing temperature, the viscosity of this nanofluid decreases. Based on all of the diagrams presented for the trained ANNs, we can conclude that a well-trained ANN can be used as an approximating function for predicting the [Formula: see text] . |
format | Online Article Text |
id | pubmed-8408142 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-84081422021-09-01 A well-trained artificial neural network for predicting the rheological behavior of MWCNT–Al(2)O(3) (30–70%)/oil SAE40 hybrid nanofluid Esfe, Mohammad Hemmat Eftekhari, S. Ali Hekmatifar, Maboud Toghraie, Davood Sci Rep Article In this study, the influence of different volume fractions ([Formula: see text] ) of nanoparticles and temperatures on the dynamic viscosity ([Formula: see text] ) of MWCNT–Al(2)O(3) (30–70%)/oil SAE40 hybrid nanofluid was examined by ANN. For this reason, the [Formula: see text] was derived for 203 various experiments through a series of experimental tests, including a combination of 7 different [Formula: see text] , 6 various temperatures, and 5 shear rates. These data were then used to train an artificial neural network (ANN) to generalize results in the predefined ranges for two input parameters. For this reason, a feed-forward perceptron ANN with two inputs (T and [Formula: see text] ) and one output ([Formula: see text] ) was used. The best topology of the ANN was determined by trial and error, and a two-layer with 10 neurons in the hidden layer with the tansig function had the best performance. A well-trained ANN is created using the trainbr algorithm and showed an MSE value of 4.3e−3 along 0.999 as a correlation coefficient for predicting [Formula: see text] . The results show that an increase [Formula: see text] has a significant effect on [Formula: see text] value. As [Formula: see text] increases, the viscosity of this nanofluid increases at all temperatures. On the other hand, with increasing temperature, the viscosity of this nanofluid decreases. Based on all of the diagrams presented for the trained ANNs, we can conclude that a well-trained ANN can be used as an approximating function for predicting the [Formula: see text] . Nature Publishing Group UK 2021-08-31 /pmc/articles/PMC8408142/ /pubmed/34465796 http://dx.doi.org/10.1038/s41598-021-96808-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Esfe, Mohammad Hemmat Eftekhari, S. Ali Hekmatifar, Maboud Toghraie, Davood A well-trained artificial neural network for predicting the rheological behavior of MWCNT–Al(2)O(3) (30–70%)/oil SAE40 hybrid nanofluid |
title | A well-trained artificial neural network for predicting the rheological behavior of MWCNT–Al(2)O(3) (30–70%)/oil SAE40 hybrid nanofluid |
title_full | A well-trained artificial neural network for predicting the rheological behavior of MWCNT–Al(2)O(3) (30–70%)/oil SAE40 hybrid nanofluid |
title_fullStr | A well-trained artificial neural network for predicting the rheological behavior of MWCNT–Al(2)O(3) (30–70%)/oil SAE40 hybrid nanofluid |
title_full_unstemmed | A well-trained artificial neural network for predicting the rheological behavior of MWCNT–Al(2)O(3) (30–70%)/oil SAE40 hybrid nanofluid |
title_short | A well-trained artificial neural network for predicting the rheological behavior of MWCNT–Al(2)O(3) (30–70%)/oil SAE40 hybrid nanofluid |
title_sort | well-trained artificial neural network for predicting the rheological behavior of mwcnt–al(2)o(3) (30–70%)/oil sae40 hybrid nanofluid |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8408142/ https://www.ncbi.nlm.nih.gov/pubmed/34465796 http://dx.doi.org/10.1038/s41598-021-96808-4 |
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