Prediction of rheological behavior of SiO2-MWCNTs/10W40 hybrid nanolubricant by designing neural network
This paper assesses the viscosity of 10W40 engine oil containing hybrid nanomaterial at different temperatures using artificial neural network (ANN). The volumetric combination of hybrid nanomaterial is 90% silica (SiO 2 ) and 10% multi-walled carbon nanotubes (MWCNTs). Solid volume fraction, temper...
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| Veröffentlicht in: | Journal of thermal analysis and calorimetry 2018-03, Vol.131 (3), p.2741-2748 |
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| container_title | Journal of thermal analysis and calorimetry |
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| creator | Ahmadi Nadooshan, Afshin Hemmat Esfe, Mohammad Afrand, Masoud |
| description | This paper assesses the viscosity of 10W40 engine oil containing hybrid nanomaterial at different temperatures using artificial neural network (ANN). The volumetric combination of hybrid nanomaterial is 90% silica (SiO
2
) and 10% multi-walled carbon nanotubes (MWCNTs). Solid volume fraction, temperatures and shear rate were considered as input variables for ANN, and relative viscosity was output parameter. In order to predict viscosity data of SiO
2
-MWCNTs (90:10%)/10W40, a comparison between the experimental viscosity and that obtained from previous theoretical models was made. This comparison showed that none of the previous theoretical models were able to estimate the viscosity data. Therefore, a neural network was designed to predict the relative viscosity of hybrid nanolubricant. Artificial neural network function was utilized for viscosity data approximation with excellent precision as R
2
value was 0.9948. |
| doi_str_mv | 10.1007/s10973-017-6688-3 |
| format | Article |
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2
) and 10% multi-walled carbon nanotubes (MWCNTs). Solid volume fraction, temperatures and shear rate were considered as input variables for ANN, and relative viscosity was output parameter. In order to predict viscosity data of SiO
2
-MWCNTs (90:10%)/10W40, a comparison between the experimental viscosity and that obtained from previous theoretical models was made. This comparison showed that none of the previous theoretical models were able to estimate the viscosity data. Therefore, a neural network was designed to predict the relative viscosity of hybrid nanolubricant. Artificial neural network function was utilized for viscosity data approximation with excellent precision as R
2
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2
) and 10% multi-walled carbon nanotubes (MWCNTs). Solid volume fraction, temperatures and shear rate were considered as input variables for ANN, and relative viscosity was output parameter. In order to predict viscosity data of SiO
2
-MWCNTs (90:10%)/10W40, a comparison between the experimental viscosity and that obtained from previous theoretical models was made. This comparison showed that none of the previous theoretical models were able to estimate the viscosity data. Therefore, a neural network was designed to predict the relative viscosity of hybrid nanolubricant. Artificial neural network function was utilized for viscosity data approximation with excellent precision as R
2
value was 0.9948.</description><subject>Analytical Chemistry</subject><subject>Artificial neural networks</subject><subject>Chemistry</subject><subject>Chemistry and Materials Science</subject><subject>Concentration (composition)</subject><subject>Inorganic Chemistry</subject><subject>Mathematical models</subject><subject>Measurement Science and Instrumentation</subject><subject>Multi wall carbon nanotubes</subject><subject>Nanomaterials</subject><subject>Neural networks</subject><subject>Order parameters</subject><subject>Physical Chemistry</subject><subject>Polymer Sciences</subject><subject>Rheological properties</subject><subject>Shear rate</subject><subject>Silicon dioxide</subject><subject>Viscosity</subject><issn>1388-6150</issn><issn>1588-2926</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp1kE9LxDAQxYMouK5-AG8Bz3UnSdM2R1n8B6sruLDHkLZpm7UmmrTKfnuzVPDkaR7De2-YH0KXBK4JQL4IBETOEiB5kmVFkbAjNCM8Cipodhw1izojHE7RWQg7ABACyAx1L17XphqMs9g12Hfa9a41lepxqTv1ZZw_7F_NmiZP2-XzJiwIbFPA3b70psZWWdePUVbKDrjc41oH01pjW2z16GON1cO382_n6KRRfdAXv3OONne3m-VDslrfPy5vVknFOBuSmha8zPKGVopxpURVMi10ATwFTkXa0CIuixx4ReJjWlNNWF6WgqecN7lgc3Q11X549znqMMidG72NFyWNnNKMQkqii0yuyrsQvG7khzfvyu8lAXngKSeeMvKUB56SxQydMiF6bav9X_P_oR_7OHei</recordid><startdate>20180301</startdate><enddate>20180301</enddate><creator>Ahmadi Nadooshan, Afshin</creator><creator>Hemmat Esfe, Mohammad</creator><creator>Afrand, Masoud</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20180301</creationdate><title>Prediction of rheological behavior of SiO2-MWCNTs/10W40 hybrid nanolubricant by designing neural network</title><author>Ahmadi Nadooshan, Afshin ; Hemmat Esfe, Mohammad ; Afrand, Masoud</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c353t-d285b67f2ca35aa9cb3e9e805405294f28aa98705c1926ee2e137bb95455f793</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Analytical Chemistry</topic><topic>Artificial neural networks</topic><topic>Chemistry</topic><topic>Chemistry and Materials Science</topic><topic>Concentration (composition)</topic><topic>Inorganic Chemistry</topic><topic>Mathematical models</topic><topic>Measurement Science and Instrumentation</topic><topic>Multi wall carbon nanotubes</topic><topic>Nanomaterials</topic><topic>Neural networks</topic><topic>Order parameters</topic><topic>Physical Chemistry</topic><topic>Polymer Sciences</topic><topic>Rheological properties</topic><topic>Shear rate</topic><topic>Silicon dioxide</topic><topic>Viscosity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ahmadi Nadooshan, Afshin</creatorcontrib><creatorcontrib>Hemmat Esfe, Mohammad</creatorcontrib><creatorcontrib>Afrand, Masoud</creatorcontrib><collection>CrossRef</collection><jtitle>Journal of thermal analysis and calorimetry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ahmadi Nadooshan, Afshin</au><au>Hemmat Esfe, Mohammad</au><au>Afrand, Masoud</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of rheological behavior of SiO2-MWCNTs/10W40 hybrid nanolubricant by designing neural network</atitle><jtitle>Journal of thermal analysis and calorimetry</jtitle><stitle>J Therm Anal Calorim</stitle><date>2018-03-01</date><risdate>2018</risdate><volume>131</volume><issue>3</issue><spage>2741</spage><epage>2748</epage><pages>2741-2748</pages><issn>1388-6150</issn><eissn>1588-2926</eissn><abstract>This paper assesses the viscosity of 10W40 engine oil containing hybrid nanomaterial at different temperatures using artificial neural network (ANN). The volumetric combination of hybrid nanomaterial is 90% silica (SiO
2
) and 10% multi-walled carbon nanotubes (MWCNTs). Solid volume fraction, temperatures and shear rate were considered as input variables for ANN, and relative viscosity was output parameter. In order to predict viscosity data of SiO
2
-MWCNTs (90:10%)/10W40, a comparison between the experimental viscosity and that obtained from previous theoretical models was made. This comparison showed that none of the previous theoretical models were able to estimate the viscosity data. Therefore, a neural network was designed to predict the relative viscosity of hybrid nanolubricant. Artificial neural network function was utilized for viscosity data approximation with excellent precision as R
2
value was 0.9948.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s10973-017-6688-3</doi><tpages>8</tpages></addata></record> |
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| ispartof | Journal of thermal analysis and calorimetry, 2018-03, Vol.131 (3), p.2741-2748 |
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| language | eng |
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| subjects | Analytical Chemistry Artificial neural networks Chemistry Chemistry and Materials Science Concentration (composition) Inorganic Chemistry Mathematical models Measurement Science and Instrumentation Multi wall carbon nanotubes Nanomaterials Neural networks Order parameters Physical Chemistry Polymer Sciences Rheological properties Shear rate Silicon dioxide Viscosity |
| title | Prediction of rheological behavior of SiO2-MWCNTs/10W40 hybrid nanolubricant by designing neural network |
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