Increasing the accuracy of estimating the viscosity of SAE40-based nanofluid containing MWCNT-TiO

The viscosity ( μ nf ) is one of the influencing parameters in choosing that nanofluid (NF) that affects its thermal beh...
Ausführliche Beschreibung

The viscosity ( μ nf ) is one of the influencing parameters in choosing that nanofluid (NF) that affects its thermal behavior and heat transfer. In this regard, the present study is conducted to investigate the μ nf of MWCNT-TiO2 (10:90) /SAE40 NF using an artificial neural network (ANN). ANN design in temperature conditions between 25 and 50°C and in different solid volume fraction (SVF) of nanoparticles ( φ =0.0625%−1%). 174 laboratory data were used. Three inputs (temperature(T), φ and shear rate(SR)) and one output ( μ nf ) are determined for ANN. In this study, ANN modeling was done with MLP method and Levenberg-Marquardt (LM) training algorithm. The selected optimal structure, among different ANN structures for MWCNT-TiO2(10:90)/SAE40 NF, has two hidden layers with an optimal structure of 10 and 4 neurons in the first and second layers. The results of R and MSE coefficients prove the accuracy of the proposed model., which in the final stage was set equal to 0.9999507 and 0.004599708, respectively. The margin of deviation (MOD) in the grid data set is in the range − 3% <MOD < + 3%. Based on studies that considered ANN correlation with computational data compared to laboratory data, the results of this comparison show that the ANN is more accurate and reliable for estimating the μ nf . The turning point of this article is designing and predicting the viscosity of MWCNT-TiO2/SAE40 NF in the least time and financial costs compared to repetitive and time-consuming experiments through ANN. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Esfe, Mohammad Hemmat [verfasserIn] Toghraie, Davood [verfasserIn] Amoozadkhalili, Fatemeh [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Correlation Multilayer perceptron MOD Nanofluid Dynamic viscosity

Übergeordnetes Werk:	Enthalten in: Tribology international - Amsterdam [u.a.] : Elsevier Science, 1975, 179
Übergeordnetes Werk:	volume:179

DOI / URN:	10.1016/j.triboint.2022.108161

Katalog-ID:	ELV009070877

Internformat


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100	1		\|a Esfe, Mohammad Hemmat \|e verfasserin \|4 aut
245	1	0	\|a Increasing the accuracy of estimating the viscosity of SAE40-based nanofluid containing MWCNT-TiO
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520			\|a The viscosity ( μ nf ) is one of the influencing parameters in choosing that nanofluid (NF) that affects its thermal behavior and heat transfer. In this regard, the present study is conducted to investigate the μ nf of MWCNT-TiO2 (10:90) /SAE40 NF using an artificial neural network (ANN). ANN design in temperature conditions between 25 and 50°C and in different solid volume fraction (SVF) of nanoparticles ( φ =0.0625%−1%). 174 laboratory data were used. Three inputs (temperature(T), φ and shear rate(SR)) and one output ( μ nf ) are determined for ANN. In this study, ANN modeling was done with MLP method and Levenberg-Marquardt (LM) training algorithm. The selected optimal structure, among different ANN structures for MWCNT-TiO2(10:90)/SAE40 NF, has two hidden layers with an optimal structure of 10 and 4 neurons in the first and second layers. The results of R and MSE coefficients prove the accuracy of the proposed model., which in the final stage was set equal to 0.9999507 and 0.004599708, respectively. The margin of deviation (MOD) in the grid data set is in the range − 3% <MOD < + 3%. Based on studies that considered ANN correlation with computational data compared to laboratory data, the results of this comparison show that the ANN is more accurate and reliable for estimating the μ nf . The turning point of this article is designing and predicting the viscosity of MWCNT-TiO2/SAE40 NF in the least time and financial costs compared to repetitive and time-consuming experiments through ANN.
650		4	\|a Correlation
650		4	\|a Multilayer perceptron
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650		4	\|a Dynamic viscosity
700	1		\|a Toghraie, Davood \|e verfasserin \|4 aut
700	1		\|a Amoozadkhalili, Fatemeh \|e verfasserin \|4 aut
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912			\|a GBV_ILN_110
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912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
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912			\|a GBV_ILN_702
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
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912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
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912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
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912			\|a GBV_ILN_4323
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912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 52.12 \|j Tribologie
951			\|a AR
952			\|d 179

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publishDate	2022
allfields	10.1016/j.triboint.2022.108161 doi (DE-627)ELV009070877 (ELSEVIER)S0301-679X(22)00732-0 DE-627 ger DE-627 rda eng 660 DE-600 52.12 bkl Esfe, Mohammad Hemmat verfasserin aut Increasing the accuracy of estimating the viscosity of SAE40-based nanofluid containing MWCNT-TiO 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The viscosity ( μ nf ) is one of the influencing parameters in choosing that nanofluid (NF) that affects its thermal behavior and heat transfer. In this regard, the present study is conducted to investigate the μ nf of MWCNT-TiO2 (10:90) /SAE40 NF using an artificial neural network (ANN). ANN design in temperature conditions between 25 and 50°C and in different solid volume fraction (SVF) of nanoparticles ( φ =0.0625%−1%). 174 laboratory data were used. Three inputs (temperature(T), φ and shear rate(SR)) and one output ( μ nf ) are determined for ANN. In this study, ANN modeling was done with MLP method and Levenberg-Marquardt (LM) training algorithm. The selected optimal structure, among different ANN structures for MWCNT-TiO2(10:90)/SAE40 NF, has two hidden layers with an optimal structure of 10 and 4 neurons in the first and second layers. The results of R and MSE coefficients prove the accuracy of the proposed model., which in the final stage was set equal to 0.9999507 and 0.004599708, respectively. The margin of deviation (MOD) in the grid data set is in the range − 3% <MOD < + 3%. Based on studies that considered ANN correlation with computational data compared to laboratory data, the results of this comparison show that the ANN is more accurate and reliable for estimating the μ nf . The turning point of this article is designing and predicting the viscosity of MWCNT-TiO2/SAE40 NF in the least time and financial costs compared to repetitive and time-consuming experiments through ANN. Correlation Multilayer perceptron MOD Nanofluid Dynamic viscosity Toghraie, Davood verfasserin aut Amoozadkhalili, Fatemeh verfasserin aut Enthalten in Tribology international Amsterdam [u.a.] : Elsevier Science, 1975 179 Online-Ressource (DE-627)314125485 (DE-600)1501092-2 (DE-576)116451750 0301-679X nnns volume:179 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.12 Tribologie AR 179
spelling	10.1016/j.triboint.2022.108161 doi (DE-627)ELV009070877 (ELSEVIER)S0301-679X(22)00732-0 DE-627 ger DE-627 rda eng 660 DE-600 52.12 bkl Esfe, Mohammad Hemmat verfasserin aut Increasing the accuracy of estimating the viscosity of SAE40-based nanofluid containing MWCNT-TiO 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The viscosity ( μ nf ) is one of the influencing parameters in choosing that nanofluid (NF) that affects its thermal behavior and heat transfer. In this regard, the present study is conducted to investigate the μ nf of MWCNT-TiO2 (10:90) /SAE40 NF using an artificial neural network (ANN). ANN design in temperature conditions between 25 and 50°C and in different solid volume fraction (SVF) of nanoparticles ( φ =0.0625%−1%). 174 laboratory data were used. Three inputs (temperature(T), φ and shear rate(SR)) and one output ( μ nf ) are determined for ANN. In this study, ANN modeling was done with MLP method and Levenberg-Marquardt (LM) training algorithm. The selected optimal structure, among different ANN structures for MWCNT-TiO2(10:90)/SAE40 NF, has two hidden layers with an optimal structure of 10 and 4 neurons in the first and second layers. The results of R and MSE coefficients prove the accuracy of the proposed model., which in the final stage was set equal to 0.9999507 and 0.004599708, respectively. The margin of deviation (MOD) in the grid data set is in the range − 3% <MOD < + 3%. Based on studies that considered ANN correlation with computational data compared to laboratory data, the results of this comparison show that the ANN is more accurate and reliable for estimating the μ nf . The turning point of this article is designing and predicting the viscosity of MWCNT-TiO2/SAE40 NF in the least time and financial costs compared to repetitive and time-consuming experiments through ANN. Correlation Multilayer perceptron MOD Nanofluid Dynamic viscosity Toghraie, Davood verfasserin aut Amoozadkhalili, Fatemeh verfasserin aut Enthalten in Tribology international Amsterdam [u.a.] : Elsevier Science, 1975 179 Online-Ressource (DE-627)314125485 (DE-600)1501092-2 (DE-576)116451750 0301-679X nnns volume:179 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.12 Tribologie AR 179
allfields_unstemmed	10.1016/j.triboint.2022.108161 doi (DE-627)ELV009070877 (ELSEVIER)S0301-679X(22)00732-0 DE-627 ger DE-627 rda eng 660 DE-600 52.12 bkl Esfe, Mohammad Hemmat verfasserin aut Increasing the accuracy of estimating the viscosity of SAE40-based nanofluid containing MWCNT-TiO 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The viscosity ( μ nf ) is one of the influencing parameters in choosing that nanofluid (NF) that affects its thermal behavior and heat transfer. In this regard, the present study is conducted to investigate the μ nf of MWCNT-TiO2 (10:90) /SAE40 NF using an artificial neural network (ANN). ANN design in temperature conditions between 25 and 50°C and in different solid volume fraction (SVF) of nanoparticles ( φ =0.0625%−1%). 174 laboratory data were used. Three inputs (temperature(T), φ and shear rate(SR)) and one output ( μ nf ) are determined for ANN. In this study, ANN modeling was done with MLP method and Levenberg-Marquardt (LM) training algorithm. The selected optimal structure, among different ANN structures for MWCNT-TiO2(10:90)/SAE40 NF, has two hidden layers with an optimal structure of 10 and 4 neurons in the first and second layers. The results of R and MSE coefficients prove the accuracy of the proposed model., which in the final stage was set equal to 0.9999507 and 0.004599708, respectively. The margin of deviation (MOD) in the grid data set is in the range − 3% <MOD < + 3%. Based on studies that considered ANN correlation with computational data compared to laboratory data, the results of this comparison show that the ANN is more accurate and reliable for estimating the μ nf . The turning point of this article is designing and predicting the viscosity of MWCNT-TiO2/SAE40 NF in the least time and financial costs compared to repetitive and time-consuming experiments through ANN. Correlation Multilayer perceptron MOD Nanofluid Dynamic viscosity Toghraie, Davood verfasserin aut Amoozadkhalili, Fatemeh verfasserin aut Enthalten in Tribology international Amsterdam [u.a.] : Elsevier Science, 1975 179 Online-Ressource (DE-627)314125485 (DE-600)1501092-2 (DE-576)116451750 0301-679X nnns volume:179 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.12 Tribologie AR 179
allfieldsGer	10.1016/j.triboint.2022.108161 doi (DE-627)ELV009070877 (ELSEVIER)S0301-679X(22)00732-0 DE-627 ger DE-627 rda eng 660 DE-600 52.12 bkl Esfe, Mohammad Hemmat verfasserin aut Increasing the accuracy of estimating the viscosity of SAE40-based nanofluid containing MWCNT-TiO 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The viscosity ( μ nf ) is one of the influencing parameters in choosing that nanofluid (NF) that affects its thermal behavior and heat transfer. In this regard, the present study is conducted to investigate the μ nf of MWCNT-TiO2 (10:90) /SAE40 NF using an artificial neural network (ANN). ANN design in temperature conditions between 25 and 50°C and in different solid volume fraction (SVF) of nanoparticles ( φ =0.0625%−1%). 174 laboratory data were used. Three inputs (temperature(T), φ and shear rate(SR)) and one output ( μ nf ) are determined for ANN. In this study, ANN modeling was done with MLP method and Levenberg-Marquardt (LM) training algorithm. The selected optimal structure, among different ANN structures for MWCNT-TiO2(10:90)/SAE40 NF, has two hidden layers with an optimal structure of 10 and 4 neurons in the first and second layers. The results of R and MSE coefficients prove the accuracy of the proposed model., which in the final stage was set equal to 0.9999507 and 0.004599708, respectively. The margin of deviation (MOD) in the grid data set is in the range − 3% <MOD < + 3%. Based on studies that considered ANN correlation with computational data compared to laboratory data, the results of this comparison show that the ANN is more accurate and reliable for estimating the μ nf . The turning point of this article is designing and predicting the viscosity of MWCNT-TiO2/SAE40 NF in the least time and financial costs compared to repetitive and time-consuming experiments through ANN. Correlation Multilayer perceptron MOD Nanofluid Dynamic viscosity Toghraie, Davood verfasserin aut Amoozadkhalili, Fatemeh verfasserin aut Enthalten in Tribology international Amsterdam [u.a.] : Elsevier Science, 1975 179 Online-Ressource (DE-627)314125485 (DE-600)1501092-2 (DE-576)116451750 0301-679X nnns volume:179 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.12 Tribologie AR 179
allfieldsSound	10.1016/j.triboint.2022.108161 doi (DE-627)ELV009070877 (ELSEVIER)S0301-679X(22)00732-0 DE-627 ger DE-627 rda eng 660 DE-600 52.12 bkl Esfe, Mohammad Hemmat verfasserin aut Increasing the accuracy of estimating the viscosity of SAE40-based nanofluid containing MWCNT-TiO 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The viscosity ( μ nf ) is one of the influencing parameters in choosing that nanofluid (NF) that affects its thermal behavior and heat transfer. In this regard, the present study is conducted to investigate the μ nf of MWCNT-TiO2 (10:90) /SAE40 NF using an artificial neural network (ANN). ANN design in temperature conditions between 25 and 50°C and in different solid volume fraction (SVF) of nanoparticles ( φ =0.0625%−1%). 174 laboratory data were used. Three inputs (temperature(T), φ and shear rate(SR)) and one output ( μ nf ) are determined for ANN. In this study, ANN modeling was done with MLP method and Levenberg-Marquardt (LM) training algorithm. The selected optimal structure, among different ANN structures for MWCNT-TiO2(10:90)/SAE40 NF, has two hidden layers with an optimal structure of 10 and 4 neurons in the first and second layers. The results of R and MSE coefficients prove the accuracy of the proposed model., which in the final stage was set equal to 0.9999507 and 0.004599708, respectively. The margin of deviation (MOD) in the grid data set is in the range − 3% <MOD < + 3%. Based on studies that considered ANN correlation with computational data compared to laboratory data, the results of this comparison show that the ANN is more accurate and reliable for estimating the μ nf . The turning point of this article is designing and predicting the viscosity of MWCNT-TiO2/SAE40 NF in the least time and financial costs compared to repetitive and time-consuming experiments through ANN. Correlation Multilayer perceptron MOD Nanofluid Dynamic viscosity Toghraie, Davood verfasserin aut Amoozadkhalili, Fatemeh verfasserin aut Enthalten in Tribology international Amsterdam [u.a.] : Elsevier Science, 1975 179 Online-Ressource (DE-627)314125485 (DE-600)1501092-2 (DE-576)116451750 0301-679X nnns volume:179 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.12 Tribologie AR 179
language	English
source	Enthalten in Tribology international 179 volume:179
sourceStr	Enthalten in Tribology international 179 volume:179
format_phy_str_mv	Article
bklname	Tribologie
institution	findex.gbv.de
topic_facet	Correlation Multilayer perceptron MOD Nanofluid Dynamic viscosity
dewey-raw	660
isfreeaccess_bool	false
container_title	Tribology international
authorswithroles_txt_mv	Esfe, Mohammad Hemmat @@aut@@ Toghraie, Davood @@aut@@ Amoozadkhalili, Fatemeh @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	314125485
dewey-sort	3660
id	ELV009070877
language_de	englisch
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author	Esfe, Mohammad Hemmat
spellingShingle	Esfe, Mohammad Hemmat ddc 660 bkl 52.12 misc Correlation misc Multilayer perceptron misc MOD misc Nanofluid misc Dynamic viscosity Increasing the accuracy of estimating the viscosity of SAE40-based nanofluid containing MWCNT-TiO
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topic_title	660 DE-600 52.12 bkl Increasing the accuracy of estimating the viscosity of SAE40-based nanofluid containing MWCNT-TiO Correlation Multilayer perceptron MOD Nanofluid Dynamic viscosity
topic	ddc 660 bkl 52.12 misc Correlation misc Multilayer perceptron misc MOD misc Nanofluid misc Dynamic viscosity
topic_unstemmed	ddc 660 bkl 52.12 misc Correlation misc Multilayer perceptron misc MOD misc Nanofluid misc Dynamic viscosity
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title	Increasing the accuracy of estimating the viscosity of SAE40-based nanofluid containing MWCNT-TiO
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title_full	Increasing the accuracy of estimating the viscosity of SAE40-based nanofluid containing MWCNT-TiO
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title_sort	increasing the accuracy of estimating the viscosity of sae40-based nanofluid containing mwcnt-tio
title_auth	Increasing the accuracy of estimating the viscosity of SAE40-based nanofluid containing MWCNT-TiO
abstract	The viscosity ( μ nf ) is one of the influencing parameters in choosing that nanofluid (NF) that affects its thermal behavior and heat transfer. In this regard, the present study is conducted to investigate the μ nf of MWCNT-TiO2 (10:90) /SAE40 NF using an artificial neural network (ANN). ANN design in temperature conditions between 25 and 50°C and in different solid volume fraction (SVF) of nanoparticles ( φ =0.0625%−1%). 174 laboratory data were used. Three inputs (temperature(T), φ and shear rate(SR)) and one output ( μ nf ) are determined for ANN. In this study, ANN modeling was done with MLP method and Levenberg-Marquardt (LM) training algorithm. The selected optimal structure, among different ANN structures for MWCNT-TiO2(10:90)/SAE40 NF, has two hidden layers with an optimal structure of 10 and 4 neurons in the first and second layers. The results of R and MSE coefficients prove the accuracy of the proposed model., which in the final stage was set equal to 0.9999507 and 0.004599708, respectively. The margin of deviation (MOD) in the grid data set is in the range − 3% <MOD < + 3%. Based on studies that considered ANN correlation with computational data compared to laboratory data, the results of this comparison show that the ANN is more accurate and reliable for estimating the μ nf . The turning point of this article is designing and predicting the viscosity of MWCNT-TiO2/SAE40 NF in the least time and financial costs compared to repetitive and time-consuming experiments through ANN.
abstractGer	The viscosity ( μ nf ) is one of the influencing parameters in choosing that nanofluid (NF) that affects its thermal behavior and heat transfer. In this regard, the present study is conducted to investigate the μ nf of MWCNT-TiO2 (10:90) /SAE40 NF using an artificial neural network (ANN). ANN design in temperature conditions between 25 and 50°C and in different solid volume fraction (SVF) of nanoparticles ( φ =0.0625%−1%). 174 laboratory data were used. Three inputs (temperature(T), φ and shear rate(SR)) and one output ( μ nf ) are determined for ANN. In this study, ANN modeling was done with MLP method and Levenberg-Marquardt (LM) training algorithm. The selected optimal structure, among different ANN structures for MWCNT-TiO2(10:90)/SAE40 NF, has two hidden layers with an optimal structure of 10 and 4 neurons in the first and second layers. The results of R and MSE coefficients prove the accuracy of the proposed model., which in the final stage was set equal to 0.9999507 and 0.004599708, respectively. The margin of deviation (MOD) in the grid data set is in the range − 3% <MOD < + 3%. Based on studies that considered ANN correlation with computational data compared to laboratory data, the results of this comparison show that the ANN is more accurate and reliable for estimating the μ nf . The turning point of this article is designing and predicting the viscosity of MWCNT-TiO2/SAE40 NF in the least time and financial costs compared to repetitive and time-consuming experiments through ANN.
abstract_unstemmed	The viscosity ( μ nf ) is one of the influencing parameters in choosing that nanofluid (NF) that affects its thermal behavior and heat transfer. In this regard, the present study is conducted to investigate the μ nf of MWCNT-TiO2 (10:90) /SAE40 NF using an artificial neural network (ANN). ANN design in temperature conditions between 25 and 50°C and in different solid volume fraction (SVF) of nanoparticles ( φ =0.0625%−1%). 174 laboratory data were used. Three inputs (temperature(T), φ and shear rate(SR)) and one output ( μ nf ) are determined for ANN. In this study, ANN modeling was done with MLP method and Levenberg-Marquardt (LM) training algorithm. The selected optimal structure, among different ANN structures for MWCNT-TiO2(10:90)/SAE40 NF, has two hidden layers with an optimal structure of 10 and 4 neurons in the first and second layers. The results of R and MSE coefficients prove the accuracy of the proposed model., which in the final stage was set equal to 0.9999507 and 0.004599708, respectively. The margin of deviation (MOD) in the grid data set is in the range − 3% <MOD < + 3%. Based on studies that considered ANN correlation with computational data compared to laboratory data, the results of this comparison show that the ANN is more accurate and reliable for estimating the μ nf . The turning point of this article is designing and predicting the viscosity of MWCNT-TiO2/SAE40 NF in the least time and financial costs compared to repetitive and time-consuming experiments through ANN.
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title_short	Increasing the accuracy of estimating the viscosity of SAE40-based nanofluid containing MWCNT-TiO
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up_date	2024-07-06T21:53:15.927Z
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Increasing the accuracy of estimating the viscosity of SAE40-based nanofluid containing MWCNT-TiO

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