Applying different types of artificial neural network for modeling thermal conductivity of nanofluids containing silica particles
Nanofluids are widely applicable in thermal devices with porous structures. Silica nanoparticles have been dispersed in different heat transfer fluids in order to increase their thermal conductivity and heat transfer capability. In this study, group method of data handling (GMDH) and multilayer perc...
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| Veröffentlicht in: | Journal of thermal analysis and calorimetry 2021-05, Vol.144 (4), p.1613-1622 |
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| container_issue | 4 |
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| container_title | Journal of thermal analysis and calorimetry |
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| creator | Maleki, Akbar Haghighi, Arman Irandoost Shahrestani, Misagh Abdelmalek, Zahra |
| description | Nanofluids are widely applicable in thermal devices with porous structures. Silica nanoparticles have been dispersed in different heat transfer fluids in order to increase their thermal conductivity and heat transfer capability. In this study, group method of data handling (GMDH) and multilayer perceptron artificial neural networks are applied for determining thermal conductivity of nanofluids with silica particles and different base fluids such as ethylene glycol, glycerol, water and ethylene glycol–water mixture. For cases with multilayer perceptron models, trained by applying scaled conjugate gradient (SCG) and Levenberg–Marquardt (LM) have been tested as two different training algorithms. The outputs of the applied models have good agreement with the values obtained in experimental studies. The values of
R
2
in the optimum conditions of using GMDH, LM and SCG are 0.9997, 0.9991 and 0.9998, respectively. In addition, the MSE values of the mentioned methods are approximately 0.000010, 0.000032 and 0.0000078, respectively. |
| doi_str_mv | 10.1007/s10973-020-09541-x |
| format | Article |
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R
2
in the optimum conditions of using GMDH, LM and SCG are 0.9997, 0.9991 and 0.9998, respectively. In addition, the MSE values of the mentioned methods are approximately 0.000010, 0.000032 and 0.0000078, respectively.</description><identifier>ISSN: 1388-6150</identifier><identifier>EISSN: 1588-2926</identifier><identifier>DOI: 10.1007/s10973-020-09541-x</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Algorithms ; Analysis ; Analytical Chemistry ; Approximation ; Artificial neural networks ; Chemistry ; Chemistry and Materials Science ; Computational fluid dynamics ; Ethylene glycol ; Glycerin ; Glycerol ; Group method of data handling ; Heat conductivity ; Heat transfer ; Inorganic Chemistry ; Measurement Science and Instrumentation ; Model testing ; Multilayer perceptrons ; Nanofluids ; Nanoparticles ; Neural networks ; Physical Chemistry ; Polymer Sciences ; Silica ; Silicon dioxide ; Thermal conductivity</subject><ispartof>Journal of thermal analysis and calorimetry, 2021-05, Vol.144 (4), p.1613-1622</ispartof><rights>Akadémiai Kiadó, Budapest, Hungary 2020</rights><rights>COPYRIGHT 2021 Springer</rights><rights>Akadémiai Kiadó, Budapest, Hungary 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c495t-9c538aefcc82710bd88f51e7a2272d20857c0ab10e7eabac4eef0498dfade903</citedby><cites>FETCH-LOGICAL-c495t-9c538aefcc82710bd88f51e7a2272d20857c0ab10e7eabac4eef0498dfade903</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10973-020-09541-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10973-020-09541-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,777,781,27905,27906,41469,42538,51300</link.rule.ids></links><search><creatorcontrib>Maleki, Akbar</creatorcontrib><creatorcontrib>Haghighi, Arman</creatorcontrib><creatorcontrib>Irandoost Shahrestani, Misagh</creatorcontrib><creatorcontrib>Abdelmalek, Zahra</creatorcontrib><title>Applying different types of artificial neural network for modeling thermal conductivity of nanofluids containing silica particles</title><title>Journal of thermal analysis and calorimetry</title><addtitle>J Therm Anal Calorim</addtitle><description>Nanofluids are widely applicable in thermal devices with porous structures. Silica nanoparticles have been dispersed in different heat transfer fluids in order to increase their thermal conductivity and heat transfer capability. In this study, group method of data handling (GMDH) and multilayer perceptron artificial neural networks are applied for determining thermal conductivity of nanofluids with silica particles and different base fluids such as ethylene glycol, glycerol, water and ethylene glycol–water mixture. For cases with multilayer perceptron models, trained by applying scaled conjugate gradient (SCG) and Levenberg–Marquardt (LM) have been tested as two different training algorithms. The outputs of the applied models have good agreement with the values obtained in experimental studies. The values of
R
2
in the optimum conditions of using GMDH, LM and SCG are 0.9997, 0.9991 and 0.9998, respectively. 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Silica nanoparticles have been dispersed in different heat transfer fluids in order to increase their thermal conductivity and heat transfer capability. In this study, group method of data handling (GMDH) and multilayer perceptron artificial neural networks are applied for determining thermal conductivity of nanofluids with silica particles and different base fluids such as ethylene glycol, glycerol, water and ethylene glycol–water mixture. For cases with multilayer perceptron models, trained by applying scaled conjugate gradient (SCG) and Levenberg–Marquardt (LM) have been tested as two different training algorithms. The outputs of the applied models have good agreement with the values obtained in experimental studies. The values of
R
2
in the optimum conditions of using GMDH, LM and SCG are 0.9997, 0.9991 and 0.9998, respectively. In addition, the MSE values of the mentioned methods are approximately 0.000010, 0.000032 and 0.0000078, respectively.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s10973-020-09541-x</doi><tpages>10</tpages></addata></record> |
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| subjects | Algorithms Analysis Analytical Chemistry Approximation Artificial neural networks Chemistry Chemistry and Materials Science Computational fluid dynamics Ethylene glycol Glycerin Glycerol Group method of data handling Heat conductivity Heat transfer Inorganic Chemistry Measurement Science and Instrumentation Model testing Multilayer perceptrons Nanofluids Nanoparticles Neural networks Physical Chemistry Polymer Sciences Silica Silicon dioxide Thermal conductivity |
| title | Applying different types of artificial neural network for modeling thermal conductivity of nanofluids containing silica particles |
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