An intelligent approach to predicting the effect of nanoparticle mixture ratio, concentration and temperature on thermal conductivity of hybrid nanofluids
<p dir="ltr">Hybrid nanofluids are better heat transfer fluids than conventional nanofluids because of the combined properties of two or more nanoparticles. In this study, the thermal conductivity of Al<sub>2</sub>O<sub>3</sub>–ZnO nanoparticles suspended in a...
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2020
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| _version_ | 1864513567589400576 |
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| author | Ifeoluwa Wole-Osho (14151315) |
| author2 | Eric C. Okonkwo (14151060) Humphery Adun (14151318) Doga Kavaz (2050357) Serkan Abbasoglu (14151063) |
| author2_role | author author author author |
| author_facet | Ifeoluwa Wole-Osho (14151315) Eric C. Okonkwo (14151060) Humphery Adun (14151318) Doga Kavaz (2050357) Serkan Abbasoglu (14151063) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ifeoluwa Wole-Osho (14151315) Eric C. Okonkwo (14151060) Humphery Adun (14151318) Doga Kavaz (2050357) Serkan Abbasoglu (14151063) |
| dc.date.none.fl_str_mv | 2020-03-29T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s10973-020-09594-y |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/An_intelligent_approach_to_predicting_the_effect_of_nanoparticle_mixture_ratio_concentration_and_temperature_on_thermal_conductivity_of_hybrid_nanofluids/21597375 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Fluid mechanics and thermal engineering Materials engineering Nanotechnology Nanoparticles Alumina Zinc oxide Hybrid nanofluids Thermal conductivity |
| dc.title.none.fl_str_mv | An intelligent approach to predicting the effect of nanoparticle mixture ratio, concentration and temperature on thermal conductivity of hybrid nanofluids |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Hybrid nanofluids are better heat transfer fluids than conventional nanofluids because of the combined properties of two or more nanoparticles. In this study, the thermal conductivity of Al<sub>2</sub>O<sub>3</sub>–ZnO nanoparticles suspended in a base fluid of distilled water is investigated. The experiments were conducted for three mixture ratios (1:2, 1:1 and 2:1) of Al<sub>2</sub>O<sub>3</sub>–ZnO nanofluid at five different volume concentrations of 0.33%, 0.67%, 1.0%, 1.33% and 1.67%. X-ray diffractometric analysis, X-ray fluorescence spectrometry and scanning electron microscopy were used to characterise the nanoparticles. The highest thermal conductivity enhancement achieved for Al2O3–ZnO hybrid nanofluids with 1:2, 1:1 and 2:1 (Al<sub>2</sub>O<sub>3</sub>:ZnO) mixture ratios was 36%, 35% and 40%, respectively, at volume concentration 1.67%. The study observed the highest thermal conductivity for Al<sub>2</sub>O<sub>3</sub>–ZnO nanofluid was achieved at a mixture ratio of 2:1. A “deeping” effect was observed at a mixture ratio of 1:1 representing the lowest value of thermal conductivity within the considered range. The study proposed and compared three models for obtaining the thermal conductivity of Al<sub>2</sub>O<sub>3</sub>–ZnO nanofluids based on temperature, volume concentration and nanoparticle mixture ratio. A polynomial correlation model, the adaptive neuro-fuzzy inference system model and an artificial neural network model optimised with three different learning algorithms. The adaptive neuro-fuzzy inference system model was most accurate in forecasting the thermal conductivity of the Al<sub>2</sub>O<sub>3</sub>–ZnO hybrid nanofluid with an R<sup>2</sup> value of 0.9946.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Thermal Analysis and Calorimetry<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1007/s10973-020-09594-y" target="_blank">http://dx.doi.org/10.1007/s10973-020-09594-y</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_308f7a634bc6fdfc33761ab539a8bd93 |
| identifier_str_mv | 10.1007/s10973-020-09594-y |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/21597375 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | An intelligent approach to predicting the effect of nanoparticle mixture ratio, concentration and temperature on thermal conductivity of hybrid nanofluidsIfeoluwa Wole-Osho (14151315)Eric C. Okonkwo (14151060)Humphery Adun (14151318)Doga Kavaz (2050357)Serkan Abbasoglu (14151063)EngineeringFluid mechanics and thermal engineeringMaterials engineeringNanotechnologyNanoparticlesAluminaZinc oxideHybrid nanofluidsThermal conductivity<p dir="ltr">Hybrid nanofluids are better heat transfer fluids than conventional nanofluids because of the combined properties of two or more nanoparticles. In this study, the thermal conductivity of Al<sub>2</sub>O<sub>3</sub>–ZnO nanoparticles suspended in a base fluid of distilled water is investigated. The experiments were conducted for three mixture ratios (1:2, 1:1 and 2:1) of Al<sub>2</sub>O<sub>3</sub>–ZnO nanofluid at five different volume concentrations of 0.33%, 0.67%, 1.0%, 1.33% and 1.67%. X-ray diffractometric analysis, X-ray fluorescence spectrometry and scanning electron microscopy were used to characterise the nanoparticles. The highest thermal conductivity enhancement achieved for Al2O3–ZnO hybrid nanofluids with 1:2, 1:1 and 2:1 (Al<sub>2</sub>O<sub>3</sub>:ZnO) mixture ratios was 36%, 35% and 40%, respectively, at volume concentration 1.67%. The study observed the highest thermal conductivity for Al<sub>2</sub>O<sub>3</sub>–ZnO nanofluid was achieved at a mixture ratio of 2:1. A “deeping” effect was observed at a mixture ratio of 1:1 representing the lowest value of thermal conductivity within the considered range. The study proposed and compared three models for obtaining the thermal conductivity of Al<sub>2</sub>O<sub>3</sub>–ZnO nanofluids based on temperature, volume concentration and nanoparticle mixture ratio. A polynomial correlation model, the adaptive neuro-fuzzy inference system model and an artificial neural network model optimised with three different learning algorithms. The adaptive neuro-fuzzy inference system model was most accurate in forecasting the thermal conductivity of the Al<sub>2</sub>O<sub>3</sub>–ZnO hybrid nanofluid with an R<sup>2</sup> value of 0.9946.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Thermal Analysis and Calorimetry<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1007/s10973-020-09594-y" target="_blank">http://dx.doi.org/10.1007/s10973-020-09594-y</a></p>2020-03-29T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s10973-020-09594-yhttps://figshare.com/articles/journal_contribution/An_intelligent_approach_to_predicting_the_effect_of_nanoparticle_mixture_ratio_concentration_and_temperature_on_thermal_conductivity_of_hybrid_nanofluids/21597375CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/215973752020-03-29T09:00:00Z |
| spellingShingle | An intelligent approach to predicting the effect of nanoparticle mixture ratio, concentration and temperature on thermal conductivity of hybrid nanofluids Ifeoluwa Wole-Osho (14151315) Engineering Fluid mechanics and thermal engineering Materials engineering Nanotechnology Nanoparticles Alumina Zinc oxide Hybrid nanofluids Thermal conductivity |
| status_str | publishedVersion |
| title | An intelligent approach to predicting the effect of nanoparticle mixture ratio, concentration and temperature on thermal conductivity of hybrid nanofluids |
| title_full | An intelligent approach to predicting the effect of nanoparticle mixture ratio, concentration and temperature on thermal conductivity of hybrid nanofluids |
| title_fullStr | An intelligent approach to predicting the effect of nanoparticle mixture ratio, concentration and temperature on thermal conductivity of hybrid nanofluids |
| title_full_unstemmed | An intelligent approach to predicting the effect of nanoparticle mixture ratio, concentration and temperature on thermal conductivity of hybrid nanofluids |
| title_short | An intelligent approach to predicting the effect of nanoparticle mixture ratio, concentration and temperature on thermal conductivity of hybrid nanofluids |
| title_sort | An intelligent approach to predicting the effect of nanoparticle mixture ratio, concentration and temperature on thermal conductivity of hybrid nanofluids |
| topic | Engineering Fluid mechanics and thermal engineering Materials engineering Nanotechnology Nanoparticles Alumina Zinc oxide Hybrid nanofluids Thermal conductivity |