Effect of particle size on second law of thermodynamics analysis of Al2O3 nanofluid: Application of XGBoost and gradient boosting regression for prognostic analysis
In this study, the current research delves into the influence of nanoparticle size on turbulent forced convective heat transfer, entropy generation, and friction factor. The investigation focused on three different sizes (30 nm, 50 nm, and 80 nm) of Al2O3 nanoparticles (NPs) suspended in a water-bas...
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my.uniten.dspace-367762025-03-03T15:44:35Z Effect of particle size on second law of thermodynamics analysis of Al2O3 nanofluid: Application of XGBoost and gradient boosting regression for prognostic analysis Kumar K P. Alruqi M. Hanafi H.A. Sharma P. Wanatasanappan V.V. 58803258700 57225072010 36772441100 58961316700 57217224948 Adaptive boosting Alumina Aluminum oxide Entropy Factor analysis Friction Heat convection Machine learning Nanofluidics Nanoparticles Particle size Particle size analysis Regression analysis Reynolds number Specific heat Thermal conductivity of liquids Entropy generation Evacuated tubes Friction factors Gradient boosting Nanofluids Optimisations Particles sizes Renewable energies Second Law of Thermodynamics Thermo dynamic analysis Solar energy In this study, the current research delves into the influence of nanoparticle size on turbulent forced convective heat transfer, entropy generation, and friction factor. The investigation focused on three different sizes (30 nm, 50 nm, and 80 nm) of Al2O3 nanoparticles (NPs) suspended in a water-based nanofluid (NF) with a 1 vol% concentration flow in a circular tube. The nanoparticles (NPs) were characterized using various characterization techniques. The stability and pH of the NF were determined, and its viscosity (VST) and thermal conductivity (TC) were measured at a temperature of 60 �C. Heat transfer experiments were conducted with varying particle sizes and Reynolds number (Re), maintaining a fluid inlet temperature of 60 �C. The results indicated that the NF containing 30 nm particles exhibited higher VST and TC compared to the other samples and the base fluid. The maximum enhancement in Nu for Al2O3 (30 nm) and Al2O3 (80 nm) NFs is 60.7 and 18.5 % greater than that of base fluid, respectively. The maximum and minimum total entropy generation (Sgen,T) value of 0.499 and 0.286 observed for base fluid and Al2O3 NF (30 nm), respectively at low Re. The highest friction factor enhancement for Al2O3 NF (30 nm) exceeded by 9.4 % compared to the base fluid, and the maximum thermal performance factor observed for Al2O3 NF (30 nm) was 1.57. Finally, regression analysis was employed to establish correlations for estimating Nu and friction factor values. Prognostic models were developed using two sophisticated machine learning algorithms, XGBoost and Gradient Boosting Regression (GBR). Both models demonstrated exceptional prediction abilities, achieving over 99 % accuracy rates based on the experimental data. ? 2023 Elsevier Masson SAS Final 2025-03-03T07:44:35Z 2025-03-03T07:44:35Z 2024 Article 10.1016/j.ijthermalsci.2023.108825 2-s2.0-85179841641 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179841641&doi=10.1016%2fj.ijthermalsci.2023.108825&partnerID=40&md5=8883a2b212f58aa29b07e063b20a3766 https://irepository.uniten.edu.my/handle/123456789/36776 197 108825 Elsevier Masson s.r.l. Scopus |
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Adaptive boosting Alumina Aluminum oxide Entropy Factor analysis Friction Heat convection Machine learning Nanofluidics Nanoparticles Particle size Particle size analysis Regression analysis Reynolds number Specific heat Thermal conductivity of liquids Entropy generation Evacuated tubes Friction factors Gradient boosting Nanofluids Optimisations Particles sizes Renewable energies Second Law of Thermodynamics Thermo dynamic analysis Solar energy |
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Adaptive boosting Alumina Aluminum oxide Entropy Factor analysis Friction Heat convection Machine learning Nanofluidics Nanoparticles Particle size Particle size analysis Regression analysis Reynolds number Specific heat Thermal conductivity of liquids Entropy generation Evacuated tubes Friction factors Gradient boosting Nanofluids Optimisations Particles sizes Renewable energies Second Law of Thermodynamics Thermo dynamic analysis Solar energy Kumar K P. Alruqi M. Hanafi H.A. Sharma P. Wanatasanappan V.V. Effect of particle size on second law of thermodynamics analysis of Al2O3 nanofluid: Application of XGBoost and gradient boosting regression for prognostic analysis |
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In this study, the current research delves into the influence of nanoparticle size on turbulent forced convective heat transfer, entropy generation, and friction factor. The investigation focused on three different sizes (30 nm, 50 nm, and 80 nm) of Al2O3 nanoparticles (NPs) suspended in a water-based nanofluid (NF) with a 1 vol% concentration flow in a circular tube. The nanoparticles (NPs) were characterized using various characterization techniques. The stability and pH of the NF were determined, and its viscosity (VST) and thermal conductivity (TC) were measured at a temperature of 60 �C. Heat transfer experiments were conducted with varying particle sizes and Reynolds number (Re), maintaining a fluid inlet temperature of 60 �C. The results indicated that the NF containing 30 nm particles exhibited higher VST and TC compared to the other samples and the base fluid. The maximum enhancement in Nu for Al2O3 (30 nm) and Al2O3 (80 nm) NFs is 60.7 and 18.5 % greater than that of base fluid, respectively. The maximum and minimum total entropy generation (Sgen,T) value of 0.499 and 0.286 observed for base fluid and Al2O3 NF (30 nm), respectively at low Re. The highest friction factor enhancement for Al2O3 NF (30 nm) exceeded by 9.4 % compared to the base fluid, and the maximum thermal performance factor observed for Al2O3 NF (30 nm) was 1.57. Finally, regression analysis was employed to establish correlations for estimating Nu and friction factor values. Prognostic models were developed using two sophisticated machine learning algorithms, XGBoost and Gradient Boosting Regression (GBR). Both models demonstrated exceptional prediction abilities, achieving over 99 % accuracy rates based on the experimental data. ? 2023 Elsevier Masson SAS |
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58803258700 |
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58803258700 Kumar K P. Alruqi M. Hanafi H.A. Sharma P. Wanatasanappan V.V. |
| format |
Article |
| author |
Kumar K P. Alruqi M. Hanafi H.A. Sharma P. Wanatasanappan V.V. |
| author_sort |
Kumar K P. |
| title |
Effect of particle size on second law of thermodynamics analysis of Al2O3 nanofluid: Application of XGBoost and gradient boosting regression for prognostic analysis |
| title_short |
Effect of particle size on second law of thermodynamics analysis of Al2O3 nanofluid: Application of XGBoost and gradient boosting regression for prognostic analysis |
| title_full |
Effect of particle size on second law of thermodynamics analysis of Al2O3 nanofluid: Application of XGBoost and gradient boosting regression for prognostic analysis |
| title_fullStr |
Effect of particle size on second law of thermodynamics analysis of Al2O3 nanofluid: Application of XGBoost and gradient boosting regression for prognostic analysis |
| title_full_unstemmed |
Effect of particle size on second law of thermodynamics analysis of Al2O3 nanofluid: Application of XGBoost and gradient boosting regression for prognostic analysis |
| title_sort |
effect of particle size on second law of thermodynamics analysis of al2o3 nanofluid: application of xgboost and gradient boosting regression for prognostic analysis |
| publisher |
Elsevier Masson s.r.l. |
| publishDate |
2025 |
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1825816244843970560 |
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13.244413 |