Nanofluid heat transfer and machine learning: Insightful review of machine learning for nanofluid heat transfer enhancement in porous media and heat exchangers as sustainable and renewable energy solutions
Nanofluid, coupled with machine learning, is at the forefront of cutting-edge research in sustainable and renewable energy sector. This review paper examines the latest developments in the intersection of nanofluid and machine learning for heat transfer enhancement. This hybrid nanofluid-machine lea...
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my.uniten.dspace-361012025-03-03T15:41:23Z Nanofluid heat transfer and machine learning: Insightful review of machine learning for nanofluid heat transfer enhancement in porous media and heat exchangers as sustainable and renewable energy solutions Riyadi T.W.B. Herawan S.G. Tirta A. Ee Y.J. Hananto A.L. Paristiawan P.A. Yusuf A.A. Venu H. Irianto Veza I. 55930344300 17434542800 36723162300 57216252884 57444363400 57232788700 57204941899 57189525542 58947149800 57205548894 Nanofluidics Transfer learning Heat Transfer enhancement Heat-transfer characteristics Hybrid nanofluid Hybrid nanofluid heat transfer enhancement Hybrid nanofluid-machine learning heat transfer Machine learning for sustainable and renewable energy Machine-learning Nanofluid heat transfer characteristic and enhancement Nanofluid heat transfer numerical study Nanofluid-machine learning heat transfer enhancement Nanofluids Renewable energies Sustainable energy Support vector machines Nanofluid, coupled with machine learning, is at the forefront of cutting-edge research in sustainable and renewable energy sector. This review paper examines the latest developments in the intersection of nanofluid and machine learning for heat transfer enhancement. This hybrid nanofluid-machine learning review investigates nanofluid heat transfer enhancement leveraged by machine learning both in porous media as well as heat exchangers. Several studies in porous media nanofluid transport utilize advanced methodologies that integrate machine learning and computational techniques. Machine learning and computational methods are employed to tackle complex thermodynamics, transport processes, and heat transfer challenges in complex multiphysics systems. An interesting hybrid nanofluid-machine learning application involves applying a machine learning method such as Support Vector Machine (SVM) to forecast movement of hybrid nanofluid flows across porous surfaces. Such hybrid nanofluid-machine learning technique involves utilising training data obtained from computational fluid dynamics (CFD) to decrease computational time and expenses. Machine learning offers a more efficient and cost-effective modelling for nanofluid heat transfer enhancement. Techniques such as scanning electron microscopy (SEM) along with X-ray diffraction (XRD) are also often used for assessing the forms as well as nanocomposites configurations in heat exchangers while studying nanofluids. The importance of machine learning models, especially artificial neural networks (ANNs) and genetic algorithms, is evident in their ability to predict and optimize thermal performance of nanofluid application for nanofluid heat transfer enhancement. Furthermore, integrating nanofluids into various heat exchanger designs has demonstrated significant enhancements in efficiency, decreased energy usage, and total cost reduction. These achievements align with the research goal in sustainable and renewable energy, highlighting the critical role of nanofluid-enhanced heat exchange systems in tackling current difficulties related to energy efficiency and sustainability. Overall, combining nanofluids with machine learning shows promising advancements, providing a route toward creating more efficient and eco-friendly heat exchange systems. ? 2024 The Authors Final 2025-03-03T07:41:23Z 2025-03-03T07:41:23Z 2024 Review 10.1016/j.rineng.2024.103002 2-s2.0-85205355348 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205355348&doi=10.1016%2fj.rineng.2024.103002&partnerID=40&md5=18b18b4946b9f0378c67f358f6602605 https://irepository.uniten.edu.my/handle/123456789/36101 24 103002 All Open Access; Gold Open Access Elsevier B.V. Scopus |
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Nanofluidics Transfer learning Heat Transfer enhancement Heat-transfer characteristics Hybrid nanofluid Hybrid nanofluid heat transfer enhancement Hybrid nanofluid-machine learning heat transfer Machine learning for sustainable and renewable energy Machine-learning Nanofluid heat transfer characteristic and enhancement Nanofluid heat transfer numerical study Nanofluid-machine learning heat transfer enhancement Nanofluids Renewable energies Sustainable energy Support vector machines |
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Nanofluidics Transfer learning Heat Transfer enhancement Heat-transfer characteristics Hybrid nanofluid Hybrid nanofluid heat transfer enhancement Hybrid nanofluid-machine learning heat transfer Machine learning for sustainable and renewable energy Machine-learning Nanofluid heat transfer characteristic and enhancement Nanofluid heat transfer numerical study Nanofluid-machine learning heat transfer enhancement Nanofluids Renewable energies Sustainable energy Support vector machines Riyadi T.W.B. Herawan S.G. Tirta A. Ee Y.J. Hananto A.L. Paristiawan P.A. Yusuf A.A. Venu H. Irianto Veza I. Nanofluid heat transfer and machine learning: Insightful review of machine learning for nanofluid heat transfer enhancement in porous media and heat exchangers as sustainable and renewable energy solutions |
| description |
Nanofluid, coupled with machine learning, is at the forefront of cutting-edge research in sustainable and renewable energy sector. This review paper examines the latest developments in the intersection of nanofluid and machine learning for heat transfer enhancement. This hybrid nanofluid-machine learning review investigates nanofluid heat transfer enhancement leveraged by machine learning both in porous media as well as heat exchangers. Several studies in porous media nanofluid transport utilize advanced methodologies that integrate machine learning and computational techniques. Machine learning and computational methods are employed to tackle complex thermodynamics, transport processes, and heat transfer challenges in complex multiphysics systems. An interesting hybrid nanofluid-machine learning application involves applying a machine learning method such as Support Vector Machine (SVM) to forecast movement of hybrid nanofluid flows across porous surfaces. Such hybrid nanofluid-machine learning technique involves utilising training data obtained from computational fluid dynamics (CFD) to decrease computational time and expenses. Machine learning offers a more efficient and cost-effective modelling for nanofluid heat transfer enhancement. Techniques such as scanning electron microscopy (SEM) along with X-ray diffraction (XRD) are also often used for assessing the forms as well as nanocomposites configurations in heat exchangers while studying nanofluids. The importance of machine learning models, especially artificial neural networks (ANNs) and genetic algorithms, is evident in their ability to predict and optimize thermal performance of nanofluid application for nanofluid heat transfer enhancement. Furthermore, integrating nanofluids into various heat exchanger designs has demonstrated significant enhancements in efficiency, decreased energy usage, and total cost reduction. These achievements align with the research goal in sustainable and renewable energy, highlighting the critical role of nanofluid-enhanced heat exchange systems in tackling current difficulties related to energy efficiency and sustainability. Overall, combining nanofluids with machine learning shows promising advancements, providing a route toward creating more efficient and eco-friendly heat exchange systems. ? 2024 The Authors |
| author2 |
55930344300 |
| author_facet |
55930344300 Riyadi T.W.B. Herawan S.G. Tirta A. Ee Y.J. Hananto A.L. Paristiawan P.A. Yusuf A.A. Venu H. Irianto Veza I. |
| format |
Review |
| author |
Riyadi T.W.B. Herawan S.G. Tirta A. Ee Y.J. Hananto A.L. Paristiawan P.A. Yusuf A.A. Venu H. Irianto Veza I. |
| author_sort |
Riyadi T.W.B. |
| title |
Nanofluid heat transfer and machine learning: Insightful review of machine learning for nanofluid heat transfer enhancement in porous media and heat exchangers as sustainable and renewable energy solutions |
| title_short |
Nanofluid heat transfer and machine learning: Insightful review of machine learning for nanofluid heat transfer enhancement in porous media and heat exchangers as sustainable and renewable energy solutions |
| title_full |
Nanofluid heat transfer and machine learning: Insightful review of machine learning for nanofluid heat transfer enhancement in porous media and heat exchangers as sustainable and renewable energy solutions |
| title_fullStr |
Nanofluid heat transfer and machine learning: Insightful review of machine learning for nanofluid heat transfer enhancement in porous media and heat exchangers as sustainable and renewable energy solutions |
| title_full_unstemmed |
Nanofluid heat transfer and machine learning: Insightful review of machine learning for nanofluid heat transfer enhancement in porous media and heat exchangers as sustainable and renewable energy solutions |
| title_sort |
nanofluid heat transfer and machine learning: insightful review of machine learning for nanofluid heat transfer enhancement in porous media and heat exchangers as sustainable and renewable energy solutions |
| publisher |
Elsevier B.V. |
| publishDate |
2025 |
| _version_ |
1825816262638305280 |
| score |
13.244109 |