Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System
Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles...
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| Published in | Energy & fuels Vol. 36; no. 13; pp. 6626 - 6658 |
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| Main Authors | , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
American Chemical Society
07.07.2022
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required. |
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| AbstractList | Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required. |
| Author | Li, Changhe Le, Anh Tuan Hoang, Anh Tuan Said, Zafar Kumar, Anurag Tran, Viet Dung Nižetić, Sandro Nguyen, Xuan Phuong Pandey, Ashok Afzal, Asif Sharma, Prabhakar Huang, Zuohua |
| AuthorAffiliation | PATET Research Group U.S.-Pakistan Center for Advanced Studies in Energy (USPCAS-E) HUTECH University Centre for Energy and Environmental Sustainability Department of Sustainable and Renewable Energy Engineering National University of Sciences and Technology (NUST) School of Mechanical Engineering School of Engineering Sciences Sustainability Cluster, School of Engineering State Key Laboratory of Multiphase Flow in Power Engineering University of Petroleum and Energy Studies University of Split, FESB Institute of Engineering Department of Mechanical Engineering School of Mechanical and Automotive Engineering Delhi Skill and Entrepreneurship University Centre for Innovation and Translational Research |
| AuthorAffiliation_xml | – name: Centre for Innovation and Translational Research – name: University of Petroleum and Energy Studies – name: School of Mechanical and Automotive Engineering – name: HUTECH University – name: Delhi Skill and Entrepreneurship University – name: PATET Research Group – name: State Key Laboratory of Multiphase Flow in Power Engineering – name: University of Split, FESB – name: Department of Mechanical Engineering – name: School of Mechanical Engineering – name: Department of Sustainable and Renewable Energy Engineering – name: School of Engineering Sciences – name: Institute of Engineering – name: National University of Sciences and Technology (NUST) – name: U.S.-Pakistan Center for Advanced Studies in Energy (USPCAS-E) – name: Sustainability Cluster, School of Engineering – name: Centre for Energy and Environmental Sustainability |
| Author_xml | – sequence: 1 givenname: Prabhakar orcidid: 0000-0002-7585-6693 surname: Sharma fullname: Sharma, Prabhakar email: psharmahal@gmail.com organization: Delhi Skill and Entrepreneurship University – sequence: 2 givenname: Zafar surname: Said fullname: Said, Zafar email: zsaid@sharjah.ac.ae organization: National University of Sciences and Technology (NUST) – sequence: 3 givenname: Anurag surname: Kumar fullname: Kumar, Anurag organization: Delhi Skill and Entrepreneurship University – sequence: 4 givenname: Sandro surname: Nižetić fullname: Nižetić, Sandro organization: University of Split, FESB – sequence: 5 givenname: Ashok orcidid: 0000-0003-1626-3529 surname: Pandey fullname: Pandey, Ashok organization: Centre for Energy and Environmental Sustainability – sequence: 6 givenname: Anh Tuan orcidid: 0000-0002-1767-8040 surname: Hoang fullname: Hoang, Anh Tuan email: hatuan@hutech.edu.vn organization: HUTECH University – sequence: 7 givenname: Zuohua surname: Huang fullname: Huang, Zuohua organization: State Key Laboratory of Multiphase Flow in Power Engineering – sequence: 8 givenname: Asif surname: Afzal fullname: Afzal, Asif organization: Department of Mechanical Engineering – sequence: 9 givenname: Changhe surname: Li fullname: Li, Changhe organization: School of Mechanical and Automotive Engineering – sequence: 10 givenname: Anh Tuan orcidid: 0000-0003-4609-0382 surname: Le fullname: Le, Anh Tuan organization: School of Mechanical Engineering – sequence: 11 givenname: Xuan Phuong surname: Nguyen fullname: Nguyen, Xuan Phuong email: phuong@ut.edu.vn organization: PATET Research Group – sequence: 12 givenname: Viet Dung surname: Tran fullname: Tran, Viet Dung organization: PATET Research Group |
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| Snippet | Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a... |
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| SubjectTerms | algorithms artificial intelligence computer software energy ethics heat transfer nanofluids prediction swarms thermal conductivity |
| Title | Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System |
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