A Review of Artificial Intelligence Methods in Predicting Thermophysical Properties of Nanofluids for Heat Transfer Applications

Ankan Basu, Aritra Saha, Sumanta Banerjee, Prokash C. Roy and Balaram Kundu ()
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Ankan Basu: Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India
Aritra Saha: Department of Computer Science and Engineering, Heritage Institute of Technology, Kolkata 700107, India
Sumanta Banerjee: Department of Mechanical Engineering, Heritage Institute of Technology, Kolkata 700107, India
Prokash C. Roy: Department of Mechanical Engineering, Jadavpur University, Kolkata 700032, India
Balaram Kundu: Department of Mechanical Engineering, Jadavpur University, Kolkata 700032, India

Energies, 2024, vol. 17, issue 6, 1-31

Abstract: This present review explores the application of artificial intelligence (AI) methods in analysing the prediction of thermophysical properties of nanofluids. Nanofluids, colloidal solutions comprising nanoparticles dispersed in various base fluids, have received significant attention for their enhanced thermal properties and broad application in industries ranging from electronics cooling to renewable energy systems. In particular, nanofluids’ complexity and non-linear behaviour necessitate advanced predictive models in heat transfer applications. The AI techniques, which include genetic algorithms (GAs) and machine learning (ML) methods, have emerged as powerful tools to address these challenges and offer novel alternatives to traditional mathematical and physical models. Artificial Neural Networks (ANNs) and other AI algorithms are highlighted for their capacity to process large datasets and identify intricate patterns, thereby proving effective in predicting nanofluid thermophysical properties (e.g., thermal conductivity and specific heat capacity). This review paper presents a comprehensive overview of various published studies devoted to the thermal behaviour of nanofluids, where AI methods (like ANNs, support vector regression (SVR), and genetic algorithms) are employed to enhance the accuracy of predictions of their thermophysical properties. The reviewed works conclusively demonstrate the superiority of AI models over the classical approaches, emphasizing the role of AI in advancing research for nanofluids used in heat transfer applications.

Keywords: nanofluid; machine learning; heat transfer augmentation; viscosity; thermal conductivity; specific heat capacity (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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