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Machine Learning Approach for Thermal Characteristics and Improvement of Heat Transfer of Nanofluids—A Review

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Machine Intelligence for Research and Innovations (MAiTRI 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 831))

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Abstract

Machine learning (ML) provides an array of methodologies for understanding many challenging obstacles in multiple domains, mostly the discipline of heat transfer and thermal systems. In nanofluid investigations about thermophysical attribute prediction and thermo-hydrodynamic performance testing, machine learning methods would be particularly helpful. The goal of the current review is to examine the advantages and uses of various machine learning algorithms applied in nanofluid-based vapour compression refrigeration systems and to highlight recent advancements in this field. The rapid development of nanofluids is being driven by a record amount of data from experiments, field observations, and extensive simulations at multiple space and time scales. There are many ways to collect data-related information from machine learning that can be used to comprehend the use of nanofluids. The importance of ML in assessing the thermophysical characteristics of nanofluid is in-depth highlighted. The forecasting of thermophysical characteristics and heat transfer rate using data-driven machine learning techniques will be helpful in finding the most important aspects in nanofluid research.

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Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Dr. B.R Ambedkar National Institute of Technology, Jalandhar, India

    Harishchandra Patel, Harishchandra Patel & Dwesh Kumar Singh

  2. Department of Instrumentation and Control Engineering, Dr. B.R Ambedkar National Institute of Technology, Jalandhar, India

    Om Prakash Verma

  3. Joint Department of Biomedical Engineering at the University of North Carolina and North Carolina State University, Raleigh, NC, USA

    Sachin Kadian

Authors
  1. Harishchandra Patel
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  2. Dwesh Kumar Singh
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  3. Om Prakash Verma
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  4. Sachin Kadian
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Corresponding author

Correspondence to Harishchandra Patel .

Editor information

Editors and Affiliations

  1. Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Jalandhar, Punjab, India

    Om Prakash Verma

  2. School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

  3. Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Rajesh Kumar

  4. Department of Mathematics, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Jalandhar, Punjab, India

    Anupam Yadav

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Patel, H., Kumar Singh, D., Prakash Verma, O., Kadian, S. (2024). Machine Learning Approach for Thermal Characteristics and Improvement of Heat Transfer of Nanofluids—A Review. In: Verma, O.P., Wang, L., Kumar, R., Yadav, A. (eds) Machine Intelligence for Research and Innovations. MAiTRI 2023. Lecture Notes in Networks and Systems, vol 831. Springer, Singapore. https://doi.org/10.1007/978-981-99-8135-9_20

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