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<p>Nanofluid-charged thermal systems are emerging as promising technologies for various industrial applications. The nanofluid has shown potential to enhance energy efficiency and thermal performance of solar thermal systems. The nonlinear behavior of nanofluid thermophysical properties poses significant challenges for system design and optimization. This review explores the integration of machine learning (ML) techniques to address these complexities and enhance the efficacy of nanofluid-charged solar thermal systems. A comprehensive assessment of recent literature highlights advancements in stable nanofluid preparation, thermophysical property prediction, stability enhancement and solar collector performance prediction. Advanced and hybrid ML algorithms are evaluated for their ability to model and predict complex behaviors, reducing predictive errors in heat transfer, efficiency, and temperature distribution estimations by 15–30% compared to traditional methods. Furthermore, ML-driven models enhance system adaptability and enable real-time control, improving efficiency by 10–20% under varying conditions. The study also reveals that ML reduces reliance on experimental testing by up to 40%, significantly cutting costs and accelerating development cycles. This review paper provides a roadmap for future research aimed at optimizing nanofluid-charged solar thermal systems, ultimately contributing to more efficient, scalable, and adaptable renewable energy solutions by identifying key ML models and strategies.</p>

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The role of machine learning in optimizing nanofluid-driven solar thermal technologies: a state-of-the-art review

  • 期刊论文

  • 发表日期：

    2025年7月1日

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  • Kishor Deshmukh,
  • Amrut Aher,
  • Vipin Sharma

摘要

Nanofluid-charged thermal systems are emerging as promising technologies for various industrial applications. The nanofluid has shown potential to enhance energy efficiency and thermal performance of solar thermal systems. The nonlinear behavior of nanofluid thermophysical properties poses significant challenges for system design and optimization. This review explores the integration of machine learning (ML) techniques to address these complexities and enhance the efficacy of nanofluid-charged solar thermal systems. A comprehensive assessment of recent literature highlights advancements in stable nanofluid preparation, thermophysical property prediction, stability enhancement and solar collector performance prediction. Advanced and hybrid ML algorithms are evaluated for their ability to model and predict complex behaviors, reducing predictive errors in heat transfer, efficiency, and temperature distribution estimations by 15–30% compared to traditional methods. Furthermore, ML-driven models enhance system adaptability and enable real-time control, improving efficiency by 10–20% under varying conditions. The study also reveals that ML reduces reliance on experimental testing by up to 40%, significantly cutting costs and accelerating development cycles. This review paper provides a roadmap for future research aimed at optimizing nanofluid-charged solar thermal systems, ultimately contributing to more efficient, scalable, and adaptable renewable energy solutions by identifying key ML models and strategies.