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Computational fluid dynamics and machine learning integration for optimizing natural convection inside a semi-annulus enclosure filled with hybrid nanofluid

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  • Baoyan Zhang
  • Yahya Rahmani

Abstract

To improve the efficiency of thermal systems, this study numerically simulates natural convection in a semi-annular enclosure using a CuO/TiO₂/water hybrid nanofluid (NF), relevant to practical thermal management systems. Computational fluid dynamics (CFD) results show optimal Nusselt number (Nu) improvements of 6.1% at Ra = 102 with 2% concentration, and up to 2.1% at higher Ra with 3%. An artificial neural network is integrated to predict Nu efficiently, identifying optimal concentration ratios of 0.52 and 0.83 at Ra = 104 and 106, respectively. The approach demonstrates the effectiveness of combining CFD and machine learning for enhancing and optimizing hybrid NF cooling systems.

Suggested Citation

  • Baoyan Zhang & Yahya Rahmani, 2025. "Computational fluid dynamics and machine learning integration for optimizing natural convection inside a semi-annulus enclosure filled with hybrid nanofluid," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 1571-1580.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:1571-1580.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctaf105
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