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

Baoyan Zhang and Yahya Rahmani

International Journal of Low-Carbon Technologies, 2025, vol. 20, 1571-1580

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.

Keywords: natural convection mechanism; hybrid nanofluid; semi-annulus enclosure; machine learning; optimization (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1093/ijlct/ctaf105 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:20:y:2025:i::p:1571-1580.

Access Statistics for this article

International Journal of Low-Carbon Technologies is currently edited by Saffa B. Riffat

More articles in International Journal of Low-Carbon Technologies from Oxford University Press
Bibliographic data for series maintained by Oxford University Press ().