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<p>The primary goal of this study is to investigate the impact of SiO<sub>2</sub>-Fe<sub>3</sub>O<sub>4</sub> hybrid nanofluid flow on thermal performance as it passes through a circular cylinder and an octagonal cavity. There are several rectangular, heated fins on the inside of the hot cylinder. The effects of a uniform magnetic field, Darcy–Forchheimer porous resistance, and internal fin structures on heat transfer characteristics are examined, with a focus on thermal energy storage. The advanced Yamada–Ota and Xue models, which enhance heat transfer rates more effectively, are examined, and their outcomes are compared with those of previous research. The control volume finite element method (CVFEM) has been employed to numerically solve the governing equations. The present simulation captures these trends, with a notable 52.25% improvement in the local Nusselt number observed with the advanced Yamada thermal conductivity model. The artificial neural network has been used, which supports the simulation of the results obtained.</p>

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Performance evaluation of hybrid nanofluid flow in finned cylindrical systems for thermal energy storage using numerical CVFEM simulation with ANN

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月22日

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  • Taza Gul,
  • Saleem Nasir,
  • Abdallah Berrouk,
  • Wajdi Alghamdi,
  • Sultan Alghamdi,
  • Muhammad Sulaiman

摘要

The primary goal of this study is to investigate the impact of SiO2-Fe3O4 hybrid nanofluid flow on thermal performance as it passes through a circular cylinder and an octagonal cavity. There are several rectangular, heated fins on the inside of the hot cylinder. The effects of a uniform magnetic field, Darcy–Forchheimer porous resistance, and internal fin structures on heat transfer characteristics are examined, with a focus on thermal energy storage. The advanced Yamada–Ota and Xue models, which enhance heat transfer rates more effectively, are examined, and their outcomes are compared with those of previous research. The control volume finite element method (CVFEM) has been employed to numerically solve the governing equations. The present simulation captures these trends, with a notable 52.25% improvement in the local Nusselt number observed with the advanced Yamada thermal conductivity model. The artificial neural network has been used, which supports the simulation of the results obtained.