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Artificial neural network based finite element analysis of irregular heat sink/source effects on Boger nanofluid flow in a triangular enclosure with a heated cylindrical obstacle using the Cattaneo–Christov heat flux model

Author

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  • Raza, Qadeer
  • Li, Shuke
  • Wang, Xiaodong
  • Mushtaq, Tahir
  • Ali, Bagh
  • Shah, Nehad Ali

Abstract

This research focuses on enhancing heat transfer within triangular enclosures, a key requirement for effective thermal management in compact electronic systems and solar thermal devices. The study investigates the thermal behavior of Boger nanofluid flow around a centrally located cylindrical obstacle under different thermal boundary conditions (cold, hot, and adiabatic). The model integrates copper nanoparticles to improve thermal conductivity and accounts for the effects of magnetic fields, thermal radiation, Cattaneo-Christov heat flux, and non-uniform heat sources/sinks. The governing continuity, momentum, and energy equations are transformed into a nondimensional form using appropriate similarity variables. A hybrid approach is employed, combining the finite element method (FEM) for numerical simulation with an Artificial Neural Network (ANN) trained using the Levenberg–Marquardt algorithm for predictive modeling. Simulations are conducted to assess the impact of various parameters, such as the type of cylindrical obstacle (cold, heated, or adiabatic), solvent fraction (20.0≤β1≤60.0), relaxation time ratio (1.0≤β2≤5.0), magnetic field strength (1.0≤M≤5.0), Darcy number (103≤Ra≤104), Rayleigh number (10−2≤Da≤1.0), thermal radiation parameter (50.0≤Rd≤150.0), thermal relaxation coefficient (3≤γ≤9), and non-uniform heat source/sink (100.0≤A∗≤1.6,0.0≤B∗≤300.0). The results demonstrate that increasing the Darcy and Rayleigh numbers significantly enhances convective flow and thermal transport, while changes in solvent fraction and relaxation time ratio lead to contrasting effects on flow field ad temperature distribution. The findings have practical relevance in the design of high-efficiency thermal enclosures for microelectronic cooling and solar energy harvesting systems, where accurate temperature control is essential.

Suggested Citation

  • Raza, Qadeer & Li, Shuke & Wang, Xiaodong & Mushtaq, Tahir & Ali, Bagh & Shah, Nehad Ali, 2025. "Artificial neural network based finite element analysis of irregular heat sink/source effects on Boger nanofluid flow in a triangular enclosure with a heated cylindrical obstacle using the Cattaneo–Christov heat flux model," Chaos, Solitons & Fractals, Elsevier, vol. 200(P2).
    • Handle: RePEc:eee:chsofr:v:200:y:2025:i:p2:s0960077925010781
    DOI: 10.1016/j.chaos.2025.117065
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    References listed on IDEAS

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    1. Raza, Qadeer & Wang, Xiaodong & Ali, Bagh & Li, Shuke & Shah, Nehad Ali & Yang, Huizhu, 2025. "Computational study on entropy generation in Casson nanofluid flow with motile gyrotactic microorganisms using finite difference method," Chaos, Solitons & Fractals, Elsevier, vol. 190(C).
    2. Aissa Abderrahmane & Naef A. A. Qasem & Obai Younis & Riadh Marzouki & Abed Mourad & Nehad Ali Shah & Jae Dong Chung, 2022. "MHD Hybrid Nanofluid Mixed Convection Heat Transfer and Entropy Generation in a 3-D Triangular Porous Cavity with Zigzag Wall and Rotating Cylinder," Mathematics, MDPI, vol. 10(5), pages 1-18, February.
    3. Usman, M. & Khan, Z.H. & Liu, M.B., 2019. "MHD natural convection and thermal control inside a cavity with obstacles under the radiation effects," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
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