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<p>Advanced numerical&#xa0;heat transport applications have great potential when it comes to thermal performance. The optimal thermal performance is employed to maximize energy efficiency, improve system dependability, and pave the way for more sustainable and effective heat exchange systems across a range of technical fields. This study predicts the thermal performance of magnetized ternary hybrid cross bio-nanofluid over permeable cylinder using artificial neural network (ANN) simulation. Blood fluid is considered as base fluid, and three nanoparticles copper oxide (CuO), titanium dioxide (TiO<sub>2</sub>), and silicon oxide (SiO<sub>2</sub>) are included in base fluid. The cross fluid model’s thermal performance is examined using the variable thermal conductivity. By making assumptions about the problem, partial differential equations (PDEs) develop, which are then transformed into ordinary differential equations (ODEs) using a similarity scheme. Following linearization, the numerical solution is obtained using the bvp4c approach, and the numerical data are subsequently trained using the artificial neural network scheme. Moreover, the ANN technique is employed to predict the ODE solutions by maintaining ten neurons and one hidden layer. It is observed that the increasing thermal conductivity parameter, ternary hybrid nanofluid thermal transport is found to rise in the flow over a permeable cylinder. Using artificial neural network simulation, this work is improved and validated.</p>

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Neural network framework for thermal performance of cross hybrid bio-nanofluid flow over a permeable cylinder subject to variable thermal conductivity

  • 期刊论文

  • 发表日期：

    2025年8月10日

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  • Huiying Xu,
  • Assad Ayub,
  • Zahoor Iqbal,
  • Syed Zahir Hussain Shah,
  • Xinzhong Zhu,
  • M. M. Alqarni,
  • Ridha Selmi,
  • Fahima Hajjej

摘要

Advanced numerical heat transport applications have great potential when it comes to thermal performance. The optimal thermal performance is employed to maximize energy efficiency, improve system dependability, and pave the way for more sustainable and effective heat exchange systems across a range of technical fields. This study predicts the thermal performance of magnetized ternary hybrid cross bio-nanofluid over permeable cylinder using artificial neural network (ANN) simulation. Blood fluid is considered as base fluid, and three nanoparticles copper oxide (CuO), titanium dioxide (TiO2), and silicon oxide (SiO2) are included in base fluid. The cross fluid model’s thermal performance is examined using the variable thermal conductivity. By making assumptions about the problem, partial differential equations (PDEs) develop, which are then transformed into ordinary differential equations (ODEs) using a similarity scheme. Following linearization, the numerical solution is obtained using the bvp4c approach, and the numerical data are subsequently trained using the artificial neural network scheme. Moreover, the ANN technique is employed to predict the ODE solutions by maintaining ten neurons and one hidden layer. It is observed that the increasing thermal conductivity parameter, ternary hybrid nanofluid thermal transport is found to rise in the flow over a permeable cylinder. Using artificial neural network simulation, this work is improved and validated.