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<p>This study investigates triple diffusion in nano-encapsulated phase change materials (NEPCMs) inside inclined containers, considering the effects of dual activation energies and two-dimensional thermal radiation. The analysis focuses on ternary convection driven by species concentrations of hydrogen and nitrogen, with the heat capacity of the suspension determined by the core–shell material properties. The governing equations were solved numerically using the finite volume method. Key physical parameters were optimized using Response Surface Methodology (RSM). Also, using the artificial neural network predictions gives value by offering accurate and efficient forecasting of heat and mass transfer rates, allowing for the rapid evaluation of different operating conditions. This enables the identification of optimal parameters (such as radiation, buoyancy ratio and chemical reaction parameters) without the need for extensive physical experimentation. By learning complex, nonlinear relationships from the numerical data, the ANN model can predict system behavior under varying conditions, significantly reducing computation time and improving the reliability of the results. The results revealed that the optimal heat transfer rate occurs at fusion temperature, radiation coefficient, and inclination angle values of 0.56994, 5, and 0°, respectively. Additionally, an increase in fusion temperature shifts the latent heat required for phase change, affecting the effective heat capacity ratio. Furthermore, the average heat transfer rate improves by up to 71% as the radiation coefficient increases from 0 to 5.</p>

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Multi-diffusion in an inclined chamber with nano-encapsulated phase change materials: optimization and neural network-based prediction

  • 期刊论文

  • 发表日期：

    2025年5月16日

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  • Z. Z. Rashed,
  • Sameh E. Ahmed,
  • Muflih Alhazmi

摘要

This study investigates triple diffusion in nano-encapsulated phase change materials (NEPCMs) inside inclined containers, considering the effects of dual activation energies and two-dimensional thermal radiation. The analysis focuses on ternary convection driven by species concentrations of hydrogen and nitrogen, with the heat capacity of the suspension determined by the core–shell material properties. The governing equations were solved numerically using the finite volume method. Key physical parameters were optimized using Response Surface Methodology (RSM). Also, using the artificial neural network predictions gives value by offering accurate and efficient forecasting of heat and mass transfer rates, allowing for the rapid evaluation of different operating conditions. This enables the identification of optimal parameters (such as radiation, buoyancy ratio and chemical reaction parameters) without the need for extensive physical experimentation. By learning complex, nonlinear relationships from the numerical data, the ANN model can predict system behavior under varying conditions, significantly reducing computation time and improving the reliability of the results. The results revealed that the optimal heat transfer rate occurs at fusion temperature, radiation coefficient, and inclination angle values of 0.56994, 5, and 0°, respectively. Additionally, an increase in fusion temperature shifts the latent heat required for phase change, affecting the effective heat capacity ratio. Furthermore, the average heat transfer rate improves by up to 71% as the radiation coefficient increases from 0 to 5.