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<p>This research aims to describe the flow characteristics and entropy creation of an Oldroyd-B tri hybrid nanofluid under conditions of MHD with heat transfer through a hybrid numerical—machine learning framework. The nonlinear boundary layer governing equations for momentum and heat transport are transformed to a commonly used ordinary differential equations (ODE) form via similarity transformations and solved using MATLAB's bvp4c solver. A mesh independence study has verified numerical. The main innovation of this study is combining ANN modelling with the nonlinear numerical calculation of Oldroyd-B tri hybrid nanofluid flow to create an accurate predictive surrogate modelling tool for complex thermofluid systems. Data collected from the bvp4c solver was then used to train a feed forward ANN, using Levenberg–Marquardt backpropagation algorithm. The trained ANN was able to accurately predict velocity, temperature and entropy creation, with regression accuracies above 0.999 and mean square error values less than <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\({10}^{-6}\)</EquationSource> </InlineEquation>, indicating a high degree of predictive power. Parametric analysis indicated that increasing the magnetic parameter caused a large reduction in velocity field (approximately 18–25%) as a result of the Lorentz force acting in against the flow. Also, the generation of heat increased the temperature profile by approximately 20%, therefore increasing Entropy Generation within the thermal boundary layer. In addition, tri-hybrid nanoparticles have better thermal conductivity and heat transfer performance than nanofluids made from conventional materials because of their ability to improve the thermal performance of fluids. A predictive framework for ANN-based modelling of nonlinear fluid transport has been developed that reduces the computational cost of obtaining accurate numerical solutions compared with traditional methods. This new framework has the potential to allow engineers and scientists to model advanced nanofluids with greater accuracy than previous approaches, thereby providing valuable information for thermal management, high-performance cooling systems, and energy conversion technologies.</p>

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Intelligent prediction of thermodynamic performance in MHD Oldroyd B trihybrid nanofluids using artificial neural networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月24日

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  • Mamoon Aamir,
  • Chemseddine Maatki,
  • Aqsa Zafar Abbasi,
  • Rajab Alsayegh,
  • Karim Kriaa,
  • Walid Hassen,
  • Lioua Kolsi

摘要

This research aims to describe the flow characteristics and entropy creation of an Oldroyd-B tri hybrid nanofluid under conditions of MHD with heat transfer through a hybrid numerical—machine learning framework. The nonlinear boundary layer governing equations for momentum and heat transport are transformed to a commonly used ordinary differential equations (ODE) form via similarity transformations and solved using MATLAB's bvp4c solver. A mesh independence study has verified numerical. The main innovation of this study is combining ANN modelling with the nonlinear numerical calculation of Oldroyd-B tri hybrid nanofluid flow to create an accurate predictive surrogate modelling tool for complex thermofluid systems. Data collected from the bvp4c solver was then used to train a feed forward ANN, using Levenberg–Marquardt backpropagation algorithm. The trained ANN was able to accurately predict velocity, temperature and entropy creation, with regression accuracies above 0.999 and mean square error values less than \({10}^{-6}\) , indicating a high degree of predictive power. Parametric analysis indicated that increasing the magnetic parameter caused a large reduction in velocity field (approximately 18–25%) as a result of the Lorentz force acting in against the flow. Also, the generation of heat increased the temperature profile by approximately 20%, therefore increasing Entropy Generation within the thermal boundary layer. In addition, tri-hybrid nanoparticles have better thermal conductivity and heat transfer performance than nanofluids made from conventional materials because of their ability to improve the thermal performance of fluids. A predictive framework for ANN-based modelling of nonlinear fluid transport has been developed that reduces the computational cost of obtaining accurate numerical solutions compared with traditional methods. This new framework has the potential to allow engineers and scientists to model advanced nanofluids with greater accuracy than previous approaches, thereby providing valuable information for thermal management, high-performance cooling systems, and energy conversion technologies.