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<p>The blood is identified as a non-Newtonian fluid. The current exploration deals with investigating blood-based radiative micropolar fluid flow of Cu-CuO hybrid nanofluids and the features of heat transfer across a movable sheet during the non-uniform heat source/sink and melting procedure. Initially, the problem is modelled in the form of partial differential equations and then altered into ordinary differential equations using similarity variables. These equations are numerically solved using the fourth-order boundary value solver bvp4c and a neural network depending on the algorithm of the Levenberg–Marquardt backpropagation. The results demonstrated that the proposed artificial neural network technique could hold non-linear data with minimum error and showed consistent performance across all phases, including training, validation, and testing. In addition, the outcomes reveal that the stretching sheet velocity is lower in contrast to the free stream velocity because of the stagnation parameter. Conversely, the temperature as well as the angular velocity decline due to the stagnation factor. Moreover, the skin-friction, the coefficient of couple-stress, and the heat transfer rate increase significantly by up to 5.71%, 7.90%, and 8.62%, respectively, with higher nanoparticle volume fractions. In contrast, the heat transfer rate decreases by about 0.0078% due to the stronger influence of the melting effect.</p>

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Radiative melting heat transfer in a micropolar hybrid nanofluid flow using a Levenberg–Marquardt backpropagation scheme: effects of the stagnation-point parameter

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月16日

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  • Umair Khan,
  • Aurang Zaib,
  • Jomana A. Bashatah

摘要

The blood is identified as a non-Newtonian fluid. The current exploration deals with investigating blood-based radiative micropolar fluid flow of Cu-CuO hybrid nanofluids and the features of heat transfer across a movable sheet during the non-uniform heat source/sink and melting procedure. Initially, the problem is modelled in the form of partial differential equations and then altered into ordinary differential equations using similarity variables. These equations are numerically solved using the fourth-order boundary value solver bvp4c and a neural network depending on the algorithm of the Levenberg–Marquardt backpropagation. The results demonstrated that the proposed artificial neural network technique could hold non-linear data with minimum error and showed consistent performance across all phases, including training, validation, and testing. In addition, the outcomes reveal that the stretching sheet velocity is lower in contrast to the free stream velocity because of the stagnation parameter. Conversely, the temperature as well as the angular velocity decline due to the stagnation factor. Moreover, the skin-friction, the coefficient of couple-stress, and the heat transfer rate increase significantly by up to 5.71%, 7.90%, and 8.62%, respectively, with higher nanoparticle volume fractions. In contrast, the heat transfer rate decreases by about 0.0078% due to the stronger influence of the melting effect.