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<p>This research investigates the momentum and heat transfer behavior of a composite fluid made from a CuO/blood-based nanofluid flowing over a melting stretching surface. The goal is to explore its potential use in advanced heat exchangers to enhance thermal performance and control viscosity. The analysis includes key factors such as magnetic effects, melting effects, viscous dissipation, and nonlinear thermal radiation. Nonlinear differential equations are transformed into ordinary differential equations using local similarity variables. These equations are solved numerically using the shooting method with the Runge–Kutta (RK) approach. The study also examines how dimensionless parameters like the porosity number, radiation, temperature ratio affects flow and temperature profiles presence of viscous dissipation. Results are validated using MATLAB’s bvp4c solver. Heat transfer rates and skin friction coefficients are discussed in detail. The model incorporates three fluids—Powell-Eyring, Maxwell, and blood-based nanofluids, making it suitable for advanced thermal applications. An artificial neural network (ANN) is used for prediction. It was trained and validated with 639, 128, and 89 epochs for the three cases. The ANN achieved validation errors of 2.294e-04, 1.2854e-05, and 2.5076e-05, showing strong prediction accuracy. </p>

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Neural network-driven analysis of different non-Newtonian fluids over a deformable melting surface with nonlinear radiation and slip effects

  • 期刊论文

  • 发表日期：

    2025年7月6日

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  • Chandralekha Mahanta,
  • Ram Prakash Sharma

摘要

This research investigates the momentum and heat transfer behavior of a composite fluid made from a CuO/blood-based nanofluid flowing over a melting stretching surface. The goal is to explore its potential use in advanced heat exchangers to enhance thermal performance and control viscosity. The analysis includes key factors such as magnetic effects, melting effects, viscous dissipation, and nonlinear thermal radiation. Nonlinear differential equations are transformed into ordinary differential equations using local similarity variables. These equations are solved numerically using the shooting method with the Runge–Kutta (RK) approach. The study also examines how dimensionless parameters like the porosity number, radiation, temperature ratio affects flow and temperature profiles presence of viscous dissipation. Results are validated using MATLAB’s bvp4c solver. Heat transfer rates and skin friction coefficients are discussed in detail. The model incorporates three fluids—Powell-Eyring, Maxwell, and blood-based nanofluids, making it suitable for advanced thermal applications. An artificial neural network (ANN) is used for prediction. It was trained and validated with 639, 128, and 89 epochs for the three cases. The ANN achieved validation errors of 2.294e-04, 1.2854e-05, and 2.5076e-05, showing strong prediction accuracy.