Revision as of 10:10, 20 April 2026

Abstract

This study investigates the flow and heat transfer of a hybrid nanofluid including alumina and titanium dioxide nanoparticles across a stretched sheet in the presence of a magnetic field, thermal radiation, and bioconvection. The problem is significant because hybrid nanofluids are increasingly employed in industrial cooling, biomedical transport, and energy systems that need accurate prediction of nonlinear thermal and solutal behavior. The governing nonlinear equations were transformed into ordinary differential form and computationally solved via a fourthorder Runge-Kutta method. In addition to this, an artificial neural network model was created to reproduce the numerical results and evaluate prediction abilities. The results show that stronger magnetic forces reduce velocity by about 18%, while higher radiation levels increase fluid temperature by nearly 22%. Increasing the Lewis number lowers nanoparticle concentration by roughly 15%, and higher bioconvection effects reduce microorganism density by about 12%. The neural network achieved excellent agreement with the numerical results, with regression values close to 1 and a mean squared error of order 10−10. The findings indicate that hybrid nano-fluids offer enhanced heat transfer and flow stability under MHD conditions, supporting potential improvements in fluid and thermal performance for applications requiring precise thermal management.OPEN ACCESS Received: 27/08/2025 Accepted: 05/11/2025 Published: 16/04/2026

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