Double Diffusion Convection in Sisko Nanofluids with Thermal Radiation and Electroosmotic Effects: A Morlet-Wavelet Neural Network Approach

Peristaltic transport of non-Newtonian nanofluids with double diffusion is essential to biological engineering, microfluidics, and manufacturing processes. The authors tackle the key problem of Sisko nanofluids under double diffusion convection with thermal radiations and electroosmotic effects. The study proposes a solution approach by using Morlet-Wavelet Neural Networks that can effectively solve this complex problem by their superior ability in the capture of nonlinear dynamics. These convergence analyses were calculated across fifty independent runs. Theil’s Inequality Coefficient and the Mean Squared Error values range from 10⁻⁷ to 10⁻⁵ and 10⁻⁷ to 10⁻¹⁰, respectively. These values showed the proposed method is scientifically reliable and fast converging. Studies reveal that the intensity of the magnetic field causes a reduction in the flow velocity profile in the center of the channel. It is also evaluated that thermal radiations enhance the energy of the system, which promotes thermally induced diffusion and particle flow. The physical applications of this work pertain to improving fluid flow and heat transfer in engineering structures like converters or cooling devices or magnetic fluids in electronics, energy, and biomedical applications, where optimal control of fluid behavior is of paramount importance.