Neural network-based computational evaluation of periodic electroosmotic flow in propylene glycol–water ternary nanofluids with oxytactic microbes

The rapid evolution of artificial intelligence (AI) is revolutionizing molecular-scale data analysis, transport modeling, and the prediction of dynamic behavior in complex fluids. In this study, we present a novel application of an AI-driven artificial neural network (ANN) to investigate chaotic transport dynamics in periodic electroosmotic flow (PEOF) of Sutterby ternary nanofluids containing oxytactic microbes. The working fluid, a 50:50 mixture of propylene glycol and water infused with Fe₃O₄, TiO₂, and Al₂O₃ nanoparticles, is modeled flowing across a deformable porous geometry. The nonlinear governing equations are solved numerically using the finite difference method (FDM), with ANN employed to enhance predictive capability. Model validation shows remarkable accuracy, achieving mean squared errors between 10 −7 and 10 −9 , thereby confirming the robustness of the AI-assisted framework. The findings reveal that electroosmotic and magnetic parameters exert competing effects on fluid motion, while oxytactic microbes reduce concentration distribution. Increasing the Brownian motion parameter enhances random particle movement, resulting in higher temperatures and lower concentrations. Additionally, the density of motile microbes decreases with increasing Peclet and bio-Schmidt numbers. Importantly, tri-hybrid nanofluids exhibit superior thermal distribution compared with hybrid nanofluids, single nanofluids, and base fluids. This study is the first to integrate AI-driven ANN modeling with chaotic PEOF transport in Sutterby ternary nanofluids containing oxytactic microbes. Unlike previous works, it uniquely combines advanced AI techniques with nonlinear bio-nanofluid dynamics, achieving unprecedented predictive accuracy while uncovering new insights into the coupled roles of electroosmosis, magnetism, Brownian motion, and microbial activity. The outcomes provide a new pathway for AI-assisted optimization of nanofluid-based systems in wastewater treatment, microfluidics, and energy transport, enabling more efficient and sustainable technologies. • Artificial neural network algorithm analyzed vast molecular-scale datasets generated from the simulation. • Explores how nanofluids and oxytactic microbes interact in a periodically driven electroosmotic environment. • Examines the synergistic effects of thermal diffusion and random molecular motion on a Propylene Glycol-Water nanofluid. • Oxytactic microbes' affinity for oxygen-rich zones leads to a reduction in nanoparticle concentration. • Validates the theoretical model and its applicability in wastewater treatment, advanced thermal management and drug delivery systems.