A novel investigation of thermo-bioconvection in oxytactic bacteria combined with tri-hybrid nanostructures under a spatially varying magnetic field: a machine learning approach

A spatially varying magnetic field changes in strength and direction from one location to another. This is different from a uniform magnetic field, which remains constant in a region. A spatially varying field can be designed with local variations, such as magnetic strips or gradients. These variations help control the motion of particles or fluids. Such magnetic fields are important for directing oxytactic bacteria and nanoparticles suspended in fluids. In drug delivery applications, magnetic fields can guide therapeutic nanoparticles toward specific target regions. This improves the accuracy and effectiveness of treatment. Motivated by these advantages, this study examines the thermo-bioconvection behavior of oxytactic bacteria in a tri-hybrid nanofluid under a spatially varying magnetic field. Numerical simulations are used to analyze the effects of thermal gradients, buoyancy forces, and viscous forces on microbial motion. Special attention is given to how localized magnetic strips influence fluid flow and heat transfer. The effects of the Bioconvection Rayleigh number and the Richardson number are also investigated. In addition, a machine learning approach is employed to optimize parameter selection and improve prediction accuracy. The numerical results show that increasing the magnetic number (Mn) enhances heat transfer by nearly 15% and reduces drag by 97%. Increasing the magnetic strip width improves heat transfer by 1.8% and reduces drag by 18%. However, higher Reynolds numbers reduce heat transfer by 38% and increase drag almost five times.