Revolutionizing bioconvection: Artificial intelligence-powered nano-encapsulation with oxytactic microorganisms

Abstract

Using incompressible smoothed particle hydrodynamics (ISPH), this study examines the bioconvection flow of oxytactic microorganisms in a porous annulus populated by nano-encapsulated phase change material (NEPCM). Artificial neural network (ANN) is joined with the ISPH method to accurately predict the values of average Nu‾. Between the outside hexagonal-shaped domain and the embedded wavy cylinder, a new annulus is produced. The ranges of the pertinent parameters are wave amplitude of an embedded cylinder A=0.1−0.5, Hartmann number Ha=0−80, a radius of the cylinder Rcyld=0.05−0.5, Darcy number Da=10−2−10−5, undulation number Kund=2−32, bioconvection Rayleigh number Rab=1−1000, Rayleigh number Ra=103−106, and Lewis number Le=1−20. The regulated geometric characteristics of an embedded wavy cylinder, such as wavy amplitude, cylinder radius, and undulation numbers, have been found to contribute significantly to widening the cooling region and minimizing the oxytactic microorganisms. Hence, the embedded wavy cylinder can be applied for various thermal uses including cooling devices and heat exchangers. The average Nu‾ is enhanced under an increment in the geometric parameters of the embedded cylinder. Increasing Rayleigh/bioconvection Rayleigh numbers enhances the buoyancy forces that accelerate the velocity field.