Neural network analysis of bioconvection effects on heat and mass transfer in Non-Newtonian chemically reactive nanofluids

Abstract

Using artificial neural networks , this study sought to investigate the magneto Williamson two-phase nanofluid , taking into account chemical reactions and the motion of gyrotactic motile microorganisms . Fluid flow behavior is influenced by chemical reactions, magnetic effects, Brownian motion, and thermophoresis , according to the study. Thermal transmission is enhanced in non-Newtonian fluids as a result of their propensity to thin under shear, increased turbulence, and superior convective heat transfer . As a result of the fluid's increased thermal conductivity , the incorporation of nanoparticles enhances heat conduction . Additionally, epidermis friction, Nusselt and Sherwood numbers, and the quantity of motile microorganisms were assessed in the study. The overall Absolute Errors lies in the range of 10 − 2 t o 10 − 10 .The mean squared error generated by Neural Networks lies in the range of 10 − 02 − 10 − 10 , and 10 − 02 − 10 − 09 respectively. Suction or injection parameter and Prandtl number have an inverse relation with fluid temperature, while Thermophoretic parameter have a direct relation. Thermophoretic parameter, Schmidt number and suction or injection parameter have an inverse relation with the concentration of nanofluid and gyrotactic microorganisms' density, while micro-organisms density have a direct relation with the microorganisms. Engineering and medicine have utilized bioconvection, a process involving heat transfer and microorganism motion, in the development of nanomedicine , pharmacokinetics , drug delivery, and biosensors , among others. Solvers utilizing stochastic numerical computing include nonlinear networks, atomistic physics , thermodynamics, astrometry , fluid mechanics, nanobiology. As a result, variant scenarios are then tested, trained, and validated, in order to prove its accuracy.

Topics & Concepts