Significance of oscillatory TiO-water nanofluid and heat transfer performance over vertical magnetized flat plate using artificial neural network
Source: PubMed Central Open Access, NCBI / U.S. National Library of Medicine
The oscillatory motion of titanium dioxide (TiO) nanoparticles in water-based fluid for heat and mass transfer performance over vertical plate has been investigated. The novelty of this work evaluates the steady and periodical behavior of heat and mass transfer using TiO-water nanofluid, magnetic field and mixed convection. The artificial neural network on dissipative Maxwell TiO-water nanofluid is applied to predict the accuracy and convergence of velocity profile under defined boundary conditions. The oscillatory flow pattern is applied to develop the fluctuating layers in heating frequency and mass transmission. This model converts the mathematical equations into steady, real and imaginary forms using stokes and primitive transformations. Several authors solved the fluid models by converting into ordinary-differential form but this model presents direct unsteady partial differential equations. Implicit finite-difference approach is used to convert the mathematical model into algebraic system in the presence of Gaussian elimination simulation. It is noticed that the rate of velocity amplitude increases as radiation, Maxwell index, and heat dissipation increase. The lowest training mean square error (1.77 × 10⁻), close validation, testing errors, small gradient (9.82 × 10⁻) at fewer epochs (636) are observed for magnetic number= 3.0. The steady behavior of heat and mass rate increases as Schmidt number and thermal radiation are enhanced. The amplitude of oscillatory heat and
Abstract
The oscillatory motion of titanium dioxide (TiO) nanoparticles in water-based fluid for heat and mass transfer performance over vertical plate has been investigated. The novelty of this work evaluates the steady and periodical behavior of heat and mass transfer using TiO-water nanofluid, magnetic field and mixed convection. The artificial neural network on dissipative Maxwell TiO-water nanofluid is applied to predict the accuracy and convergence of velocity profile under defined boundary conditions. The oscillatory flow pattern is applied to develop the fluctuating layers in heating frequency and mass transmission. This model converts the mathematical equations into steady, real and imaginary forms using stokes and primitive transformations. Several authors solved the fluid models by converting into ordinary-differential form but this model presents direct unsteady partial differential equations. Implicit finite-difference approach is used to convert the mathematical model into algebraic system in the presence of Gaussian elimination simulation. It is noticed that the rate of velocity amplitude increases as radiation, Maxwell index, and heat dissipation increase. The lowest training mean square error (1.77 × 10⁻), close validation, testing errors, small gradient (9.82 × 10⁻) at fewer epochs (636) are observed for magnetic number= 3.0. The steady behavior of heat and mass rate increases as Schmidt number and thermal radiation are enhanced. The amplitude of oscillatory heat and mass transfer intensifies as magnetic number and Richardson number are increased. The increasing skin friction rate is observed at each value of Maxwell parameter and Eckert number. Abs1