Computational analysis of soret and dufour effects on nanofluid flow through a stenosed artery in the presence of temperature-dependent viscosity

In this study, the Soret and Dufour effects in a composite stenosed artery were combined with an analysis of the effect of varying viscosity on copper nanofluids in a porous medium. Blood viscosity, which changes with temperature, is taken into account using the Reynolds viscosity model. The finite difference approach is used to quantitatively solve the governing equations. For use in medical applications, the effects of the physical parameters on velocity, temperature and concentration along the radial axis have been investigated and physically interpreted. The results are graphically displayed and physically defined in order to facilitate comprehension of the various phenomena that occur in the artery when nanofluid is present. It is observed that the Soret effect increases the rate of heat transfer but decreases the rate of mass transfer. The new study enhances knowledge of non-surgical treatment options for stenosis and other abnormalities, hence reducing post-operative complications. Additionally, current research may have biomedical applications such as magnetic resonance angiography (MRA), which provide a picture of an artery and enable identification of any anomalies, and thus may be usefu

Darcy-Forchheimer hybrid nanofluid flow over the rotating Riga disk in the presence of chemical reaction: Artificial neural network approach

The aim of present study is to examine the augmentation of thermal energy transfer in hybrid nanofluid flow caused by a rotating Riga disk in the presence of thermal radiation and chemical reaction. The silver and aluminium oxide nanoparticles are used to examine the thermal effect of water base fluid. The Darcy-Forchheimer model is considered to endorse the inertial and porous media effects and makes the model more realistic from the physical scenario. Levenberg-Marquardt backpropagation algorithm is considered to analyze the hybrid nanofluid’s properties. Using scaling group transformations, the governing partial differential equations are transformed into a system of ordinary differential equations. Resulting ordinary differential equations are solved numerically by applying a suitable shooting technique by MATLAB. The results obtained for the governing differential equations have been incorporated into a dataset on which the neural network has been trained. The effects of physical parameters have been analyzed for velocity, temperature, and concentration profiles. The determination, designing, convergence, verification, and stability of the Levenberg-Marquardt backpropagation neural network algorithm are validated on the assessment of achieved accuracy through performance, fit, regression, and error histogram plots for the discussed hybrid nanofluid. It is observed that fluid velocity reduces for enhanced Darcy-Forchheimer number, magnetic parameters and boosted for enhanced modified Hartmann number. Temperature profile increases by increasing the Brownian motion and thermophoresis parameters

Entropy generation optimization of cilia regulated MHD ternary hybrid Jeffery nanofluid with Arrhenius activation energy and induced magnetic field

Abstract This study deals with the entropy generation analysis of synthetic cilia using a ternary hybrid nanofluid (Al–Cu–Fe2O3/Blood) flow through an inclined channel. The objective of the current study is to investigate the effects of entropy generation optimization, heat, and mass transfer on ternary hybrid nanofluid passing through an inclined channel in the proximity of the induced magnetic field. The novelty of the current study is present in studying the combined effect of viscous dissipation, thermophoresis, Brownian motion, exponential heat sink/source, porous medium, endothermic–exothermic chemical reactions, and activation energy in the proximity of induced magnetic field is examined. The governing partial differential equations (PDEs) are transformed into the ordinary differential equations (ODEs) using appropriate transformations. Applying the low Reynolds number and the long-wavelength approximation, resultant ODEs are numerically solved using shooting technique via BVP5C in MATLAB. The velocity, temperature, concentration, and induced magnetism profiles are visually discussed and graphically analyzed for various fluid flow parameters. Graphical analysis of physical interest quantities like mass transfer rate, heat transfer rate, entropy generation optimization, and skin friction coefficient are also graphically discussed. The entropy generation improves for enhancing values of Reynolds number, solutal Grashof number, heat sink/source parameter, Brinkman number, magnetic Prandtl number, and endothermic-exothermic reaction parameter while the reverse effect is noticed for chemical reaction and induced magnetic field parameter. The findings of this study can be applied to enhance heat transfer efficiency in biomedical devices, optimizing cooling systems, designing efficient energy conversion processes, and spanning from renewable energy technologies to aerospace propulsion systems

Electroosmotic MHD ternary hybrid Jeffery nanofluid flow through a ciliated vertical channel with gyrotactic microorganisms: Entropy generation optimization

In this study, the computational analysis of entropy generation optimization for synthetic cilia regulated ternary hybrid Jeffery nanofluid (Ag–Au–TiO2/PVA) flow through a peristaltic vertical channel with swimming motile Gyrotactic microorganisms is investigated. Understanding the intricate interaction of multiple physical phenomena in biomedical applications is essential for optimizing entropy generation and advancing microfluidic systems. The characteristics of nanofluid are explored for the electroosmotic MHD fluid flow in the presence of thermophoresis and Brownian motion, viscous dissipation, Ohmic heating and chemical reaction. Using the appropriate transformations, a set of ordinary differential equations are created from the governing partial differential equations. The resulting ODEs are numerically solved using the shooting technique using BVP5C in MATLAB after applying the long-wavelength and low Reynolds number approximation. The velocity, temperature, concentration, electroosmosis, and microorganism density profiles are analyzed graphically for different emerging parameters. Graphical investigation of engineering interest quantities like heat transfer rate, mass transfer rate, skin friction coefficient, and entropy generation optimization are also presented. It is observed that the rate of mass transfer increases for increasing thermophoretic parameter, while reverse effect is noted for Brownian motion parameter, Schmidt number, and chemical reaction number. The outcomes of present study can be pertinent in studying Cilia properties of respiratory tract, reproductive system, and brain ventricles

Bayesian regularization networks for micropolar ternary hybrid nanofluid flow of blood with homogeneous and heterogeneous reactions: Entropy generation optimization

This study aims to analyze a Bayesian regularization backpropagation algorithm for micropolar ternary hybrid nanofluid flow over curved surfaces with homogeneous and heterogeneous reactions, Joule heating and viscous dissipation. The ternary hybrid nanofluid consists of nanoparticles of titanium oxide (TiO2), copper oxide (CuO), and silicon oxide (SiO2), with blood as the base fluid. The governing partial differential equations for the fluid flow are converted into ordinary differential equations using a group of self-similar transformations. The ordinary differential equations are solved using an appropriate shooting algorithm in MATLAB. The effects of physical parameters including curvature, micro-polar, radiation, magnetic, Prandtl, Eckert, Schmidt, and homogeneous and heterogeneous chemical reaction parameters are analyzed for velocity, micro rotational, temperature, and concentration profile. Physical quantities of engineering interest like heat transfer rate, mass transfer rate, skin friction coefficient, couple stress coefficient, and entropy generation are also discussed in this study. A Bayesian regularization backpropagation algorithm is also designed for the solution of the ordinary differential equations. The obtained network is analyzed using training state, performance, error histograms, model response, Error autocorrelation, and input-error correlation plots. It is observed that the entropy generation and the Bejan number increase for enhancing Brinkman and radiation parameter. Clinical researchers and biologists may use the results of this computational study to forecast endothelial cell damage and plaque deposition in curved arteries, by which the severity of these conditions can be reduced