Aspects of infinite shear rate viscosity and heat transport of magnetized Carreau nanofluid

Ayub, Assad; Sabir, Zulqurnain; Shah, Syed Zahir Hussain; Mahmoud, S.R.; Algarni, Ali; Sadat, R.; Ali, Mohamed R.

doi:10.1140/epjp/s13360-022-02410-6

“…( 3 ), ( 4 ), ( 5 ) and ( 6 ), developed the following non-linear ODE’s The above Eqs. ( 13 – 16 ) are having the following dimension-less parameters namely Biot number, local Weissenberg number 20 , unsteadiness parameter, Schmidt number, thermophoresis parameter, Magnetic parameter, generalized Biot number, velocity slip parameter, Lewis number, Bio-convection rely number, Bouncy ratio parameter, Bio convective Lewis number, Peclet number, Brownian motion parameter, Prandtl number respectively are given below The system of mass, flow and heat transfer are the local skin friction coefficient , the local Nusselt number , local Sherwood number and the motile microorganism’s number which are written as …”

Section: Developed Modelmentioning

confidence: 99%

“…Naz 19 numerated the basic terms to elaborate thermal factors upon motile microorganism over viscoelastic nanofluid. Ayub et al 20 disclosed the aspects of all the basic parameters required for the propagation of heat transfer over magnetized Carreau nanofluid. Energy exchange featuring infinite shear rate viscosity model of Carreau nanofluids to examine radiation and heat transformation towards the poles is discussed by Shah 21 .…”

Section: Introductionmentioning

confidence: 99%

Multiple slip effects on time dependent axisymmetric flow of magnetized Carreau nanofluid and motile microorganisms

Faiz

¹

,

Habib

²

,

Siddique

³

et al. 2022

Sci Rep

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This presented work investigate the bio-convections effects of the magnetized time dependent axisymmetric flow of Carreau-nanomaterial performances with multiple slip effects over a stretching sheet. The momentum, heat, concentration and density of motile micro-organism are renovated into the system of equation via using well known similarity revolution. Well known Mathematical computational techniques and software (i.e. bvp4c and MATLAB) are used to draw graphical and tabular results. Velocity profile equation $$f^{\prime}(\zeta )$$ f ′ ( ζ ) , energy equation $$\theta (\zeta )$$ θ ( ζ ) , volumetric nanoparticles $$\phi \,(\zeta )$$ ϕ ( ζ ) , density motile microorganism $$\chi (\zeta )$$ χ ( ζ ) .The Carreau viscosity model is use to reduce the viscosity of fluid when $$W_{e} = 0\,\,\,$$ W e = 0 and $$\,n = 0$$ n = 0 . Besides we moderate this into power law index with $$\,n = 0.5$$ n = 0.5 and $$\,n = 1.5$$ n = 1.5 partial slip condition of velocity is also instigated at the surface. Gravity dependent gyrotactic nanoparticles are utilized for well observing axisymmetric flow with convective boundary layer condition and comparatively better heat transfer rate result and applicable to maximum realistic approach.

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“…Numerical approach Lobatto IIIA is used to analyze heat transport of nanofluid in three-dimensional magnetic flow in the study by Ayub et al (2021a). Similarly, the energy transport of magnetic Carreau nanofluid has been studied by Ayub et al (2022) using the infinite rate of share viscosity condition. The nanofluid magnetic dipole flow is studied by Shah et al (2022) under the effect of binary reaction and heat transportation through a cylindrical channel.…”

Section: Introductionmentioning

confidence: 99%

MHD williamson nanofluid flow in the rheology of thermal radiation, joule heating, and chemical reaction using the Levenberg–Marquardt neural network algorithm

Ali

¹

,

Ahammad

²

,

Eldin

³

et al. 2022

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Various studies have been conducted on the topic of predicting the thermal conductivity of nanofluids. Here, the thermal conductivity of nanofluids is determined using artificial neural networks since this approach is rapid and accurate, as well as cost-effective. To forecast the thermal conductivity of magnetohydrodynamic Williamson nanofluids flow through a vertical sheet, a feed-forward neural network with various numbers of neurons has been evaluated, and the best network based on the performance is selected. The fluid model incorporates the effects of Joule heating, heat generation absorption, thermal radiation, and a chemical reaction (MHD-WNF-HGA). A combination of heat radiation and reactive species improves the energy and solute profiles. The magnetic Reynolds number is assumed to be so small; therefore, the generated magnetic field has no effect. A postulate of similarity variables is used to convert the physical model in the form of nonlinear partial differential equations to an ordinary differential equation system. A supervised Levenberg–Marquardt backpropagation algorithm possesses a multilayer perceptron that is used for training the network, which is one of the top algorithms in machine learning. The bvp4c numerical technique is adopted to build the datasets for the construction of continuous neural network mapping. Flow, energy, and concentration profiles of the fluidic flow are constructed by adjusting several physical quantities such as the Williamson parameter, thermal radiation parameter, magnetic parameter, Eckert number, Darcy number, Brownian motion, and thermophoresis parameter. Analytical techniques such as error histogram graphs and regression-based statistical graphs are used to examine the accuracy of a suggested method. It has been found that the Levenberg–Marquardt backpropagation neural network mappings’ derivation, convergence, authentication, and consistency have been proven. Furthermore, thermal radiation assists the energy distribution to increase smoothly. Fluid velocity drops with the Williamson parameter, whereas thermophoresis impact enhances the strength of the nanofluid density.

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“…They have used the homotopy analysis method to model equations and have revealed that the thermal flow has improved for higher values of the volumetric fraction and radiation parameter which, on the other hand, has declined the flow profiles in all directions. Further investigation about heat transmission regarding nanofluids can be studied in Ayub et al (2021a), Ayub et al (2021b), Ayub et al (2021c), Shah et al (2021), Ayub et al (2022b), Shah et al (2022b), and Ayub et al (2022c).…”

Section: Introductionmentioning

confidence: 99%

Thermal examination for the micropolar gold–blood nanofluid flow through a permeable channel subject to gyrotactic microorganisms

Khan

¹

,

Alyami

²

,

Alghamdi

³

et al. 2022

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Presently, scientists across the world are carrying out theoretical and experimental examinations for describing the importance of nanofluids in the heat transfer phenomena. Such fluids can be obtained by suspending nanoparticles in the base fluid. Experimentally, it has proved that the thermal characteristics of nanofluids are much better and more appealing than those of traditional fluids. The current study investigates the heat transfer for the flow of blood that comprises micropolar gold nanoparticles. The influence of chemically reactive activation energy, thermophoresis, thermal radiations, and Brownian motion exists between the walls of the channel. A microorganism creation also affects the concentration of nanoparticles inside the channel. Suitable transformation has been used to change the mathematical model to its dimensionless form and then solve by using the homotopy analysis method. In this investigation, it has been revealed that the linear velocity behavior is two-folded over the range 0≤η≤1. The flow is declining in the range 0≤η≤0.5, whereas it is augmenting upon the range 0.5≤η≤1. Thermal characteristics are supported by augmentation in volumetric fraction, thermophoresis, radiation, and Brownian motion parameters while opposed by the Prandtl number. The concentration of the fluid increases with augmentation in activation energy parameters and decays with an increase in thermophoresis, Brownian motion, chemical reaction parameters, and the Schmidt number. The density of microorganisms weakens by growth in Peclet and bioconvection Lewis numbers.

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Aspects of infinite shear rate viscosity and heat transport of magnetized Carreau nanofluid

Cited by 35 publications

References 52 publications

Multiple slip effects on time dependent axisymmetric flow of magnetized Carreau nanofluid and motile microorganisms

Multiple slip effects on time dependent axisymmetric flow of magnetized Carreau nanofluid and motile microorganisms

MHD williamson nanofluid flow in the rheology of thermal radiation, joule heating, and chemical reaction using the Levenberg–Marquardt neural network algorithm

Thermal examination for the micropolar gold–blood nanofluid flow through a permeable channel subject to gyrotactic microorganisms

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