Adomian decomposition method simulation of von Kármán swrling bioconvection nanofluid flow

Shamshuddin, Md.; Mishra, S. R.; Bég, O. Anwar; Kadir, A

doi:10.1007/s11771-019-4214-4

Cited by 52 publications

(19 citation statements)

References 41 publications

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“…Recently, new fluids have come up with magnetic and supermagnetic particles that show both heat and magnetic augmentation properties. This kind of nanoparticles has wide range of usages in biomedicine, 29 thin film smart polymer coating smart thin films, 28–30 swirling bioconvection nanofluid, 31 nuclear smart pumping bio‐inspired systems 32 . Quadruple solutions on nanofluid over exponential shrinking/stretching surface 33 .…”

Section: Introductionmentioning

confidence: 99%

Computation of ferromagnetic/nonmagnetic nanofluid flow over a stretching cylinder with induction and curvature effects

et al. 2021

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Motivated by enrobing processes in manufacturing technology with intelligent coatings, this study analyses the flow of an electroconductive incompressible nanofluid with heat distribution in a boundary layer containing metallic nanoparticles or ferroparticles along an extending cylindrical body with magnetic induction effects. The quasilinear boundary conditions for the partial derivative formulations connecting to the far stream and cylinder wall are converted to ordinary nonlinear derivatives by applying appropriate similarity transformations. The emerging system of derivatives is solved by a stable, efficient spectral relaxation method (SRM). The SRM procedure is benchmarked with special limiting cases in the literature and found to corroborate exceptionally well with other studies in the literature. Here, water is taken as the base liquid containing homogenously suspended nonmagnetic (Nimonic 80a, silicon dioxide [SiO2]) or magnetic nanoparticles (ferric oxide [Fe3O4] and manganese franklinite [Mn–ZnFe2O4]). The influence of all key parameters on the velocity and temperature distributions is displayed in graphs and tables with extensive elucidation. The wall local drag force (skin friction) and local temperature gradient (Nusselt number) are also visualized graphically for various parameters. The rate of convergence of the SRM convergence is compared with that of the successive over‐relaxation method, and it is observed to converge faster. Larger magnetohydrodynamic body force parameter and inverse Prandtl magnetic number induce flow deceleration and enhance temperature. Flow acceleration is computed for SiO2 nonmagnetic nanoparticles, and good heat conduction augmentation is produced with magnetic nanoparticle Fe3O4.

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Section: Introductionmentioning

confidence: 99%

Computation of ferromagnetic/nonmagnetic nanofluid flow over a stretching cylinder with induction and curvature effects

et al. 2021

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“…In ADM, the analytic approximate solutions to a nonlinear equation are obtained without linearization and discretization, yielding more accurate results. ADM has been deployed recently in a variety of complex multiphysical fluid dynamic problems including squeeze film magnetic biolubrication, 39 stagnation flow of nanofluids on rotating bodies, 40 magnetically actuated ciliated propulsion 41 and swirling bioconvection nanofluid from a rotating radially stretching disk 42 . Aski et al, 38 utilized recursive relations and also produced an alternative approach.…”

Section: Adm Solution and Validation With Gdqmentioning

confidence: 99%

“…Thus, arranging all the Equations (7) to (9) and by writing them as infinite series using recursive relations with initial guesses, the approximate analytical solutions can be obtained for the variables (velocities,

F, H

, and temperature

θ

). For further details, the reader is referred to Reference [42]. The ADM power series expansions with notations are as follows:

F (ξ) = - \frac{1}{2} H false' (ξ),

H (ξ) = θ_{0} (ξ) + θ_{1} (ξ) + θ_{2} (ξ) + θ_{3} (ξ),

\begin{array}{l} H false(ξ false) = α ξ - M a c ξ^{2} + T_{1} ξ^{3} + T_{2} ξ^{4} + T_{3} ξ^{5} + T_{4} ξ^{6} + false(T_{5} + T_{6} false) ξ^{7} + T_{7} ξ^{8} + T_{8} ξ^{9} + T_{9} ξ^{10} \end{array},

θ (ξ) = H_{0} (ξ) + H_{1} (ξ) + H_{2} (ξ) + H_{3} (ξ),

\begin{matrix} left \\ \begin{array}{l} θ false(ξ false) = 1 + β ξ + T_{10} \end{array} \end{matrix}

…”

Section: Adm Solution and Validation With Gdqmentioning

confidence: 99%

Computation of radiative Marangoni (thermocapillary) magnetohydrodynamic convection in a Cu‐water based nanofluid flow from a disk in porous media: Smart coating simulation

et al. 2020

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With emerging applications for smart and intelligent coating systems in energy, there has been increasing activity in researching magnetic nanomaterial coating flows. Surface tension features significantly in such regimes, and in the presence of heat transfer, Marangoni (thermocapillary) convection arises. Motivated by elaborating deeper the intrinsic transport phenomena in such systems, in this paper, a mathematical model is developed for steady radiative heat transfer and Marangoni magnetohydrodynamic flow of a Cu‐water nanofluid influencing a strong magnetic field through a porous disk. The semianalytical adomain decomposition method is employed to find the solution of flow governing equations, which are reduced into ordinary differential equation form via the Von Karman similarity transformation. Validation with a generalized differential quadrature algorithm is included. The response in dimensionless velocity, temperature, wall heat transfer rate and shear stress is investigated for various values of the control parameters. Temperature is reduced with increasing Marangoni parameter, whereas the flow is accelerated. With increasing permeability parameter, the temperatures are elevated. Increasing radiative flux boosts temperatures further from the disk surface. Increasing magnetic parameter strongly dampens the boundary layer flow and elevates the temperatures, also eliminating temperature oscillations at lower magnetic field strengths.

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“…In ADM, the analytic approximate solutions to a nonlinear equation are obtained without linearization and discretization yielding more accurate results. Therefore, ADM has been deployed recently in many sophisticated multi-physical fluid dynamic problems including magnetic bio-lubrication [38], hydromagnetic cilia propulsion [39] and swirling bioconvection nanofluid flows [40]. Adomain [31] deployed an infinite series solution for the unknown functions, utilized recursive relations and also produced an alternative approach for polynomial expansions to achieve faster convergence.…”

Section: Adm Solutionmentioning

confidence: 99%

Adomain computation of radiative-convective bi-directional stretching flow of a magnetic non-Newtonian fluid in porous media with homogeneous–heterogeneous reactions

Mishra

Shamshuddin²,

Bég

et al. 2020

Int. J. Mod. Phys. B

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In the present communication, the laminar, incompressible, hydromagnetic flow of an electrically conducting non-Newtonian (Sisko) fluid over a bi-directional stretching sheet in a porous medium is studied theoretically. Thermal radiation flux, homogeneous–heterogeneous chemical reactions and convective wall heating are included in the model. The resultant nonlinear ordinary differential equations with transformed boundary conditions via similarity transformation are then solved with the semi-analytical Adomain Decomposition Method (ADM). Validation with earlier studies is included for the nonradiative case. Extensive visualization of velocity, temperature and species concentration distributions for various emerging parameters is included. Increasing the magnetic field and inverse permeability parameter is observed to decelerate both the primary and secondary velocity magnitudes whereas they increase temperatures in the regime. Increasing sheet stretching ratio weakly accelerates the primary flow throughout the boundary layer whereas it more dramatically accelerates the secondary flow near sheet surface. Temperature is consistently reduced with increasing stretching sheet ratio whereas it is strongly enhanced with greater radiative parameter. With greater Sisko non-Newtonian power-law index the primary velocity and temperature are decreased whereas the secondary velocity is increased. Increasing both homogenous and heterogeneous chemical reaction parameters is found to weakly and more strongly, respectively, deplete concentration magnitudes whereas greater Schmidt number enhances them.

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Adomian decomposition method simulation of von Kármán swrling bioconvection nanofluid flow

Abstract: Ti t l e Ado mi a n d e c o m p o si tio n m e t h o d si m ul a tio n of Von Ká r m á n s wi rlin g bio c o nv e c tio n n a n ofl ui d flo w

Cited by 52 publications

References 41 publications

Computation of ferromagnetic/nonmagnetic nanofluid flow over a stretching cylinder with induction and curvature effects

Computation of ferromagnetic/nonmagnetic nanofluid flow over a stretching cylinder with induction and curvature effects

Computation of radiative Marangoni (thermocapillary) magnetohydrodynamic convection in a Cu‐water based nanofluid flow from a disk in porous media: Smart coating simulation

Adomain computation of radiative-convective bi-directional stretching flow of a magnetic non-Newtonian fluid in porous media with homogeneous–heterogeneous reactions

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