Analysis of Heat Transfer and Thermal Radiation on Natural Convective Flow of Fractional Nanofluids

Madhura, K. R.; Babitha,; Iyengar, S. S.

doi:10.1166/jon.2019.1645

Cited by 11 publications

(6 citation statements)

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“…Aman et al 10 have analyzed the influence of heat and mass transfer on sodium alginate‐graphene fractional nanofluid using Caputo‐time fractional derivatives, and also, they have discussed the heat transfer enhancement of mixed convection magnetohydrodynamics Poisecuille flow of water‐graphene nanofluid in a vertical channel 11 . Analytical computation is presented by Madhura et al 12 to study the consequences of heat transfer by considering three types water‐based fractional nanofluids containing silver, copper oxide, and copper as nanoparticles over a vertical plate with the impact of thermal radiation. Using q ‐homotopy analysis transform technique, Prakash et al 13 have obtained the solution for fractional Richards equation.…”

Section: Introductionmentioning

confidence: 99%

Influence of nanoparticle shapes on natural convection flow with heat and mass transfer rates of nanofluids with fractional derivative

Madhura

Atiwali²,

Iyengar

2021

Math Methods in App Sciences

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Nanofluids are gaining extensive attention due to their thermo-physical properties in technological and industrial fields for controlling the effects of heat transfer. Classical nanofluid studies are generally confined to models described by partial differential equations of an integer order where the memory effect and hereditary properties of materials are neglected. In order to overcome these downsides, the present work focuses on studying nanofluids with fractional derivative formed by differential equations with Caputo time derivatives that provides memory effect on nanofluid characteristics. Also, investigation on natural convective flow, heat, and mass transfer of nanofluids formed by different base fluids with different shapes of copper nanoparticles past an infinite vertical plate with radiation effect is carried out. The governing fractional differential equations are solved by employing Laplace transform technique with suitable boundary conditions. The different base fluids-water (H 2 O), SA:sodium alginate (C 6 H 9 Na O 7 ), and EG:ethylene glycol (C 2 H 6 O 2 ) and different shapes of nanoparticles-blade, brick, platelet, and cylinder are considered for the study.The exact solutions are obtained for the temperature, concentration, and velocity distributions and the respective Nusselt number, Sherwood number, and skin-friction coefficient. The influence of non-dimensional parameters provides physical interpretations of temperature, concentration, and velocity fields, Nusselt number, Sherwood number, and skin friction in detail with the help of graphical representations. From the results, it is found that nanofluid with water-based blade-shaped nanoparticle exhibits more velocity and temperature distributions. Also, strengthen of fluid flow, temperature, and concentration of nanofluids are inversely correlate with fractional order derivatives.

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Section: Introductionmentioning

confidence: 99%

Influence of nanoparticle shapes on natural convection flow with heat and mass transfer rates of nanofluids with fractional derivative

Madhura

Atiwali²,

Iyengar

2021

Math Methods in App Sciences

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“…The following corelations provides the most effective properties of nanofluid 37–39 :

{(ρ C_{p})}_{nf} = ϕ {(ρ C_{p})}_{CNTs} + (1 - ϕ) {(ρ C_{p})}_{f}, ρ_{nf} = ϕ ρ_{CNTs} + (1 - ϕ) ρ_{f},

\frac{k_{nf}}{k_{normalf}} = \frac{(1 - ϕ) + 2 ϕ (\frac{k_{CNTs}}{k_{CNTs} - k_{normalf}})) italicln (\frac{k_{CNTs} + k_{normalf}}{2 k_{normalf}}))}{(1 - ϕ) + 2 ϕ (\frac{k_{f}}{k_{CNTs} - k_{normalf}})) italicln (\frac{k_{CNTs} + k_{normalf}}{2 k_{normalf}}))}, μ_{nf} = \frac{μ_{normalf}}{{(1 - ϕ)}^{2.5}} .

…”

Section: Mathematical Formulationunclassified

Computational study on heat transfer and MHD‐electrified flow of fractional Maxwell nanofluids suspended with SWCNT and MWCNT

Babitha¹,

Madhura

Makinde

2021

Heat Trans

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Recent developments in fluid dynamics have been focusing on nanofluids, which preserve significant thermal conductivity properties and magnify heat transport in fluids. Classical nanofluid studies are generally confined to models described by partial differential equations of an integer order, where the memory effect and hereditary properties of materials are neglected. To overcome these downsides, the present work focuses on studying nanofluids with fractional derivatives formed by differential equations with Caputo time derivatives that provide memory effect on nanofluid characteristics. Further, heat transfer enhancement and boundary layer flow of fractional Maxwell nanofluid with single‐wall and multiple walls carbon nanotubes are investigated. The Maxwell nanofluid saturates the porous medium. Also, buoyancy, magnetic, electric, and heating effects are considered. Governing continuity, momentum, and energy equations involving Caputo time‐fractional derivatives reduced nondimensional forms using suitable dimensionless quantities. Numerical solutions for arising nonlinear problems are developed using finite difference approximation combined with L1 algorithm. The influence of involved physical parameters on flow and heat transfer characteristics is analyzed and depicted graphically. Our simulations found out that surface drag of Maxwell nanofluid with single‐walled carbon nanotubes dominates nanofluids with multiple walls carbon nanotubes, but the reverse trend is noticed for larger Grashof number values.

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“…Heat transfer is important in many industrial and technical processes, such as combustors, axial blade compressors, fuel cells, heat exchangers, symmetric microelectronic board circuits, gas turbine blades, computer processors, and hybrid engines. Madhura et al [13] investigated a novel solution for studying heat and mass transfer in a nanofluid over a moving/stationary vertical plate in a porous medium. The free convection flow, heat, and mass transfer of fractional nanofluids made of several base fluids (water, sodium alginate, and ethylene glycol) suspended with copper nanoparticles via an endless vertical plate with radiation effect were studied by Madhura et al [14].…”

Section: Introductionmentioning

confidence: 99%

Numerical and Machine Learning Approach for Fe3O4-Au/Blood Hybrid Nanofluid Flow in a Melting/Non-Melting Heat Transfer Surface with Entropy Generation

Jakeer,

Easwaramoorthy,

Reddy

et al. 2023

Symmetry

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The physiological system loses thermal energy to nearby cells via the bloodstream. Such energy loss can result in sudden death, severe hypothermia, anemia, high or low blood pressure, and heart surgery. Gold and iron oxide nanoparticles are significant in cancer treatment. Thus, there is a growing interest among biomedical engineers and clinicians in the study of entropy production as a means of quantifying energy dissipation in biological systems. The present study provides a novel implementation of an intelligent numerical computing solver based on an MLP feed-forward backpropagation ANN with the Levenberg–Marquard algorithm to interpret the Cattaneo–Christov heat flux model and demonstrate the effect of entropy production and melting heat transfer on the ferrohydrodynamic flow of the Fe3O4-Au/blood Powell–Eyring hybrid nanofluid. Similarity transformation studies symmetry and simplifies PDEs to ODEs. The MATLAB program bvp4c is used to solve the nonlinear coupled ordinary differential equations. Graphs illustrate the impact of a wide range of physical factors on variables, including velocity, temperature, entropy generation, local skin friction coefficient, and heat transfer rate. The artificial neural network model engages in a process of data selection, network construction, training, and evaluation through the use of mean square error. The ferromagnetic parameter, porosity parameter, distance from origin to magnetic dipole, inertia coefficient, dimensionless Curie temperature ratio, fluid parameters, Eckert number, thermal radiation, heat source, thermal relaxation parameter, and latent heat of the fluid parameter are taken as input data, and the skin friction coefficient and heat transfer rate are taken as output data. A total of sixty data collections were used for the purpose of testing, certifying, and training the ANN model. From the results, it is found that the fluid temperature declines when the thermal relaxation parameter is improved. The latent heat of the fluid parameter impacts the entropy generation and Bejan number. There is a less significant impact on the heat transfer rate of the hybrid nanofluid over the sheet on the melting heat transfer parameter.

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Analysis of Heat Transfer and Thermal Radiation on Natural Convective Flow of Fractional Nanofluids

Cited by 11 publications

References 0 publications

Influence of nanoparticle shapes on natural convection flow with heat and mass transfer rates of nanofluids with fractional derivative

Influence of nanoparticle shapes on natural convection flow with heat and mass transfer rates of nanofluids with fractional derivative

Computational study on heat transfer and MHD‐electrified flow of fractional Maxwell nanofluids suspended with SWCNT and MWCNT

Numerical and Machine Learning Approach for Fe3O4-Au/Blood Hybrid Nanofluid Flow in a Melting/Non-Melting Heat Transfer Surface with Entropy Generation

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