Squeezing flow of nanofluids of Cu–water and kerosene between two parallel plates by Gegenbauer Wavelet Collocation method

Çelik, İbrahim

doi:10.1007/s00366-019-00821-1

Cited by 24 publications

(8 citation statements)

References 50 publications

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“…The numerical application based on LMBNN is presented here for the squeezing flow model obtained in Eqs. (5)(6)(7). The proposed LMBNN is implemented for six scenarios by variation of Re, Q, with three different cases for each scenarios, as shown in Table (1).…”

Section: Resultsmentioning

confidence: 99%

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Intelligent Computing Technique Based Supervised Learning for Squeezing Flow Model

Almalki

Alaidarous

Maturi

et al. 2021

Preprint

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In this study, the unsteady squeezing flow between infinite parallel plates (USF-IPP) is investigated through the intelligent computing paradigm of Levenberg-Marquard backpropagation neural networks (LMBNN). Similarity transformation introduces the fluidic system of the governing partial differential equations (PDEs) into nonlinear ordinary differential equations (ODEs). A dataset is generated based on squeezing fluid flow system USF-IPP for the LMBNN through the Runge-Kutta method by the suitable variations of Reynolds number and volume flow rate. TO attain approximation solutions for USF-IPP to different scenarios and cases of LMBNN, the operations of training, testing, and validation are prepared and then the outcomes are compared with the reference data set to ensure the suggested model's accuracy. The output of LMBNN is discussed by the mean square error, dynamics of state transition, analysis of error histograms, and regression illustrations.

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Section: Resultsmentioning

confidence: 99%

“…Çelik,İbrahim. [5] studied the effect of viscosity on squeezing flow in a magnetic field for a specific type of fluid known as Copper-water and Copper-kerosene. Noor et al [6] discussed the impact of Cattaneo-Christov heat and mass fluxes on nanofluid's squeezing flow.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Computing Technique Based Supervised Learning for Squeezing Flow Model

Almalki

Alaidarous

Maturi

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…The impact of thermophoresis and Brownian motion on the unsteady flow of fluid between parallel plates was analyzed by Sheikholeslami et al 17 In this study, authors solved the differential equations (DEs) using the differential transformation method and observed that the heat transfer rate decreases with the increase in the squeeze parameter. Celik 18 applied the Gegenbauer wavelet collocation method to solve the equations elucidating the nanofluid flow between parallel plates in the presence of the magnetic field. The author used the quasilinearization technique to reduce the system of nonlinear DEs into linear DEs.…”

Section: Introductionmentioning

confidence: 99%

Unsteady squeezing flow of magnetic hybrid nanofluids within parallel plates and entropy generation

2020

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Nowadays, due to the novel thermal effectiveness, a new class of fluid, named "hybrid nanofluid," is used. It has significant applications in domestic and industrial fields. In this study, we investigated the entropy generation and heat transfer of unsteady squeezing magnetic hybrid nanofluid flow between parallel plates by considering heat source/sink and thermal radiation. In this analysis, carbon nanotubes (CNTs) (single-walled carbon nanotube and multiwalled carbon nanotube) are considered as nanoparticles that are dispersed in water-ethylene glycol (EG) mixtures (ie, 70%W + 30%EG and 50%W + 50% EG). For the analysis of the physical behavior of hybrid nanofluids, new models related to hybrid nanofluids are incorporated. From this study, it has been observed that as the hybrid nanofluids moved away from the surface, the entropy generation outlines accelerated with an increase in magnetic field values. Moreover, an increase in the volume fraction of CNTs, the thermal conductivity of 50%W + 50% EG + CNTs hybrid nanofluid is greater than 70% W + 30%EG + CNTs hybrid nanofluid.

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“…They implemented different types of methods in obtaining the solution. The squeezing flow of Copper-water and Copper-kerosene with magnetic field and viscous dissipation effects using Tiwari and Das model was discussed by Acharya et al, [1] and Çelik [10]. A series of studies on the similar problem had been investigated by Sheikholeslami et al, [30], Hedayati and Ramiar [15] and Azimi and Riazi [5].…”

Section: Introductionmentioning

confidence: 99%

MHD Squeezing Flow of Casson Nanofluid with Chemical Reaction, Thermal Radiation and Heat Generation/Absorption

Noor¹,

Shafie²,

Admon³

2020

ARFMTS

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The heat and mass transfer characteristics on unsteady squeezing flow of magnetohydrodynamic (MHD) Casson nanofluid with chemical reaction, thermal radiation and heat generation/absorption effects is investigated in this study. The influences of viscous and joule dissipation are also examined. The flow is caused by squeezing between two parallel plates embedded in a porous medium. The highly coupled nonlinear partial differential equations are reduced to a system of nonlinear ordinary differential equations via similarity transformations. The transformed equations are solved using numerical scheme of Keller-box method. The accuracy of present method is validated through comparison of skin friction coefficient, Nusselt and Sherwood numbers with previously published results. Comparisons reveal that good agreements are achieved. Graphical results for velocity, temperature and nanoparticles concentration are analysed with various parameters. Findings demonstrate that the fluid velocity and temperature enhance when the plates move closer. Besides, increase in Hartmann number suppressed the fluid velocity and concentration due to the presence of strong Lorentz forces. The Brownian motion boosts the fluid temperature and concentration. Moreover, nanoparticles concentration is found to be higher in constructive chemical reaction and opposite effect is observed in destructive chemical reaction.

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Squeezing flow of nanofluids of Cu–water and kerosene between two parallel plates by Gegenbauer Wavelet Collocation method

Cited by 24 publications

References 50 publications

Intelligent Computing Technique Based Supervised Learning for Squeezing Flow Model

Intelligent Computing Technique Based Supervised Learning for Squeezing Flow Model

Unsteady squeezing flow of magnetic hybrid nanofluids within parallel plates and entropy generation

MHD Squeezing Flow of Casson Nanofluid with Chemical Reaction, Thermal Radiation and Heat Generation/Absorption

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