Interaction of multi-walled carbon nanotubes in mineral oil based Maxwell nanofluid

Hanif, Hanifa; Shafie, Sharidan

doi:10.1038/s41598-022-07958-y

Cited by 19 publications

(1 citation statement)

References 39 publications

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“…Nanofluid shows better performance in heat transfer than regular fluids. Many investigations on nanofluid flow with various physical situations have been done by several authors; Hanif [2], Hanif and Shafie [3,4], Saeed et al, [5], Sharma et al, [6,7], and Mishra et al, [8]. A major requirement for today's industry is the existence of systems or applications that allow heat transfer to occur more quickly.…”

Section: Introductionmentioning

confidence: 99%

Heat Transfer in Hartmann Flow of Hybrid Nano-Jeffrey Fluid with Heat Absorption and Thermal Radiation Impact

Nor Athirah Mohd Zin,

Siti Nur Alwani Salleh,

Ahmad Qushairi Mohamad

et al. 2024

ARFMTS

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In this study, we examine how heat radiation and absorption affect the flow of Jeffrey fluid across an infinite vertical plate in an unsteady magnetohydrodynamic (MHD) free convection flow. We use alumina (Al2O3) and copper (Cu) nanoparticles in water assuming it as a base fluid. The problem is solved, and exact solutions are obtained using the Laplace transform method. For embedded parameters like radiation parameter, heat absorption parameter, Hartmann number, Grashof number, material parameter of Jeffrey fluid, volume fraction of hybrid nanofluid, time, and Prandtl number, results of velocity and temperature distributions are visually displayed. It is clear from the results that while raising the heat absorption parameter and Prandtl number causes a decrease in the velocity and temperature profiles whereas increasing radiation parameter and Grashof Number increases the hybrid nanofluid velocity. The resulting analytical solution is then verified by comparing it to the results of the earlier investigation and is determined to be in excellent accord. This outcome may be used in a number of nanofluid cooling systems. This research might serve as a reference for other numerical and experimental studies as well as a manual for several industries because the answers are established in an analytical form.

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Section: Introductionmentioning

confidence: 99%

Heat Transfer in Hartmann Flow of Hybrid Nano-Jeffrey Fluid with Heat Absorption and Thermal Radiation Impact

Nor Athirah Mohd Zin,

Siti Nur Alwani Salleh,

Ahmad Qushairi Mohamad

et al. 2024

ARFMTS

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A novel design of recurrent neural network to investigate the heat transmission of radiative Casson nanofluid flow consisting of carbon nanotubes (CNTs) across a curved stretchable surface

Shahbaz,

Ahmad,

Raja

et al. 2024

Z Angew Math Mech

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This study aims to develop a supervised learning artificial recurrent neural network algorithm supported by Bayesian regularization called (ARNN‐BR) to analyze the impact of physical parameters, including radius of curvature (), Casson parameter (), heat generation parameter () and radiation parameter () on velocity fʹ(η), and temperature profiles θ(η) in Casson nanofluid consisting of carbon nanotubes (CNTs‐CNF) model for single and multiwalled CNTs across a curved stretched surface. The numerical dataset of the proposed model has been constructed by varying various parameters for five scenarios that are used in a Bayesian regularization‐based intelligent computing method to build networks for approximating the numerical solutions of CNTs‐CNF model. It is observed that increment in the dimensionless radius of curvature () causes to rise an increase in the velocity profile fʹ(η) for both SWCNTs and MWCNTs. However, a contrasting trend is observed when the Casson parameter () is increased to higher values. The temperature θ(η) of fluid increases as the heat generation parameter () and radiation parameter () increase. However, an opposite behavior is noticed when the dimensionless radius of curvature () varies. The effectiveness and significance of designed Bayesian regularization based artificial recurrent neural networks (ARNN‐BR) is demonstrated through regression index measurements, error histogram studies, auto‐correlation analysis and convergence curves showing a minimal level of mean square error (E‐11 to E‐04) for the comprehensive simulations of CNTs‐CNF model. The designed ARNN‐BR algorithm is employed in many domains such as voice recognition, machine translation, identification of neurological brain illnesses as well as for automated translation of texts across different languages.

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Entropy analysis of magnetized ferrofluid over a vertical flat surface with variable heating

Hanif

Shafie

Rawi

et al. 2023

Alexandria Engineering Journal

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Interaction of multi-walled carbon nanotubes in mineral oil based Maxwell nanofluid

Cited by 19 publications

References 39 publications

Heat Transfer in Hartmann Flow of Hybrid Nano-Jeffrey Fluid with Heat Absorption and Thermal Radiation Impact

Heat Transfer in Hartmann Flow of Hybrid Nano-Jeffrey Fluid with Heat Absorption and Thermal Radiation Impact

A novel design of recurrent neural network to investigate the heat transmission of radiative Casson nanofluid flow consisting of carbon nanotubes (CNTs) across a curved stretchable surface

Entropy analysis of magnetized ferrofluid over a vertical flat surface with variable heating

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