Boundary layer flow past a continuously moving thin needle in a nanofluid

Soid, Siti Khuzaimah; Ishak, Anuar Mohd; Pop, Ioan

doi:10.1016/j.applthermaleng.2016.11.165

Cited by 90 publications

(74 citation statements)

References 51 publications

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“…The thin needle is supposed to be moving with a constant velocity u w and the fluid in a stress-free region is moving with velocity u ∞ as shown in Figure 1. Under the stated assumptions, the governing flow equations along with the boundary conditions take the following form [21][22][23][24]:…”

Section: Flow Analysismentioning

confidence: 99%

“…represents the velocity ratio parameter. The local skin friction coefficient takes the following form: The similarity variables are defined as [23]:…”

Section: Flow Analysismentioning

confidence: 99%

“…They solved the resulting problem numerically for two types of nanoparticles, which were either nonmetallic or metallic, such as alumina (Al 2 O 3 ) and copper (Cu) and water was taken as base fluid. Soid et al [23] examined the analysis of boundary layer flow in a thin needle in continuous motion in a nanofluid. All the above thin needle problems focused on the heat transfer analysis only.…”

Section: Introductionmentioning

confidence: 99%

“…Flow model and coordinate system.The similarity variables are defined as[23]: and ν identically satisfied the continuity equation. The composite velocity is defined as U = uw + u∞ ≠ 0 and the dimensionless stream function is denoted by f(η).…”

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confidence: 99%

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Thermodynamic Analysis of Entropy Generation Minimization in Thermally Dissipating Flow Over a Thin Needle Moving in a Parallel Free Stream of Two Newtonian Fluids

Khan

Qasim

et al. 2019

Entropy

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This article is devoted to study sustainability of entropy generation in an incompressible thermal flow of Newtonian fluids over a thin needle that is moving in a parallel stream. Two types of Newtonian fluids (water and air) are considered in this work. The energy dissipation term is included in the energy equation. Here, it is presumed that u∞ (the free stream velocity) is in the positive axial direction (x-axis) and the motion of the thin needle is in the opposite or similar direction as the free stream velocity. The reduced self-similar governing equations are solved numerically with the aid of the shooting technique with the fourth-order-Runge-Kutta method. Using similarity transformations, it is possible to obtain the expression for dimensionless form of the volumetric entropy generation rate and the Bejan number. The effects of Prandtl number, Eckert number and dimensionless temperature parameter are discussed graphically in details for water and air taken as Newtonian fluids.

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Section: Flow Analysismentioning

confidence: 99%

“…represents the velocity ratio parameter. The local skin friction coefficient takes the following form: The similarity variables are defined as [23]:…”

Section: Flow Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Thermodynamic Analysis of Entropy Generation Minimization in Thermally Dissipating Flow Over a Thin Needle Moving in a Parallel Free Stream of Two Newtonian Fluids

Khan

Qasim

et al. 2019

Entropy

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“…Chen and Kearns investigated the forced convection with nonuniform temperature and nonuniform heat flux from a thin needle in a non‐Newtonian fluid. Further, Soid et al simulated the boundary layer flow over a thin needle in a nanofluid. The forced convective flow along the moving horizontal needle in MHD dissipative nanofluid was numerically investigated .…”

Section: Introductionmentioning

confidence: 99%

MHD boundary layer bionanoconvective non‐Newtonian flow past a needle with Stefan blowing

Amirsom

Uddin

Ismail

2018

Heat Trans. Asian Res.

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The purpose of the article is to present a transport model for magnetohydrodynamics‐forced convective non‐Newtonian boundary flow from a thin needle in a nanofluid in the presence of microorganisms and Stefan blowing. The governing equations are reduced to ordinary differential equations with the help of similarity transformations and then numerically solved by using the Matlab bvp4c function. The effect of various emerging parameters on the flow field, heat, mass, and density of motile microorganisms transfer was computed and studied. It was found that some of the parameters have an important effect on the boundary layer thickness. Justification with earlier simpler model in the absence of magnetic field is included. The model finds applications in various transdermal delivery system, biomedical electromagnetic treatments and to design new medical devices for cell delivery to the central nervous system.

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Designing artificial neural network of nanoparticle diameter and solid–fluid interfacial layer on single‐walled carbon nanotubes/ethylene glycol nanofluid flow on thin slendering needles

Shafiq

Çolak

Sindhu

2021

Numerical Methods in Fluids

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In this study, an artificial neural network (ANN) has been developed to predict the boundary layer flow of a single‐walled carbon nanotubes nanofluid toward three different nonlinear thin isothermal needles of paraboloid, cone, and cylinder shapes with convective boundary conditions. Different effects of particle diameter and solid–fluid interface coating have been taken into account in the thermal conductivity model of nanofluid in which ethylene glycol has been used as the base fluid. Single and dual phase approach is used to establish the management model under the phenomenon of zero heat and mass flux. A dataset has been developed for different scenarios of the fluid model by changing the relevant parameters with the Runge–Kutta based shooting technique. Two different ANN models have been developed to predict Nusselt number and skin friction coefficient (SFC) values. The values obtained from ANN models have been compared with the numerical data, which are the target values. In addition, mean square error and R values have also been examined in order to analyze the prediction performance of ANN models more comprehensively. The calculated R values for Nusselt number and SFC were obtained as 0.9999. The results obtained showed that ANN can predict Nusselt number and SFC values with high accuracy.

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Boundary layer flow past a continuously moving thin needle in a nanofluid

Cited by 90 publications

References 51 publications

Thermodynamic Analysis of Entropy Generation Minimization in Thermally Dissipating Flow Over a Thin Needle Moving in a Parallel Free Stream of Two Newtonian Fluids

Thermodynamic Analysis of Entropy Generation Minimization in Thermally Dissipating Flow Over a Thin Needle Moving in a Parallel Free Stream of Two Newtonian Fluids

MHD boundary layer bionanoconvective non‐Newtonian flow past a needle with Stefan blowing

Designing artificial neural network of nanoparticle diameter and solid–fluid interfacial layer on single‐walled carbon nanotubes/ethylene glycol nanofluid flow on thin slendering needles

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