Mixed convective flow of hybrid nanofluid over a heated stretching disk with zero-mass flux using the modified Buongiorno model

Alexandria Engineering Journal, Journal Year: 2023, Volume and Issue: 72, P. 83 - 96

Published: April 5, 2023

In this study, the mixed convective stagnation-point flow of a Al2O3-Cu/H2O hybrid nanofluid towards stretched disc with boundary and zero mass flux condition is described suction viscous dissipation effects. The process accomplished by using thermophoretic Brownian motion physical phenomena. After performing similarity transformation on PDEs in order to convert them into an ODE system, bvp4c solver then employed carry out numerical solution. study that was above, flow, heat, transfer characteristics were investigated assistance Buongiorno model Devi model. following brought up as points contention: ϕ1,ϕ2,λ,S,Nt,Nb,Le,Ec,Bi B. There very good consonance among existing antecedent results undeniable cases, well connection error approximately 0%. velocity temperature profiles upsurge increment nanoparticle volume fraction convection parameter, while increases addition parameters. As result we are able estimate thermal behavior when parameters embedded.

Language: Английский

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Numerical analysis of ternary hybrid nanofluid flow over a stagnation region of stretching/shrinking curved surface with suction and Lorentz force

Journal of Magnetism and Magnetic Materials, Journal Year: 2023, Volume and Issue: 573, P. 170654 - 170654

Published: March 28, 2023

Language: Английский

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Thermal performance of iron oxide and copper (Fe3O4, Cu) in hybrid nanofluid flow of Casson material with Hall current via complex wavy channel

Materials Science and Engineering B, Journal Year: 2023, Volume and Issue: 289, P. 116250 - 116250

Published: Jan. 5, 2023

Language: Английский

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Numerical analysis of magneto-radiated annular fin natural-convective heat transfer performance using advanced ternary nanofluid considering shape factors with heating source

Case Studies in Thermal Engineering, Journal Year: 2023, Volume and Issue: 44, P. 102825 - 102825

Published: March 2, 2023

The fins performance under natural convection is essential to make it more functional for large scale applications specifically in thermal engineering. For this, important introduce new techniques enhance the instead of traditional way. Thus, this study introduces a way fin efficient using ternary nanomaterial nanoparticles shape factor. annular significantly contributes electronics exhaust hot air, injector pumps and applied Methodology: This work focuses on energy model factors. Therefore, nanofluid, convection, radiation magnetic field used develop model. Then, RKF-45 implemented investigate physical results. Keen analysis results reveal that coefficient conductivity ranging from 0.0% < α1 3.0% have major role performance. Induction Rd are reliable cooling and, heating source Q1 = 0.2,0.4,0.6,0.8 promote capability existence (Al2O3–CuO–Cu) with concentration factor up 2%. On comparative basis, makes than hybrid nanomaterial.

Language: Английский

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Numerical thermal study of ternary nanofluid influenced by thermal radiation towards convectively heated sinusoidal cylinder

Heliyon, Journal Year: 2023, Volume and Issue: 9(9), P. e20057 - e20057

Published: Sept. 1, 2023

The heat transfer remains a huge problem for industrialists and engineers because many production processes required considerable amount of to finish the process successfully. Although, conventional fluids have large scale industrial applications but unable provide transfer. Therefore, study is organized propose new ternary model using different physical constraints. key area nanofluid are chemical, applied thermal food processing engineering.and Methodology: purpose this research introduce impressive effects radiations, surface convection saddle/nodal points. results simulated via RKF-45 discussed in detail.The strength Al2O3 nanoparticles form 1%-7% (keeping fixed CuO Cu as 4% 6%) s1 = -0.2,-0.4,-0.6,-0.8 controlled fluid movement while 0.2,0.4,0.6,0.8 boosted velocity. Increasing Bi 0.1,0.2,0.3,0.4 increased temperature significantly. Further, shear drag maximum radiations Rd enhances rate.

Language: Английский

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Impact of suction with nanoparticles aggregation and joule heating on unsteady MHD stagnation point flow of nanofluids over horizontal cylinder

Heliyon, Journal Year: 2023, Volume and Issue: 9(4), P. e15012 - e15012

Published: March 31, 2023

Significance of studyNanofluids with aggregation effects mediated by nanoparticles, like geothermal panels and crossflow heat exchangers, ignite new industrial interests. Polymer conversion processes have transport phenomena in the stagnation zone that must be continuously improved to raise process quality standard.Aim studyHence, current computational study examines a TiO2−C2H6O2 nanofluid's unsteady stagnation-point flow performance via shrinking horizontal cylinder. In addition, magnetic field, joule-heating viscous dissipation, nanoparticles mass suction on boundary layer are reflected.Methodology: The RK-IV shooting method is applied resolve simplified mathematical model numerically computing software MATHEMATICA. certain circumstances, comparing prior findings indicates good agreement relative error around 0%.FindingsThe implementation transfer operation may increasing settings. Unsteadiness, nanoparticle volume fraction, magnetic, curvature, Eckert number (implies operating Joule heating dissipation) all influence rate. velocity temperature profiles both increase as unsteadiness, fraction parameters increase, whereas curvature show opposite behavior. When values were changed from 2.0 2.5 φ = 0.01, rates rose 4.751%. A comparison shows has better profile, while without profile.

Language: Английский

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Analysis of heat transfer performance for ternary nanofluid flow in radiated channel under different physical parameters using GFEM

Journal of the Taiwan Institute of Chemical Engineers, Journal Year: 2023, Volume and Issue: 146, P. 104887 - 104887

Published: April 26, 2023

Language: Английский

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Numerical investigation of non-transient comparative heat transport mechanism in ternary nanofluid under various physical constraints

AIMS Mathematics, Journal Year: 2023, Volume and Issue: 8(7), P. 15932 - 15949

Published: Jan. 1, 2023

<abstract> <sec><title>Significance</title><p>The study of non-transient heat transport mechanism in mono nano as well ternary nanofluids attracts the researchers because their promising characteristics. Applications these fluids spread industrial and various engineering disciplines more specifically chemical applied thermal engineering. Due huge significance nanofluids, is organized for latest class termed along with induced magnetic field.</p> </sec> <sec><title>Methodology</title><p>The model development done via similarity equations properties nanoparticles, resulting a nonlinear mathematical model. To analyze physical results parametric values performed RKF-45 scheme.</p> <sec><title>Study findings</title><p>The reveal that velocity $ F{'}\left(\eta \right) increased increasing m = 0.1, 0.2, 0.3 {\lambda }_{1} 1.0, 1.2, 1.3 $. However, decreased {\delta Tangential G{'}\left(\eta reduces rapidly near wedge surface {M}_{1} Further, nanofluid was greater than hybrid nanofluids. Shear drag local gradient quantities were greatest nanofluid.</p> </abstract>

Language: Английский

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Computational investigation of thermal process in radiated nanofluid modulation influenced by nanoparticles (Al2O3) and molecular (H2O) diameters

Journal of Computational Design and Engineering, Journal Year: 2024, Volume and Issue: 11(2), P. 22 - 36

Published: Jan. 29, 2024

Abstract The study of variety Newtonian nanofluids subject to various physical model parameters gained much interest engineers and scientists. Owing their coolant absorption characteristics, these are broadly found in chemical engineering, biomedical engineering (expansion contraction veins arteries), detection cancer cells through magnetic nanoparticles, microchips, particularly petroleum industry. This focuses on investigation nanofluid heat transfer applications inside a channel formed by expanding/contracting walls. A new transport is introduced adding the effects nanoparticles molecular diameters, thermal radiations, walls permeability. Then, numerical code for developed executed analyze dynamics from aspects. For expanding (${\alpha }_1 = 1.0,2.0,3.0,4.0$) contracting - 1.0, 2.0, 3.0, 4.0$) walls, velocity examined maximum center. However, fluid movement working domain reverse proportion ${Re} 1.0,3.0,5.0,7.0$. Further, high absorbent (${A}_1 0.1,0.3,0.5,0.7$) controlled motion both ${\alpha &gt; 0$ &lt; 0$, respectively. addition radiation number ${Rd} 0.1,0.3,0.5,0.7$ played role catalytic parameter which imperatively increased temperature. temperature ratio ${\theta }_r reduced this decrease rapid conventional fluid.

Language: Английский

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Application of machine learning for thermal exchange of dissipative ternary nanofluid over a stretchable wavy cylinder with thermal slip

Case Studies in Thermal Engineering, Journal Year: 2024, Volume and Issue: 60, P. 104599 - 104599

Published: May 27, 2024

This article explores the enhancement of thermal exchange in a dissipative Triple-nanoparticle hybrid fluid over stretchable wavy cylindrical surface with slip effect, incorporating Python bvp algorithm artificial intelligence AI analysis numerical results. The stochastic gives enhanced and optimized results predictive modeling, randomness influencing parameters nonlinear turbulent behavior model. model has significant importance application noise reducing drag reduction devices or structures. Moreover, presented geometrical structure is useful enhancing conduction characteristic. intricate interplay constituent nanoparticles their effect on complex heat transfer optimization main focus this study. Mathematical Model PDEs flow problem converted into system ODEs by similarity transformations introducing dimensionless parameters. Numerical solutions emerged are obtained solver graphical presented. To expedite solution process enhance accuracy prediction, advanced algorithm, such as neural network machine learning technique adopted. dataset from embedded for further using Levenberg Marquardt Feed-forward Algorithm (LMFA) 10 computing neurons 4 output layers representing parametric variations. A rise speed observed higher value yield stress Newtonian-behavior i.e. Casson parameter stretching λ sheet, but shows decline turbulence . Temperature profile show descending inclination Eckert ratio Ec, Prandtl momentum-thermal diffusivity.

Language: Английский

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