Numerical simulation of double diffusive mixed convective nanofluid flow and entropy generation in a square porous enclosure

Hussain, Shafqat; Mehmood, Khalid; Sagheer, Muhammad; Yamin, Muhammad

doi:10.1016/j.ijheatmasstransfer.2018.02.082

Cited by 59 publications

(19 citation statements)

References 51 publications

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“…Firstly, Pera and Gebhart [ 18 ] investigated numerically this phenomenon for vertical laminar fluid motions. After this, numerous studies have been investigated on double diffusion [ 19 – 21 ]. Nag and Molla [ 22 ] studied the double-diffusive natural convention effect on non-Newtonian nanofluid in square cavity.…”

Section: Introductionmentioning

confidence: 99%

“…Sometimes, it is not possible to find an exact solution of a problem analytically for this purpose; researcher uses different numerical and semi-numerical techniques to solve the problem. Some techniques are homotopy perturbation method [ 34 ], Keller–Box method [ 35 ], spectral relaxation method [ 36 ], Galerkin finite element method [ 21 ] and many others [ 37 – 39 ]. All the above-mentioned cited studies on different nanofluidic systems are solved by using different numerical and semi-numerical methods, but AI-based numerical computing paradigms are important to exploit double-diffusive free convection nanofluid flow model (DDFC-NFM) due to their worthiness and efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Further analysis of double-diffusive flow of nanofluid through a porous medium situated on an inclined plane: AI-based Levenberg–Marquardt scheme with backpropagated neural network

Shoaib

Tabassum

Raja

et al. 2022

J Braz. Soc. Mech. Sci. Eng.

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The present article exploits a novel application of AI-based Levenberg–Marquardt scheme with backpropagated neural network (LMS–BPNN) to analyze the double-diffusive free convection nanofluid flow model (DDFC-NFM) over an inclined plate in the existence of Brownian motion and thermophoresis properties embedded in a porous medium. The governing PDEs representing DDFC-NFM are transformed into system of nonlinear ODEs by applying suitable transformation. The reference data set is generated from Lobatto III-A numerical solver by variation of magnetic field parameter ( M ), thermal Grashof number (Gr), angle of inclination ( α ), Brownian motion parameter (Nb), Dufour-solutal Lewis number (Ld), modified Dufour parameter (Nd) and thermophoresis parameter (Nt) for all scenarios of the designed LMS–BPNN. The approximate solution and its comparison with standard solution are analyzed by execution of training, testing and validation procedure of the designed LMS–BPNN. The effectiveness and reliable performance of LMS–BPNN are endorsed with MSE-based fitness curve, regression analysis, error histogram analysis and correlation index. Results reveal that velocity increases with the rise in Gr, whereas reverse trend has been noticed for angle of inclination and magnetic field parameter and the temperature profile increases with the increase in Nb, Nd and Nt. The solutal concentration profile increases with the increment in Ld, while an increase in Nd causes a decrease in it. When Nt increases, the enhancement in the nanoparticle volume frictions occurs, but an opposite behavior is depicted for Brownian motion parameter.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Further analysis of double-diffusive flow of nanofluid through a porous medium situated on an inclined plane: AI-based Levenberg–Marquardt scheme with backpropagated neural network

Shoaib

Tabassum

Raja

et al. 2022

J Braz. Soc. Mech. Sci. Eng.

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“…Hussain et al [14] examined the mixed convection of nanofluids in a chemically-reactive porous cavity. They showed that heat transfer increased with increasing volume fraction of nanoparticles, Darcy number and Richardson number, but that it decreased with increasing Lewis number and chemical reaction rate.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Predicting the Heat Convection and Thermodynamics of an External Flow of Hybrid Nanofluid

Alizadeh

Abad

Fattahi

et al. 2021

Journal of Energy Resources Technology

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This study numerically investigates heat convection and entropy generation in a hybrid nanofluid (Al2O3–Cu–water) flowing around a cylinder embedded in porous media. An artificial neural network is used for predictive analysis, in which numerical data are generated to train an intelligence algorithm and to optimize the prediction errors. Results show that the heat transfer of the system increases when the Reynolds number, permeability parameter, or volume fraction of nanoparticles increases. However, the functional forms of these dependencies are complex. In particular, increasing the nanoparticle concentration is found to have a nonmonotonic effect on entropy generation. The simulated and predicted data are subjected to particle swarm optimization to produce correlations for the shear stress and Nusselt number. This study demonstrates the capability of artificial intelligence algorithms in predicting the thermohydraulics and thermodynamics of thermal and solutal systems.

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“…Chamkha et al 18 analyzed numerically the conjugate natural convection in a semicircular cavity with a solid shell of finite thickness filled with a hybrid nanofluid. Hussain et al 19 numerically simulated a double-diffusive mixed convective alumina water nanofluid flow in a square porous lid-driven cavity.…”

Section: Introductionmentioning

confidence: 99%

Bioconvection in a porous square cavity containing gyrotactic microorganisms under the effects of heat generation/absorption

Jamuna

Balla

2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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In this paper, the bioconvective nanofluid flow in a porous square cavity containing gyrotactic microorganisms in the presence of heat generation/absorption is investigated. The bioconvection flow in porous medium is formulated based on Darcy model of Boussinesq approximation. Galerkin finite elements method is employed to solve the governing equations. The numerical results are obtained and discussed the effect of parameters such as Rayleigh number Ra, bioconvection number Rayleigh number Rb, Peclet number Pe, Lewis number Le, Brownian motion Nb, Thermophoresis Nt, Schmidt number Sc, Prandtl number Pr, heat generation/absorption Q and buoyancy ratio Nr. Also, the average Sherwood number, average Nusselt number and average density number are discussed. The present solutions are validated with existing studies under limited cases. It is found that Peclet number and buoyancy ratio show a significant increasing effect on the streamlines, isotherms, isoconcentrations of nanoparticles and microorganisms. The heat generation/absorption reverses the patterns of the flow, temperature distribution and isoconcentrations of nanoparticles and microorganisms.

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Numerical simulation of double diffusive mixed convective nanofluid flow and entropy generation in a square porous enclosure

Cited by 59 publications

References 51 publications

Further analysis of double-diffusive flow of nanofluid through a porous medium situated on an inclined plane: AI-based Levenberg–Marquardt scheme with backpropagated neural network

Further analysis of double-diffusive flow of nanofluid through a porous medium situated on an inclined plane: AI-based Levenberg–Marquardt scheme with backpropagated neural network

A Machine Learning Approach to Predicting the Heat Convection and Thermodynamics of an External Flow of Hybrid Nanofluid

Bioconvection in a porous square cavity containing gyrotactic microorganisms under the effects of heat generation/absorption

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