MHD natural convection and entropy generation of ferrofluid in an open trapezoidal cavity partially filled with a porous medium

Astanina, Marina S.; Sheremet, Mikhail А.; Öztop, Hakan F.; Abu‐Hamdeh, Nidal H.

doi:10.1016/j.ijmecsci.2018.01.001

Cited by 168 publications

(47 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various research studies related with the entropy analysis in flows in presence of porous media have been reported. [27][28][29][30] Particularly, the entropy analysis using the model of Darcy-Forchheimer flow has been performed by various researchers. In one study, Hayat et al 31 analyzed the entropy production in a bidirectional flow of carbon nanotubes in water.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, previous published studies with flows through porous media have received the attention of investigators owing to the variety of their applications, such as separation or purification of mixtures and extraction of energy from the geothermal regions. Various research studies related with the entropy analysis in flows in presence of porous media have been reported 27‐30 . Particularly, the entropy analysis using the model of Darcy‐Forchheimer flow has been performed by various researchers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Entropy generation minimization and nonlinear heat transport in MHD flow of a couple stress nanofluid through an inclined permeable channel with a porous medium, thermal radiation and slip

et al. 2020

View full text Add to dashboard Cite

Irreversible losses and heat transport in a magnetohydrodynamic flow of a viscous, steady, incompressible, and fully developed couple stress Al 2 O 3-water nanofluid through a sloping permeable wall channel with porous medium and under the effect of radiation heat flux and slip were analyzed. The fundamental equations were solved numerically by using Runge-Kutta together with the shooting technique and the results were in qualitative agreement with an exact solution obtained for a limit case. The impacts of couple stress, Darcy number, solid nanoparticle concentrations, conduction-radiation parameter, Hartmann number and hydrodynamic slip on flow, temperature, heat transport, and entropy production were examined. It was possible to achieve values of minimum entropy production not yet reported in previous studies. In this way, optimal values of couple stress and slip were obtained. The heat transport was also explored and optimal values of slip flow and conduction-radiation parameter with maximum heat transfer were found. Finally, in addition to the alumina, the distributions of velocity, temperature, and entropy generation in TiO 2-water and Cu-water were presented for different solid nanoparticle concentrations. It was obtained that the local entropy of TiO 2-water was lower than Cu-water and Al 2 O 3-water in the channel bottom region while it was greater in the upper region.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Entropy generation minimization and nonlinear heat transport in MHD flow of a couple stress nanofluid through an inclined permeable channel with a porous medium, thermal radiation and slip

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The utilization of metal froth-filled improved tubes is broadly examined in literature 5 – 7 . Calmidi et al 8 , Zhao et al 9 , and Kim et al 10 conducted convection heat transfer implementation of metal foam-occupied channels with various structures experimentally and numerically to understand the underlying phenomena.…”

Section: Introductionmentioning

confidence: 99%

Prediction of turbulence eddy dissipation of water flow in a heated metal foam tube

Babanezhad

Behroyan

Nakhjiri

et al. 2020

Sci Rep

View full text Add to dashboard Cite

The insertion of porous metal media inside the pipes and channels has already shown a significant heat transfer enhancement by experimental and numerical studies. Porous media could make a mixing flow and small-scale eddies. Therefore, the turbulence parameters are attractive in such cases. The computational fluid dynamics (CFD) approach can predict the turbulence parameters using the turbulence models. However, the CFD is unable to find the relation of the turbulence parameters to the boundary conditions. The artificial intelligence (AI) has shown potential in combination with the CFD to build high-performance predictive models. This study is aimed to establish a new AI algorithm to capture the patterns of the CFD results by changing the system’s boundary conditions. The ant colony optimization-based fuzzy inference system (ACOFIS) method is used for the first time to reduce time and computational effort needed in the CFD simulation. This investigation is done on turbulent forced convection of water through an aluminum metal foam tube under constant wall heat flux. The ANSYS-FLUENT CFD software is used for the simulations. The x and y of the fluid nodal locations, inlet temperature, velocity, and turbulent kinetic energy (TKE) are the inputs of the ACOFIS to predict turbulence eddy dissipation (TED) as the output. The results revealed that for the best intelligence of the ACOFIS, the number of inputs, the number of ants, the number of membership functions (MFs) and the rule are 5, 10, 93 and 93, respectively. Further comparison is made with the adaptive network-based fuzzy inference system (ANFIS). The coefficient of determination for both methods was close to 1. The ANFIS showed more learning and prediction times (785 s and 10 s, respectively) than the ACOFIS (556 s and 3 s, respectively). Finding the member function versus the inputs, the value of TED is calculated without the CFD modeling. So, solving the complicated equations by the CFD is replaced with a simple correlation.

show abstract

“…They are not able to act as effective heat transfer agents. 16 , 17 Using nanoparticles is identified as an effective way to improve the main fluids’ thermal conductivity. Fluids possessing suspended nanoparticles are titled nanofluids.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Nanofluid Characteristics and Flow Pattern on Artificial Differential Evolution Learning Nodes and Fuzzy Framework

et al. 2020

View full text Add to dashboard Cite

A combination of a fuzzy inference system (FIS) and a differential evolution (DE) algorithm, known as the differential evolution-based fuzzy inference system (DEFIS), is developed for the prediction of natural heat transfer in Cu–water nanofluid within a cavity. In the development of the hybrid model, the DE algorithm is used for the training process of FIS. For this purpose, first, the case study is simulated using the computational fluid dynamic (CFD) method. The CFD outputs, including velocity in the y -direction, the temperature of the nanofluid, and the nanoparticle content ( Ø ), are employed for the learning process of the DEFIS model. By choosing the optimum number of inputs and the number of population, the underlying DEFIS variable parameters are studied. After reaching the high value of DEFIS intelligence, in the learning step, a variety of Ø values (e.g., 0.5, 1, and 2) are reviewed. For the full intelligence of DEFIS, the velocity of the nanofluid is predicted in further nodes of the cavity domain. Finally, the velocity of the nanofluid is predicted by using the data at Ø = 0.15, which are absent in the DEFIS process.

show abstract

MHD natural convection and entropy generation of ferrofluid in an open trapezoidal cavity partially filled with a porous medium

Cited by 168 publications

References 36 publications

Entropy generation minimization and nonlinear heat transport in MHD flow of a couple stress nanofluid through an inclined permeable channel with a porous medium, thermal radiation and slip

Entropy generation minimization and nonlinear heat transport in MHD flow of a couple stress nanofluid through an inclined permeable channel with a porous medium, thermal radiation and slip

Prediction of turbulence eddy dissipation of water flow in a heated metal foam tube

Prediction of Nanofluid Characteristics and Flow Pattern on Artificial Differential Evolution Learning Nodes and Fuzzy Framework

Contact Info

Product

Resources

About