The rheological behavior of MWCNTs–ZnO/Water–Ethylene glycol hybrid non-Newtonian nanofluid by using of an experimental investigation

Yan, Shu‐Rong; Toghraie, Davood; Abdulkareem, Lokman A.; Alizadeh, As’ad; Barnoon, Pouya; Afrand, Masoud

doi:10.1016/j.jmrt.2020.05.018

Cited by 97 publications

(14 citation statements)

References 22 publications

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“…The research for finding the aspect of non-Newtonian hybrid nanofluid also needed to be done. Latterly, Yan et al 37 have conducted an investigation towards the rheological behaviour of non-Newtonian hybrid nanofluid for a powered pump. They reported at the highest volume fraction hybrid nanofluid, the viscosity reduced at most 21%.…”

Section: Introductionmentioning

confidence: 99%

The improved thermal efficiency of Prandtl–Eyring hybrid nanofluid via classical Keller box technique

Jamshed

Băleanu

Nasir

et al. 2021

Sci Rep

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Prandtl–Eyring hybrid nanofluid (P-EHNF) heat transfer and entropy generation were studied in this article. A slippery heated surface is used to test the flow and thermal transport properties of P-EHNF nanofluid. This investigation will also examine the effects of nano solid tubes morphologies, porosity materials, Cattaneo–Christov heat flow, and radiative flux. Predominant flow equations are written as partial differential equations (PDE). To find the solution, the PDEs were transformed into ordinary differential equations (ODEs), then the Keller box numerical approach was used to solve the ODEs. Single-walled carbon nanotubes (SWCNT) and multi-walled carbon nanotubes (MWCNT) using Engine Oil (EO) as a base fluid are studied in this work. The flow, temperature, drag force, Nusselt amount, and entropy measurement visually show significant findings for various variables. Notably, the comparison of P-EHNF's (MWCNT-SWCNT/EO) heat transfer rate with conventional nanofluid (SWCNT-EO) results in ever more significant upsurges. Spherical-shaped nano solid particles have the highest heat transport, whereas lamina-shaped nano solid particles exhibit the lowest heat transport. The model's entropy increases as the size of the nanoparticles get larger. A similar effect is seen when the radiative flow and the Prandtl–Eyring variable-II are improved.

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Section: Introductionmentioning

confidence: 99%

The improved thermal efficiency of Prandtl–Eyring hybrid nanofluid via classical Keller box technique

Jamshed

Băleanu

Nasir

et al. 2021

Sci Rep

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“…In addition, fluid viscosity is used to calculate the required power of pumps, mixing processes, piping systems, liquid pulverization, fluid storage, fluid injection, and fluid transport 20 . Hybrid nanofluids combine two heterogeneous nanoparticles (hybrid nanocomposites) suspended in the base fluid 21 – 24 . The purpose of using hybrid nanocomposites in an intermediate fluid is to improve the heat transfer characteristics of the base fluid through the combined thermophysical characteristics of effective nanomaterials 25 – 28 .…”

Section: Introductionmentioning

confidence: 99%

A well-trained artificial neural network for predicting the rheological behavior of MWCNT–Al2O3 (30–70%)/oil SAE40 hybrid nanofluid

Esfe

Eftekhari

Hekmatifar

et al. 2021

Sci Rep

Self Cite

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In this study, the influence of different volume fractions ($$\phi$$ ϕ ) of nanoparticles and temperatures on the dynamic viscosity ($$\mu_{nf}$$ μ nf ) of MWCNT–Al2O3 (30–70%)/oil SAE40 hybrid nanofluid was examined by ANN. For this reason, the $$\mu_{nf}$$ μ nf was derived for 203 various experiments through a series of experimental tests, including a combination of 7 different $$\phi$$ ϕ , 6 various temperatures, and 5 shear rates. These data were then used to train an artificial neural network (ANN) to generalize results in the predefined ranges for two input parameters. For this reason, a feed-forward perceptron ANN with two inputs (T and $$\phi$$ ϕ ) and one output ($$\mu_{nf}$$ μ nf ) was used. The best topology of the ANN was determined by trial and error, and a two-layer with 10 neurons in the hidden layer with the tansig function had the best performance. A well-trained ANN is created using the trainbr algorithm and showed an MSE value of 4.3e−3 along 0.999 as a correlation coefficient for predicting $$\mu_{nf}$$ μ nf . The results show that an increase $$\phi$$ ϕ has a significant effect on $$\mu_{nf}$$ μ nf value. As $$\phi$$ ϕ increases, the viscosity of this nanofluid increases at all temperatures. On the other hand, with increasing temperature, the viscosity of this nanofluid decreases. Based on all of the diagrams presented for the trained ANNs, we can conclude that a well-trained ANN can be used as an approximating function for predicting the $$\mu_{nf}$$ μ nf .

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“…Different numerical methods have been developed to estimate several properties and behavior of dispersions and nanofluids (dispersions of nanometric particles), such as dynamic viscosity (Goodarzi et al 2019 ; Khodadadi et al 2019 ), thermal conductivity (Shahsavar et al 2019 ; Soltani et al 2020 ), flow and heat transfer (Moradi et al 2019 ) and rheological behavior (Hussein et al 2018 ; Yan et al 2020 ). However, for this research theoretical equations (Tsakalakis and Stamboltzis 2004 ; Mhadhbi et al 2013 ) and industrial data (Campbell 2004 ; Ibarrondo 2008 ), which consider the different variables involved in the two production processes, were evaluated.…”

Section: Introductionmentioning

confidence: 99%

Novel sustainable metallic powder production process with water used as milling medium

Salazar

Carreόn

Lagos

2021

Clean Techn Environ Policy

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Graphic abstract Today, Fe–Al intermetallic compounds are receiving a great interest from the mechanical, aerospace, and biomedical industries. A novel production process for Fe–Al intermetallic powders based on the generation of metallic tapes by rapid solidification and disintegration by water vapor was proposed. In this research work, a comparison is made between the energy required to manufacture of Fe–Al powder using the aforementioned process and one of the most commonly used manufacturing processes within the industry such as mechanical alloying. In addition, some other benefits of the proposed manufacturing process are analyzed. To carry out this comparison, the theoretical equations that take into account the most important variables involved during the process such as the type of material and hardness, the initial and final particle size, the grinding stages and the heating of the treatment powder were considered. In the case of calculating the energy required for the new proposed process, the two main stages were considered such as (1) the production of FeAl metal tape and (2) the subsequent transformation of the tape into powder by means of injection water vapor. For the first stage, the CASTRIP process is considered, and for the second stage, the energy required for the generation steam. Although the calculations may have certain limitations, it is obvious that the energy required to Fe–Al powder production using the new process is much lower than that required by mechanical alloying, resulting in at least three orders of magnitude lower (2.75 × 10 6 versus 2.206 × 10 9 kJ/ton). This lower energy implies considerable economic savings in the production process. On the other hand, when using water as a grinding medium during the process, it results in less environmental and acoustic pollution, less manipulation risks for humans and finally, no harmful agents or additives are used, making the proposed process sustainable.

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The rheological behavior of MWCNTs–ZnO/Water–Ethylene glycol hybrid non-Newtonian nanofluid by using of an experimental investigation

Cited by 97 publications

References 22 publications

The improved thermal efficiency of Prandtl–Eyring hybrid nanofluid via classical Keller box technique

The improved thermal efficiency of Prandtl–Eyring hybrid nanofluid via classical Keller box technique

A well-trained artificial neural network for predicting the rheological behavior of MWCNT–Al2O3 (30–70%)/oil SAE40 hybrid nanofluid

Novel sustainable metallic powder production process with water used as milling medium

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