Dispersion behavior and thermal conductivity characteristics of Al2O3–H2O nanofluids

Zhu, Dongsheng; Li, Xinfang; Wang, Nan; Wang, Xianju; Gao, Jinwei; Li, Hua

doi:10.1016/j.cap.2007.12.008

Cited by 437 publications

(125 citation statements)

References 32 publications

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“…have improved thermal properties such as thermal conductivity, heat capacity and convective heat transfer coefficient [1,2]. Heat transfer fluid that possesses higher thermal conductivity provides better heat removal capacity and also could support the reduction of the size and weight of heat exchanger in line with the global sustainable development and energy sustainability.…”

Section: Nanofluid Is a Modified Heat Transfer Fluid Produced By Homomentioning

confidence: 99%

Experimental investigation and model development for effective viscosity of MgO–ethylene glycol nanofluids by using dimensional analysis, FCM-ANFIS and GA-PNN techniques

Adio

Mehrabi

Sharifpur

et al. 2016

International Communications in Heat and Mass Transfer

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Desirable effective viscosity behaviour is an essential transport property required for the effective utilisation of nanofluids in industrial systems as well as other applications. Viscosity influences significantly the pumping power and heat transfer effectiveness in a thermal system since Reynolds and Prandtl numbers are functions of viscosity. In this study, the optimum energy required for the preparation of MgO-ethylene glycol (MgO-EG) nanofluids was determined by varying the ultrasonication energy input into the preparation process. The uniformly dispersed nanofluids were characterised and the viscosity measurements were carried out as a function of temperature (20 to Therefore, new correlation is proposed using the method of dimensional analysis and considering essential factors, including nanoparticle size, volume fraction temperature, capping layer thickness, viscosity of the base fluid, the density of base fluid and the density of nanofluid as input parameters.Furthermore, genetic algorithm-polynomial neural network (GA-PNN) and fuzzy C-means clusteringbased adaptive neuro-fuzzy inference system (FCM-ANFIS) was used to model the effective viscosity of the MgO-EG nanofluids considering the parameters mentioned above. The results of all the modelling techniques showed good agreement with the experimental data.

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Section: Nanofluid Is a Modified Heat Transfer Fluid Produced By Homomentioning

confidence: 99%

Experimental investigation and model development for effective viscosity of MgO–ethylene glycol nanofluids by using dimensional analysis, FCM-ANFIS and GA-PNN techniques

Adio

Mehrabi

Sharifpur

et al. 2016

International Communications in Heat and Mass Transfer

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show abstract

“…There is a lack of reported research on hybrid combined models for the effective thermal conductivity that takes into consideration all major mechanisms plus other important recognized effects like particle settling down time [75][76][77][78], temperature [79], pH [80][81][82], dispersion [83] and the particle size effect on surface contact of liquid/particle interaction [41,[84][85][86]. However, the following models are valuable because they included some of these effects.…”

Section: Hybrid Model and Other Effectsmentioning

confidence: 99%

A Review of Thermal Conductivity Models for Nanofluids

Aybar

Sharifpur

Azizian

et al. 2015

Heat Transfer Engineering

198

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“…Например, оксиды железа и алюминия находят применение в ме-дицине из-за отсутствия токсического действия на организм [1]. Помимо ме-дицинского применения, наночастицы оксидов металлов являются основой для получения защитных покрытий, керамики, катализаторов и новых эф-фективных теплоносителей [2].…”

Section: Introductionunclassified

“…Несмотря на насущную необходи-мость, в литературе имеется весьма ог-раниченное число работ, посвященных детальному изучению стабилизации наносуспензий оксидов металлов ПАВ [2,3]. На современном этапе исследо-вания пока только точно установлено, что стабилизация суспензий идет за счет адсорбции ПАВ на наночастицах.…”

Section: Introductionunclassified

Adsorption of TX-100 and SDBS on the surface of alumina and maghemite nanoparticles from aqueous solutions

et al. 2014

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Adsorption equilibriums in aqueous aluminum and iron oxides nanosuspensions stabilized by SDBS and TX-100 were investigated using UV spectrophotometry. It was established that the non-ionic surfactant TritonX-100 is not adsorbed from aqueous solution on a hydrophilic surface of both aluminum and iron oxide nanoparticles. At the same time adsorption of the anionic surfactant SDBS was observed in both oxides nanoparticles. In the investigated range of concentrations adsorption isotherms SDBS from aqueous solution on the surfaces of nanoparticles Al2O3 and γ-Fe2O3 not reach saturation. The share of the particles surface occupied by surfactant molecules were estimated based on the value of an area of molecules SDBS in the adsorption layer, which was derived from the isotherm of surface tension (0.10 nm2). The calculations showed that at the investigated concentrations SDBS Al2O3 employed approximately 30 % of surface of nanoparticles, and for γ-Fe2O3 – up to 10%.

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Dispersion behavior and thermal conductivity characteristics of Al2O3–H2O nanofluids

Cited by 437 publications

References 32 publications

Experimental investigation and model development for effective viscosity of MgO–ethylene glycol nanofluids by using dimensional analysis, FCM-ANFIS and GA-PNN techniques

Experimental investigation and model development for effective viscosity of MgO–ethylene glycol nanofluids by using dimensional analysis, FCM-ANFIS and GA-PNN techniques

A Review of Thermal Conductivity Models for Nanofluids

Adsorption of TX-100 and SDBS on the surface of alumina and maghemite nanoparticles from aqueous solutions

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