Modeling thermal conductivity augmentation of nanofluids using diffusion neural networks

Papari, Mohammad Mehdi; Yousefi, Fakhri; Moghadasi, Jalil; Karimi, Hajir; Campo, Antônio

doi:10.1016/j.ijthermalsci.2010.09.006

Cited by 116 publications

(36 citation statements)

References 57 publications

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“…In early works by Huseyin and Muhammet [229], Hojjat et al [230] and Papari et al [231], artificial neural network (ANN) had been applied for the prediction of thermal conductivity of different nanofluids with good agreement with the values obtainable in the literature. Less than a year after, Mehrabi et al [232], using an FCM-based neuro-fuzzy inference system and genetic algorithm-polynomial neural network alongside experimental data, developed two new models for the prediction of thermal conductivity of Al2O3-water nanofluids.…”

mentioning

confidence: 85%

The Viscosity of Nanofluids: A Review of the Theoretical, Empirical, and Numerical Models

Meyer

Adio

Sharifpur

et al. 2015

Heat Transfer Engineering

204

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The enhanced thermal characteristics of nanofluids have made it one of the fastest-growing research areas in the last decade. Numerous researches have shown the merits of nanofluids in heat transfer equipment. However, one of the problems is the increase in viscosity due to the suspension of nanoparticles. This viscosity increase is not desirable in the industry, especially when it involves flow, such as in heat exchanger or micro-channel applications INTRODUCTIONColloidal suspension dates back to Maxwell's study in 1873 [1]. Though the idea behind his study was vivid, the imposed problems were too enormous for profitable engineering solutions [2]. In 1995, Choi [3] came up with a pioneering idea based on Maxwell's study and suspended ultrafine particles (nanoparticles) in conventional heat transfer fluids. His invention has opened up a myriad of opportunities in research and development. Nanofluids descriptively are colloidal suspensions containing metallic (Ag, Au, Al, Cu, Ni, etc.), non-metallic (single-and multi-wall carbon nanotube (SWCNT and MWCNT), Si, Graphene, etc.), metallic oxide (Al2O3, CuO, NiO2,TiO2, etc.) and oxides of non-metals (SiO2, SiC, MgO, CaCO3, etc.) nanoparticles suspended in conventional heat transfer fluids such as water, engine oil, ethylene glycol, transformer oil, gear oil or mixture of two or more heat transfer fluids [2,3]. When compared with previous microparticle suspensions in conventional heat transfer fluids, it is a special type of fluid with numerous applications potentials because of its enhanced thermal conductivity, stability and homogeneity [3,4]. Microscale particles in suspensions lead to abrasion, clogging of flow paths, pressure drop and high pumping power requirements, therefore, its sustainability was impossible. Besides, nanofluids can reduce the pumping power in engineering equipment significantly and do not pose the problem of clogging and abrasion of equipment flow paths [5][6][7][8]. Therefore, the design and engineering of physical systems are now being tailored towards using nanofluid as working fluid.The impact of colloidal suspension cuts across the fields of science, biological science, medical, pharmaceutical and engineering. In the context of sustainable energy development and thermal management, nanofluids are becoming more and more significant as the need for efficient thermal management is of paramount importance. Moreover, the level of miniaturisation of devices today as technology advances is overwhelming. Devices such as microprocessors, microelectromechanical systems (MEMS), nanoelectromechanical systems (NEMS), microchannels and lab-on-chips come with high-density heat flux that needs quick heat removal for 3 efficient performance, stability and durability, which, from all indications, could be provided by nanofluids for now [9][10][11][12]. The following are also emerging areas of applications of nanoparticles and nanofluids: (i) they could be useful in medicine in the targeted treatment of malignant cells without damaging healthy tis...

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mentioning

confidence: 85%

The Viscosity of Nanofluids: A Review of the Theoretical, Empirical, and Numerical Models

Meyer

Adio

Sharifpur

et al. 2015

Heat Transfer Engineering

204

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show abstract

“…Kurt and Kayfeci [11] developed an artificial neural network model to predict the thermal conductivities of ethylene glycol/water-based nanofluids by taking into account temperatures, volume concentrations and densities. Papari et al [12] modelled the thermal conductivity of single-wall carbon nanotubes and multi-wall carbon nanotubes dispersed into several base fluids by using a diffusion neural network.…”

Section: Introductionmentioning

confidence: 99%

Viscosity of nanofluids based on an artificial intelligence model

Mehrabi

Sharifpur

Meyer

2013

International Communications in Heat and Mass Transfer

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“…Amongst the different thermo-physical properties of nanofluids, viscosity and thermal conductivity were more interested for the researchers [7][8][9][10]. A few number of researchers worked on other properties such as specific heat and density.…”

Section: Introductionmentioning

confidence: 99%

The RSM approach to develop a new correlation for density of metal-oxide aqueous nanofluids

Montazer

Salami

Yarmand

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Modeling thermal conductivity augmentation of nanofluids using diffusion neural networks

Cited by 116 publications

References 57 publications

The Viscosity of Nanofluids: A Review of the Theoretical, Empirical, and Numerical Models

The Viscosity of Nanofluids: A Review of the Theoretical, Empirical, and Numerical Models

Viscosity of nanofluids based on an artificial intelligence model

The RSM approach to develop a new correlation for density of metal-oxide aqueous nanofluids

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