A modified model for thermal conductivity of carbon nanotube-nanofluids

Abstract

Carbon nanotubes (CNTs) are one of the most valuable materials with high thermal conductivity (above 1750 W/m K compared to thermal conductivity of Ag 419 W/m K). Owing to their very high thermal conductivity, CNTs are one of the most suitable nanoadditives in fabricating the nanofluid with thermal conductivities that are significantly higher than those of the parent liquids even when the CNTs’ concentrations are negligible. This work presents a modified model for predicting the thermal conductivity of carbon nanotube-nanofluids (CNT-nanofluids), which take into consideration the effects of size, volume fraction, and thermal conductivity of CNTs as well as the properties of base liquid. The modified model is found to correctly predict the trends observed in experimental data for different combinations of CNT-nanofluids with varying concentrations.

Enhanced thermal conductivity of nanofluid-based ethylene glycol containing Cu nanoparticles decorated on a Gr–MWCNT hybrid material

“…4,5 Since the discovery of carbonaceous materials with very high thermal conductivity, such as carbon nanotubes (CNT), graphene, graphene oxide (GO), 6 several hundred studies concerning carbonaceous materials-based NFs have been conducted and presented. [7][8][9][10][11][12] These demonstrate that a signicant thermal conductivity enhancement of NFs was obtained both in theoretical and in experimental studies. In addition, graphene decorated by metallic or ceramic particles for nanouid application has also been presented.…”

Influence of defects induced by chemical treatment on the electrical and thermal conductivity of nanofluids containing carboxyl-functionalized multi-walled carbon nanotubes

“…In fact, Thang et al model also conrmed that their model only validates for nanouids with the CNT content below 1 vol%. 67 There are many factors lead to the erroneous results calculated by the mentioned models such as the effect of Brown motion, the thermal conductivity of interfacial layer (K i ), type of CNTs, etc. In which, the effect of K i between CNTs and base uid on the thermal conductivity of CNT based nanouids is well-known, but K i was not taken into account for the calculation.…”

Application of ANFIS and LSSVM strategies for estimating thermal conductivity enhancement of metal and metal oxide based nanofluids

“…Efficiency and consistency of any developed algorithm is extremely function of utilized real data points which need to be precise and easily accessible in order to carry out the algorithm (Baghban, Bahadori, et al, 2017;. A general model was evolved in the present work utilizing 1109 actual data points from reliable papers for 29 various fluids (Chen, Xie, Li, & Yu, 2008;Choi, Zhang, Yu, Lockwood, & Grulke, 2001; Chon, Kihm, Lee, & Choi, 2005;Das, Putra, Thiesen, & Roetzel, 2003;Eastman, Choi, Li, Yu, & Thompson, 2001;Fedele, Colla, & Bobbo, 2012;Godson, Raja, Lal, & Wongwises, 2010;Halelfadl, Maré, & Estellé, 2014;Hwang et al, 2006;Jiang, Ding, & Peng, 2009;Jiang et al, 2014;Kazemi-Beydokhti, Heris, Moghadam, Shariati-Niasar, & Hamidi, 2014;Khedkar, Sonawane, & Wasewar, 2012;Kim, Choi, & Kim, 2007;Lee, Choi, Li, & Eastman, 1999;Mintsa, Roy, Nguyen, & Doucet, 2009;Moghadassi, Hosseini, & Henneke, 2010;Mondragón, Segarra, Martínez-Cuenca, Juliá, & Jarque, 2013;Murshed, 2012;Murshed et al, 2008;Nan, Shi, & Lin, 2003;Pastoriza-Gallego, Lugo, Cabaleiro, Legido, & Piñeiro, 2014;Patel, Sundararajan, & Das, 2010;Rashmi, Khalid, Ismail, Saidur, & Rashid, 2015;Thang, Khoi, & Minh, 2015;Zerradi, Ouaskit, Dezairi, Loulijat, & Mizani, 2014). The details of used nanofluids as ranges of void fraction, ranges of particle diameter, ranges of temperature and their references were reported in Table S1.…”