Assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: Optimal artificial neural network and curve-fitting
The neural network is a technique to reduce cost and time that can be a good alternative to practical testing. This technique, which has become more important with the advancement of computer science, can also be used to predict the properties of nanofluids. To prove this claim, in this research, an...
Main Authors: | Moradikazerouni, Alireza, Hajizadeh, Ahmad, Safaei, Mohammad Reza, Afrand, Masoud, Yarmand, Hooman, Zulkifli, Nurin Wahidah Mohd |
---|---|
Format: | Article |
Published: |
Elsevier
2019
|
Subjects: |
Similar Items
-
Evaluating the effect of temperature and concentration on the thermal conductivity of ZnO-TiO2/EG hybrid nanofluid using artificial neural network and curve fitting on experimental data
by: Safaei, Mohammad Reza, et al.
Published: (2019) -
Prediction of rheological behavior of a new hybrid nanofluid consists of copper oxide and multi wall carbon nanotubes suspended in a mixture of water and ethylene glycol using curve-fitting on experimental data
by: Tian, Zhe, et al.
Published: (2020) -
Predicting the viscosity of multi-walled carbon nanotubes/water nanofluid by developing an optimal artificial neural network based on experimental data
by: Afrand, M., et al.
Published: (2016) -
Ethanediol antifreeze
by: 8096 British Standards Institution
Published: (1959) -
Smart Antifreeze Hydrogels with Abundant Hydrogen Bonding for Conductive Flexible Sensors
by: Bailin Dai, et al.
Published: (2022-06-01)