scholarly journals Effect of Temperature and Multiwalled Carbon Nanotubes Concentration on Thermophysical Properties of Water Base Nanofluid

2017 ◽  
Vol 7 (4) ◽  
pp. 151-160
Author(s):  
Mohit Thakur et al., Mohit Thakur et al., ◽  
Keyword(s):  
Carbon Nanotubes ◽  
Multiwalled Carbon Nanotubes ◽  
Thermophysical Properties ◽  
Effect Of Temperature ◽  
Multiwalled Carbon ◽  
Water Base

scholarly journals Evaluation of Physicothermal Properties of Solar Thermic Fluids Dispersed with Multiwalled Carbon Nanotubes and Prediction of Data Using Artificial Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
K. Ch. Sekhar ◽  
Raviteja Surakasi ◽  
ilhan Garip ◽  
S. Srujana ◽  
V. V. Prasanna Kumar ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Carbon Nanotubes ◽  
Ethylene Glycol ◽  
Multiwalled Carbon Nanotubes ◽  
Thermophysical Properties ◽  
Viscosity Measurement ◽  
Multiwalled Carbon ◽  
Constant Thermal Conductivity ◽  
Mathematical Computation ◽  
Surfactant Assisted

A review of multiwalled carbon nanotubes as solar thermic fluids and their thermophysical properties is done in this article. The basic fluids were ethylene glycol and water in ratios of 100 : 0, 90 : 10, and 80 : 20. To investigate how surface modification impacts thermophysical properties, three base fluids were combined with surfactant-assisted MWCNTs and oxidized MWCNTs in weight fractions of 0.125, 0.25, and 0.5 percent, respectively. It takes two months to check whether the dispersion stays constant. Thermal conductivity and viscosity measurement were done using heated discs and Anton Paar viscometers. Using oxidized MWCNTs to disperse, the base fluids increased thermal conductivity by 15% to 24%. Surfactant-assisted MWCNTs in nanofluids perform worse than oxidized MWCNTs. The dynamic viscosity of nanofluids is higher than that of basic fluids between 50 and 70°C. During a mathematical computation, all of the MWCNT weight fractions and ethylene glycol volume percentages are included. The correlation may be a good fit for the experimental data within limits. The characteristics are forecasted using feed-forward backpropagation. In this research, buried layer neurons and factors are examined.

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