Background:
Because of nanofluids applications in improvement of heat transfer rate in
heating and cooling systems, many researchers have conducted various experiments to investigate
nanofluid's characteristics more accurate. Thermal conductivity, electrical conductivity, and heat
transfer are examples of these characteristics.
Method:
This paper presents a modeling and validation method of heat transfer coefficient and pressure
drop of functionalized aqueous COOH MWCNT nanofluids by artificial neural network and
proposing a new correlation. In the current experiment, the ANN input data has included the volume
fraction and the Reynolds number and heat transfer coefficient and pressure drop considered as ANN
outputs.
Results:
Comparing modeling results with proposed correlation proves that the empirical correlation
is not able to accurately predict the experimental output results, and this is performed with a lot more
accuracy by the neural network. The regression coefficient of neural network outputs was equal to
99.94% and 99.84%, respectively, for the data of relative heat transfer coefficient and relative pressure
drop. The regression coefficient for the provided equation was also equal to 97.02% and
77.90%, respectively, for these two parameters, which indicates this equation operates much less
precisely than the neural network.
Conclusion:
So, relative heat transfer coefficient and pressure drop of nanofluids can also be modeled
and estimated by the neural network, in addition to the modeling of nanofluid’s thermal conductivity
and viscosity executed by different scholars via neural networks.
Cooling of the heating device in conditions of time-varying ambient temperature, heat transfer coefficient and radiation temperature of the fencing
