jp6b08850_si_003.zip (10.02 kB)
Predicting Physical Properties of Nanofluids by Computational Modeling
dataset
posted on 2016-12-27, 00:00 authored by Natalia Sizochenko, Michael Syzochenko, Agnieszka Gajewicz, Jerzy Leszczynski, Tomasz PuzynThe
focal point of the
current contribution was to develop global quantitative structure–property
relationship (QSPR) models for nanofluids. Two target properties,
thermal conductivity and viscosity of nanofluids, were thoroughly
investigated. Under this investigation, a new database of thermal
conductivity and viscosity of nanofluids (more than 150 data points)
was created. A hierarchical system of molecular representation reflecting
features of nanoparticle’s structure at the different levels
of organization was introduced. Also, size-dependent, volume-dependent,
and intensive parameters were calculated. The model for thermal conductivity
is characterized by determination coefficient R2 = 0.81 and root-mean-squared error RMSE = 0.055; the model
for viscosity is characterized by R2 =
0.79 and RMSE = 0.234. Developed models are in
agreement with modern theories of nanofluids behavior. Size- and concentration-related
behavior of target properties were discussed. Findings suggest that
the increase in surface area ratio and interfacial layer thickness
and decrease in nanoparticles size lead to thermal conductivity and
viscosity increase. Thermal conductivity and viscosity increase with
an increase in weighted fraction-dependent parameters. Up-to-date,
reliable theoretical models were created only for a single type of
nanoparticles. In this article, developed models can simultaneously
predict the thermal conductivity and viscosity in an effective way
using both size and volume concentration of nanofluid.