Predicting Physical Properties of Nanofluids by Computational
Modeling
Natalia Sizochenko
Michael Syzochenko
Agnieszka Gajewicz
Jerzy Leszczynski
Tomasz Puzyn
10.1021/acs.jpcc.6b08850.s003
https://acs.figshare.com/articles/dataset/Predicting_Physical_Properties_of_Nanofluids_by_Computational_Modeling/4558621
The
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 <i>R</i><sup>2</sup> = 0.81 and root-mean-squared error RMSE = 0.055; the model
for viscosity is characterized by <i>R</i><sup>2</sup> =
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.
2016-12-27 00:00:00
QSPR
root-mean-squared error RMSE
nanoparticle
target properties
conductivity
surface area ratio
viscosity increase
nanofluid
150 data points
determination coefficient R 2
model