  Heat transfer characteristics of nanofluids have been extensively studied
since the 1990s. Research investigations show that the suspended nanoparticles
significantly alter the suspension's thermal properties. The thermal
conductivity of nanofluids is one of the properties that is generally found to
be greater than that of the base fluid. This increase in thermal conductivity
is found to depend on several parameters. Several theories have been proposed
to model the thermal conductivities of nanofluids, but there is no reliable
universal theory yet to model the anomalous thermal conductivity of nanofluids.
In recent years, supervised data-driven methods have been successfully employed
to create surrogate models across various scientific disciplines, especially
for modeling difficult-to-understand phenomena. These supervised learning
methods allow the models to capture highly non-linear phenomena. In this work,
we have taken advantage of existing correlations and used them concurrently
with available experimental results to develop more robust surrogate models for
predicting the thermal conductivity of nanofluids. Artificial neural networks
are trained using the transfer learning approach to predict the thermal
conductivity enhancement of nanofluids with spherical particles for 32
different particle-fluid combinations (8 particles materials and 4 fluids). The
large amount of lower accuracy data generated from correlations is used to
coarse-tune the model parameters, and the limited amount of more trustworthy
experimental data is used to fine-tune the model parameters. The transfer
learning-based models' results are compared with those from baseline models
which are trained only on experimental data using a goodness of fit metric. It
is found that the transfer learning models perform better with goodness of fit
values of 0.93 as opposed to 0.83 from the baseline models.
