On the assessment of specific heat capacity of nanofluids for solar energy applications: Application of Gaussian process regression (GPR) approach
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Date
2020-10-29
Journal Title
Journal ISSN
Volume Title
Publisher
Journal of Energy Storage
Abstract
To characterize the performance of nanofluids for heat transfer applications in solar systems, an accurate estimation
of their specific heat capacity (SHC) is of paramount importance. To this end, having such properties of
nanofluids via computational approaches has gained attention as an effective method to eliminate the timeconsuming
process of experimental investigations. This study focuses on modeling the SHC of different
carbon-based and metal oxide-based nanoparticles dispersed in various base fluids. Herein, we propose a novel
data-driven dynamic model based on the Gaussian process regression (GPR) technique in comparison with the
random forest (RF) approach and generalized regression neural network (GRNN) to predict the SHC of nanofluids.
The developed models employ the solid volume fraction (ϕ), temperature (T), mean diameter of nanoparticle
(Dp), and SHC of base fluid (CPBase) as the input parameters. The data has been collected from 10 reliable
references. The results showed that the GPR model (R=0.99974, RMSE=0.01506 J/K.g) shows superior performance
than the results of the RF (R=0.99761, RMSE=0.04598 J/K.g) and GRNN (R=0.99563,
RMSE=0.06085 J/K.g). The results proved that the developed model would accurately estimate the SHC of the
studied nanofluids. In addition, the sensitivity analysis of the dependence of input variables on the SHC of
nanofluids revealed that the mean diameter of nanoparticles and the SHC of base fluid are the major critical
factors in the determination of SHC of nanofluids.
Description
Staff Publication
Keywords
Specific heat capacity, Solar energy, Nanofluids, Volume fraction, Gaussian process regression
Citation
Jamei,M.; Ahmadianfar, I. ; Olumegbon, I.A.; Karbasi, M. ; Asadi, A. (2021) On the assessment of specific heat capacity of nanofluids for solar energy applications: Application of Gaussian process regression (GPR) approach. Journal of Energy Storage, 33, https://doi.org/10.1016/j.est.2020.102067.