Please use this identifier to cite or link to this item: http://repository.elizadeuniversity.edu.ng/jspui/handle/20.500.12398/991
Title: On the assessment of specific heat capacity of nanofluids for solar energy applications: Application of Gaussian process regression (GPR) approach
Authors: Jamei, Mehdi
Ahmadianfar, Iman
Olumegbon, Ismail A.
Karbasi, Masoud
Asadi, Amin
Keywords: Specific heat capacity
Solar energy
Nanofluids
Volume fraction
Gaussian process regression
Issue Date: 29-Oct-2020
Publisher: Journal of Energy Storage
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.
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
URI: http://repository.elizadeuniversity.edu.ng/jspui/handle/20.500.12398/991
Appears in Collections:Research Articles

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