TY - JOUR
T1 - Study of capabilities of the ANN and RSM models to predict the thermal conductivity of nanofluids containing SiO2 nanoparticles
AU - Ibrahim, Muhammad
AU - Algehyne, Ebrahem A.
AU - Saeed, Tareq
AU - Berrouk, Abdallah S.
AU - Chu, Yu Ming
N1 - Funding Information:
This work was supported by the University of Science and Technology Beijing. Muhammad Ibrahim acknowledges the Office of China Postdoctoral Council (OCPC) for the postdoctoral international exchange program. The research was supported by the National Natural Science Foundation of China (Grant Nos. 11971142, 61673169). This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia, under grant number (KEP-17-130-41). The authors, therefore, acknowledge with thanks DSR for technical and financial support.
Publisher Copyright:
© 2021, Akadémiai Kiadó, Budapest, Hungary.
PY - 2021/8
Y1 - 2021/8
N2 - In this paper, thermal conductivity prediction of nanofluids is discussed by the RSM and ANN models. The nanofluids contain SiO2 nanoparticles, and their thermal conductivity is measured in the temperature range of 30–60 °C. The effect of SiO2 nanoparticles on thermal conductivity depends on the type of base fluid. For the ethylene glycol (EG) base fluid, SiO2 nanoparticles improve the thermal conductivity by 12%. For glycerol base fluid, thermal conductivity is increased by 6%. The thermal conductivity of both nanofluids depends on temperature and volume fraction so that as the volume fraction and temperature increase, their positive effect on thermal conductivity is enhanced. ANN and RSM models are used to estimate the thermal conductivity ratio of nanofluid to base fluid, i.e., TCR = knfkbf. Both techniques are well able to predict the amount of TCR for both nanofluids. A cubic function with R2= 0.997 and 0.994 is proposed for SiO2-EG and SiO2-G nanofluids, respectively. It is found by trial and error that the neural network with 8 neurons is suitable for both nanofluids. For Ann statistical calculations demonstrate that the R2 for SiO2-EG nanofluid is 0.995 and for SiO2-G nanofluid is 0.994. The error of both methods is less than 0.3%, which indicates that both methods can well estimate TCR value.
AB - In this paper, thermal conductivity prediction of nanofluids is discussed by the RSM and ANN models. The nanofluids contain SiO2 nanoparticles, and their thermal conductivity is measured in the temperature range of 30–60 °C. The effect of SiO2 nanoparticles on thermal conductivity depends on the type of base fluid. For the ethylene glycol (EG) base fluid, SiO2 nanoparticles improve the thermal conductivity by 12%. For glycerol base fluid, thermal conductivity is increased by 6%. The thermal conductivity of both nanofluids depends on temperature and volume fraction so that as the volume fraction and temperature increase, their positive effect on thermal conductivity is enhanced. ANN and RSM models are used to estimate the thermal conductivity ratio of nanofluid to base fluid, i.e., TCR = knfkbf. Both techniques are well able to predict the amount of TCR for both nanofluids. A cubic function with R2= 0.997 and 0.994 is proposed for SiO2-EG and SiO2-G nanofluids, respectively. It is found by trial and error that the neural network with 8 neurons is suitable for both nanofluids. For Ann statistical calculations demonstrate that the R2 for SiO2-EG nanofluid is 0.995 and for SiO2-G nanofluid is 0.994. The error of both methods is less than 0.3%, which indicates that both methods can well estimate TCR value.
KW - ANN
KW - RSM
KW - SiO-EG
KW - SiO-G
KW - Thermal conductivity
UR - http://www.scopus.com/inward/record.url?scp=85102711067&partnerID=8YFLogxK
U2 - 10.1007/s10973-021-10674-w
DO - 10.1007/s10973-021-10674-w
M3 - Article
AN - SCOPUS:85102711067
SN - 1388-6150
VL - 145
SP - 1993
EP - 2003
JO - Journal of Thermal Analysis and Calorimetry
JF - Journal of Thermal Analysis and Calorimetry
IS - 4
ER -