TY - JOUR
T1 - Prediction of heat transfer distribution induced by the variation in vertical location of circular cylinder on Rayleigh-Bénard convection using artificial neural network
AU - Seo, Young Min
AU - Pandey, Sudhanshu
AU - Lee, Hyeon Uk
AU - Choi, Changyoung
AU - Park, Yong Gap
AU - Ha, Man Yeong
N1 - Funding Information:
* This work is supported by Teaching and Research Award Program for Outstanding Young Teachers of Nanjing Normal University，China and the Postdoctoral Science Funding of China
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/11/1
Y1 - 2021/11/1
N2 - The present study investigates the flow and thermal fields of Rayleigh-Bénard convection (RBC) in a rectangular channel with an internal circular cylinder. The parameters considered are Rayleigh number (104≤Ra≤106), Prandtl number (Pr = 0.7), and irreversibility distribution ratio (φ = 1). The vertical distance (δ) in the range of -0.2 ≤ δ ≤ 0.2 is the major simulation parameter in present study. The results are analyzed based on the iso-surface of temperature, vortical structure with orthogonal enstrophy distribution, and entropy generations. Additionally, Nusselt number (Nu) and Bejan number (Be) are obtained to analyze the heat transfer characteristics and irreversibility, respectively. The Rayleigh number and the vertical distance significantly influence the flow and thermal characteristics within the channel. Besides, an artificial neural network (ANN) model is used to predict the distribution of local Nusselt number. The performance of present ANN model is evaluated by comparing the tendency and quantitative values with the direct numerical simulation (DNS) results. The results show that the ANN model used in this study can precisely predict the correlation between the input parameters and output parameter with lesser computational time and cost compared to the DNS.
AB - The present study investigates the flow and thermal fields of Rayleigh-Bénard convection (RBC) in a rectangular channel with an internal circular cylinder. The parameters considered are Rayleigh number (104≤Ra≤106), Prandtl number (Pr = 0.7), and irreversibility distribution ratio (φ = 1). The vertical distance (δ) in the range of -0.2 ≤ δ ≤ 0.2 is the major simulation parameter in present study. The results are analyzed based on the iso-surface of temperature, vortical structure with orthogonal enstrophy distribution, and entropy generations. Additionally, Nusselt number (Nu) and Bejan number (Be) are obtained to analyze the heat transfer characteristics and irreversibility, respectively. The Rayleigh number and the vertical distance significantly influence the flow and thermal characteristics within the channel. Besides, an artificial neural network (ANN) model is used to predict the distribution of local Nusselt number. The performance of present ANN model is evaluated by comparing the tendency and quantitative values with the direct numerical simulation (DNS) results. The results show that the ANN model used in this study can precisely predict the correlation between the input parameters and output parameter with lesser computational time and cost compared to the DNS.
KW - Artificial neural network
KW - Heat transfer performance
KW - Irreversibility
KW - Rayleigh-Bénard convection
KW - Supervised learning algorithm
KW - Vertical distance
UR - http://www.scopus.com/inward/record.url?scp=85111888246&partnerID=8YFLogxK
U2 - 10.1016/j.ijmecsci.2021.106701
DO - 10.1016/j.ijmecsci.2021.106701
M3 - Article
AN - SCOPUS:85111888246
SN - 0020-7403
VL - 209
JO - International Journal of Mechanical Sciences
JF - International Journal of Mechanical Sciences
M1 - 106701
ER -