Prediction of heat transfer distribution induced by the variation in vertical location of circular cylinder on Rayleigh-Bénard convection using artificial neural network

•Flow and thermal fields of Rayleigh-Bénard convection (RBC) in a rectangular channel with an internal circular cylinder.•The Rayleigh number and the vertical distance significantly influence the flow and thermal characteristics within the channel.•An artificial neural network (ANN) model is used to...

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Bibliographic Details
Published in:International journal of mechanical sciences Vol. 209; p. 106701
Main Authors: Seo, Young Min, Pandey, Sudhanshu, Lee, Hyeon Uk, Choi, Changyoung, Park, Yong Gap, Ha, Man Yeong
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.11.2021
Subjects:
Artificial neural network
Heat transfer performance
Irreversibility
Rayleigh-Bénard convection
Supervised learning algorithm
Vertical distance
Rayleigh-Bénard convection
Heat transfer performance
Vertical distance
Irreversibility
Artificial neural network
Supervised learning algorithm
ISSN:0020-7403, 1879-2162
Online Access:Get full text
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Summary:•Flow and thermal fields of Rayleigh-Bénard convection (RBC) in a rectangular channel with an internal circular cylinder.•The Rayleigh number and the vertical distance significantly influence the flow and thermal characteristics within the channel.•An artificial neural network (ANN) model is used to predict the distribution of local Nusselt number.•The results show that the ANN model can precisely predict the correlation between the input and output parameters with lesser computational time and cost compared to the DNS. 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. [Display omitted]
ISSN:0020-7403
1879-2162
DOI:10.1016/j.ijmecsci.2021.106701