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

The present study investigates the flow and thermal fields of Rayleigh-Benard convection (RBC) in a rectangular channel with an internal circular cylinder. The parameters considered are Rayleigh number (10(4) <= Ra <= 10(6) ), Prandtl number (Pr = 0.7), and irreversibility distribution ratio (phi = 1). The vertical distance (delta) in the range of -0.2 <= delta <= 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.