Pressure Drop and Heat Transfer in Flow Over an Array of Blocks of Varying Heights: A Statistical and AI Analysis on the Effect of Block Height Variation
Abstract
The presence of a stiff obstruction in the path of fluid causes the creation of a boundary layer over and around the obstruction. The flow over an idealized, two-dimensional series of blocks is numerically investigated to determine how statistical blocks height variation, such as standard deviation, mean, and skewness, influence pressure drop and heat flux. These data sets serve as a foundation for developing models for estimating the heat transfer coefficient of each block using machine learning (ML) methods. The results show that the pressure drop increased by 60% when the standard deviation of heights of blocks increased from 0.1 to 0.4 due to promoting turbulent mixing over the blocks, hence boosting pressure drop and heat flux. Furthermore, the ML model has great potential for predicting the Convective heat transfer coefficient (CHTC) of an individual block given the heights of a few nearby obstacles.
Computational fluid dynamics (CFD), Machine learning, Convective heat transfer coefficient (CHTC), Varying height