Near-wall modeling of forests for atmosphere boundary layers using lattice Boltzmann method on GPU

Shao, Xinyuan; Santasmasas, Marta Camps; Xue, Xiao; Niu, Jiqiang; Davidson, Lars; Revell, Alistair J.; Yao, Hua-Dong

Abstract

In this paper, the simulation and modeling of the turbulent atmospheric boundary layers (ABLs) in the presence of forests are studied using a lattice Boltzmann method with large eddy simulation, which was implemented in the open-source program GASCANS with the use of Graphic Processing Units (GPU). A method of modeling forests in the form of body forces injected near the wall is revisited, while the effects of leaf area density (LAD) on the model accuracy is further addressed. Since a uniform cell size is applied throughout the computational domain, the wall-normal height of the near-wall cells is very large, theoretically requiring a wall function to model the boundary layer. However, the wall function is disregarded here when the forest is modeled. This approximation is validated based on the comparison with previous experimental and numerical data. It concludes that for the ABL conditions specified in this study as well as a large body of literature, the forest forces overwhelm the wall friction so that the modeling of the latter effect is trivial. Constant and varying LAD profiles across the forest zone are defined with the same total leaf area despite the varying one being studied previously. It is found that the two LAD profiles provide consistent predictions. The present forest modeling can therefore be simplified with the use of the constant LAD without degrading the model accuracy remarkably.

Citation

Shao, X., Santasmasas, M. C., Xue, X., Niu, J., Davidson, L., Revell, A. J., & Yao, H. (2022). Near-wall modeling of forests for atmosphere boundary layers using lattice Boltzmann method on GPU. Engineering Applications of Computational Fluid Mechanics, 16(1), 2143-2156. https://doi.org/10.1080/19942060.2022.2132420

• Journal Article Type: Article
• Acceptance Date: Oct 16, 2022
• Online Publication Date: Oct 27, 2022
• Publication Date: Dec 31, 2022
• Deposit Date: Mar 21, 2024
• Publicly Available Date: Mar 22, 2024
• Journal: Engineering Applications of Computational Fluid Mechanics
• Print ISSN: 1994-2060
• Electronic ISSN: 1997-003X
• Publisher: Taylor and Francis Group
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Issue: 1
• Pages: 2143-2156
• DOI: https://doi.org/10.1080/19942060.2022.2132420
• Keywords: Modeling and Simulation; General Computer Science