论文标题

在GPU上使用晶格Boltzmann方法,在存在森林的情况下,近壁近似以加快大气边界层的模拟

Near-wall approximations to speed up simulations for atmosphere boundary layers in the presence of forests using lattice Boltzmann method on GPU

论文作者

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

论文摘要

森林在影响大气边界层的风资源和风力涡轮机疲劳寿命方面起着重要作用。由于湍流,模拟森林效应的困难是,应使用湍流解决的CFD方法准确地解决流动统计和波动内容,这需要大量的计算时间和资源。在本文中,我们演示了一个快速但准确的仿真平台,该平台使用晶格玻尔兹曼方法在图形处理单元(GPU)上使用大型涡流模拟。模拟工具是曼彻斯特大学开发的开源计划Gascans。基于规范壁构成的湍流流对仿真平台进行验证。森林是以注射壁附近注入的身体力的形式建模的。由于在整个计算域中都应用了均匀的单元格大小,因此墙壁上的平均一层单元高度达到$ \langleΔy^+\ rangle = 165 $。模拟结果与以前的实验和从有限体积方法获得的数值数据非常吻合。我们证明,在不使用壁功能的情况下,由于森林力压倒了壁摩擦,因此可以良好的结果。只要森林区域用几个细胞解决,就可以证明这一点。除了GPU加速外,近似值还显着有益于计算效率。

Forests play an important role in influencing the wind resource in atmospheric boundary layers and the fatigue life of wind turbines. Due to turbulence, a difficulty in the simulation of the forest effects is that flow statistical and fluctuating content should be accurately resolved using a turbulence-resolved CFD method, which requires a large amount of computing time and resources. In this paper, we demonstrate a fast but accurate simulation platform that uses a lattice Boltzmann method with large eddy simulation on Graphic Processing Units (GPU). The simulation tool is the open-source program, GASCANS, developed at the University of Manchester. The simulation platform is validated based on canonical wall-bounded turbulent flows. A forest is modelled in the form of body forces injected near the wall. Since a uniform cell size is applied throughout the computational domain, the averaged first-layer cell height over the wall reaches to $\langle Δy^+\rangle = 165$. Simulation results agree well with previous experiments and numerical data obtained from finite volume methods. We demonstrate that good results are possible without the use of a wall-function, since the forest forces overwhelm wall friction. This is shown to hold as long as the forest region is resolved with several cells. In addition to the GPU speedup, the approximations also significantly benefit the computation efficiency.

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