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An urban large-eddy-simulation-based dispersion model for marginal grid resolutions: CAIRDIO v1.0

机译:基于城市大涡旋仿真的边际网格分辨率分散模型:Cairdio V1.0

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The ability to achieve high spatial resolutions is an important feature for numerical models to accurately represent the large spatial variability of urban air pollution. On the one hand, the well-established mesoscale chemistry transport models have their obvious shortcomings due to the extensive use of physical parameterizations. On the other hand, obstacle-resolving computational fluid dynamics (CFD) models, although accurate, are still often too computationally intensive to be applied regularly for entire cities. The major reason for the inflated computational costs is the required horizontal resolution to meaningfully apply obstacle discretization, which is mostly based on boundary-fitted grids, e.g., the marker-and-cell method. In this paper, we present the new City-scale AIR dispersion model with DIffuse Obstacles (CAIRDIO v1.0), in which the diffuse interface method, simplified for non-moving obstacles, is incorporated into the governing equations for incompressible large-eddy simulations. While the diffuse interface method is widely used in two-phase modeling, this method has not been used in urban boundary-layer modeling so far. It allows us to consistently represent buildings over a wide range of spatial resolutions, including grid spacings equal to or larger than typical building sizes. This way, the gray zone between obstacle-resolving microscale simulations and mesoscale simulations can be addressed. Orographic effects can be included by using terrain-following coordinates. The dynamic core is compared against a standard quality-assured wind-tunnel dataset for dispersion-model evaluation. It is shown that the model successfully reproduces dispersion patterns within a complex city morphology across a wide range of spatial resolutions tested. As a result of the diffuse obstacle approach, the accuracy test is also passed at a horizontal grid spacing of 40 ? m . Although individual flow features within individual street canyons are not resolved at the coarse-grid spacing, the building effect on the dispersion of the air pollution plume is preserved at a larger scale. Therefore, a very promising application of the CAIRDIO model lies in the realization of computationally feasible yet accurate air-quality simulations for entire cities.
机译:实现高空间分辨率的能力是数值模型准确代表城市空气污染的大空间变异性的重要特征。一方面,由于物理参数化的广泛使用,良好建立的Mesoscale化学传输模型具有显而易见的缺点。另一方面,解决障碍物的计算流体动力学(CFD)模型虽然准确,但仍然通常过于计算到整个城市定期应用。膨胀计算成本的主要原因是所需的水平分辨率,以有意义地应用障碍物离散化,这主要基于边界拟合网格,例如标记和细胞方法。在本文中,我们介绍了具有漫反射障碍物(Cairdio V1.0)的新城市级空气分散模型,其中漫射界面方法简化为不动障碍物,纳入了不可压缩大涡模拟的控制方程。虽然漫反射界面方法广泛用于两相模型,但该方法尚未在城市边界层建模中使用。它允许我们一致地代表广泛的空间分辨率上的建筑物,包括等于或大于典型建筑大小的网格间距。这样,可以解决障碍物解析微观模拟和Messcore模拟之间的灰色区域。可以通过使用地形跟随坐标来包括地形效果。将动态核心与标准质量保证的风隧道数据集进行比较,用于分散模型评估。结果表明,该模型在经过各种空间分辨率的复杂城市形态中成功地再现了分散模式。由于漫反射障碍方法,精度测试也通过40的水平网格间距。 m。尽管在粗栅间距不解决各个街道峡谷内的个体流动特征,但是对空气污染羽流分散的建筑效果以更大的规模保存。因此,Cairdio模型的非常有希望的应用在于实现整个城市的计算可行但准确的空气质量模拟。

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