A Statistical Model for the Prediction of Wind-Speed Probabilities in the Atmospheric Surface Layer
A Statistical Model for the Prediction of Wind-Speed Probabilities in the Atmospheric Surface Layer
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DOI：
10.1007/s10546-016-0221-2
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发表时间：
2017-05
影响因子：
4.3
通讯作者：
G. Efthimiou;D. Hertwig;S. Andronopoulos;J. Bartzis;O. Coceal
中科院分区：
文献类型：
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作者：
G. Efthimiou;D. Hertwig;S. Andronopoulos;J. Bartzis;O. Coceal
大气近地层风场是高度三维的，具有很强的时空变化性。对于风舒适度评估和结构设计等各种应用，了解潜在的危险极端风是很重要的。统计模型的目的是为了方便的结论，发生概率的风速的基础上的知识，低阶流量统计。特别感兴趣的是在上尾区域，我们表明，近地面风速的统计行为是充分代表的贝塔分布。通过使用的Beta概率密度函数的属性结合模型估计极值的基础上，现成的湍流统计，它表明，这种新的建模方法可靠地预测遇到的风速的上限。该模型的基本参数来自三个基本不同的校准数据集的流动在ASL起源于边界层风洞测量和直接数值模拟。评估模型的基础上独立的近地面风速的现场观测显示，统计建模的水平风速和测量值之间的高度一致性。结果表明，基于知识的只有几个简单的流量统计（平均风速，风速波动和积分时间尺度），在ASL中的任意流量位置的速度大小的发生概率可以估计具有较高的置信度。
Wind fields in the atmospheric surface layer (ASL) are highly three-dimensional and characterized by strong spatial and temporal variability. For various applications such as wind-comfort assessments and structural design, an understanding of potentially hazardous wind extremes is important. Statistical models are designed to facilitate conclusions about the occurrence probability of wind speeds based on the knowledge of low-order flow statistics. Being particularly interested in the upper tail regions we show that the statistical behaviour of near-surface wind speeds is adequately represented by the Beta distribution. By using the properties of the Beta probability density function in combination with a model for estimating extreme values based on readily available turbulence statistics, it is demonstrated that this novel modelling approach reliably predicts the upper margins of encountered wind speeds. The model’s basic parameter is derived from three substantially different calibrating datasets of flow in the ASL originating from boundary-layer wind-tunnel measurements and direct numerical simulation. Evaluating the model based on independent field observations of near-surface wind speeds shows a high level of agreement between the statistically modelled horizontal wind speeds and measurements. The results show that, based on knowledge of only a few simple flow statistics (mean wind speed, wind-speed fluctuations and integral time scales), the occurrence probability of velocity magnitudes at arbitrary flow locations in the ASL can be estimated with a high degree of confidence.