Specification of the design wind load based on wind tunnel experiments considering the statistical uncertainties due to limited ensemble sizes

对于基于风洞试验的设计风荷载规范，必须考虑从受限系综获得的统计不确定性。满足所选择的置信度区间的基本策略使用调节因子，该调节因子尤其依赖于集合大小。例如，在欧洲法规DIN-EN 1990：2010-12的附件设计中，通过测试电阻设计值的规格来实施相应的程序。该项目的目的是为估计气动系数设计值的最佳方法开发调整系数。虽然这些调整系数保证了不安全解的比率与集合尺寸无关地保持恒定，但是对于显著高估的设计风荷载而言，不经济解的比率随着集合尺寸的减小而增加。从根本上说，这个比率是全新的，其目的是在遵循尽可能少的运行和尽可能多的运行的基本原则下，获得关于所需风洞运行次数的决策辅助。与阻力侧相反，气动系数设计值没有统一的目标分数值，目标值取决于极值的变异系数。因此，需要根据合奏的大小和变化系数来调整系数。有两种可供选择的方法来估算设计值。第一种方法直接使用独立的极值，从单个游程或子集中选取。使用子集的基本优势在于扩大了集合大小，基本上导致了更高的统计稳定性。然而，对于子集，目标分数值增加，导致统计稳定性降低。此外，对于变得越来越短的子集，存在子集不再具有代表性的基本危险。从根本上说，新的方法是通过峰值过阈值采样获得更大的集合尺寸。尤其是基于最佳线性无偏估计(BLUE)来执行估计。第二种方法是使用莱斯方法，基于等价正态分布估计设计值。该方法的基本元素是使用CDF映射或Hermite多项式估计平移规则，以及使用时间导数的方差或估计的谱密度函数的频率加权积分来估计循环速率。最优方法是通过比较不同方法得到的估计的质量特征，特别是比较相当不经济的估计的比率来获得的。

For the specification of the design wind load based on wind tunnel experiments statistical uncertainties have to be considered which are obtained from confined ensembles. The basic strategy for meeting a chosen confidence interval uses an adjusting factor which especially depends on the ensemble size. A corresponding procedure is implemented e.g. in the Eurocode DIN-EN 1990:2010-12 in the annex Design by testing for the specification of the design value of the resistance. The project aims in developing the adjusting factors for an optimal method for the estimation of the design value of the aerodynamic coefficient. While these adjusting factors guarantee that the ratio of unsafe solutions remains constant independent of the ensemble size, the ratio of uneconomic solutions in terms of considerable overestimated design wind loads increases for decreasing ensemble size. Fundamentally new, this ratio is developed to obtain an aid to decision-making in regard to the required number of wind tunnel runs following the basic principle as few runs as possible and as many runs as necessary. Opposite to the resistance side, there is no uniform target fractile value for the design value of the aerodynamic coefficients; the target value rather depends on the variation coefficient of the extremes. Consequently, adjusting factors are required depending on the ensemble size and the variation coefficient. Two alternative approaches are available for the estimation of the design value. The first approach directly uses independent extremes, either picked from single runs or from subsets. The basic advantage of using subsets lies in an enlarged ensemble size, basically leading to an increased statistical stability. However, for subsets the target fractile value increases, leading to decreasing statistical stability. Furthermore, for subsets becoming shorter and shorter there is the basic danger that the subset is no longer representative. Fundamentally new, an enlarged ensemble size is obtained by peaks-over-threshold sampling. Estimations especially are performed based on the Best Linear Unbiased Estimator (BLUE). The second approach estimates the design values based on an equivalent normal distribution using the Rice method. The fundamental elements of this approach are the estimation of the translation rule using CDF-mapping or Hermite polynomials, and the estimation of the cycling rate either using the variance of the time derivative or the frequency-weighted integration of the estimated spectral density function. The optimal method is obtained by comparing quality features of the estimations obtained by the different methods, especially the ratio of considerable uneconomic estimations.