Abstract Large negative peak pressures commonly take place near the edges of buildings due to the presence of local corner vortices and impingement of shear layers. As a result, Probability Density Functions (PDF) of the measured pressure signals exhibit one or more components which contributes to the non-Gaussianity of the pressure loading. These mixed flows can be modeled with mixture models. Whenever several processes coexist, and when one of them is leading in the tail of the statistical distribution, it is natural to construct the extreme value model with only this process leading in the tail and not with the mixed observed pressures. In this paper, we propose a method that is based on the autocorrelation of the pressure coefficient to de-mix the measured signals. This information improves the de-mixing process where classical methods would struggle. Indeed, the two phenomena to be separated and identified might be characterized by significantly different time-scales, which are not reflected in the PDF. In this paper, the large negative pressures measured on a flat roof are analyzed and decomposed into two elementary processes, namely, the flapping corner vortex and the turbulent flow detaching from the sharp upstream edges. This paper finally shows that an accurate decomposition of the recorded pressures into their underlying modes provides a more meaningful evaluation of extreme pressures.

中文翻译：

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一种用于管理风洞数据中大负峰的去混合方法

摘要 由于局部角涡的存在和剪切层的冲击，在建筑物边缘附近通常会产生较大的负峰值压力。结果，测得的压力信号的概率密度函数 (PDF) 表现出一个或多个对压力负载的非高斯性有贡献的分量。这些混合流可以用混合模型建模。当多个过程共存时，当其中一个过程在统计分布的尾部领先时，很自然地构建只有这个过程在尾部领先而不是混合观测压力的极值模型。在本文中，我们提出了一种基于压力系数自相关的方法来对测量信号进行解混。该信息改进了经典方法难以解决的去混合过程。事实上，要分离和识别的两种现象可能具有显着不同的时间尺度，这在 PDF 中没有反映出来。本文对平屋顶上测得的大负压进行了分析，并将其分解为两个基本过程，即拍动角涡和从上游锐边脱离的湍流。本文最终表明，将记录的压力准确分解为它们的潜在模式，可以对极端压力进行更有意义的评估。对平屋顶上测得的大负压进行了分析，并将其分解为两个基本过程，即拍动角涡和从上游锋利边缘脱离的湍流。本文最终表明，将记录的压力准确分解为它们的潜在模式，可以对极端压力进行更有意义的评估。对平屋顶上测得的大负压进行了分析，并将其分解为两个基本过程，即拍动角涡和从上游锋利边缘脱离的湍流。本文最终表明，将记录的压力准确分解为它们的潜在模式，可以对极端压力进行更有意义的评估。