A de-mixing approach for the management of large negative peaks in wind tunnel data

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.