Reference : Mixture model in high-order statistics for peak factor estimation on low-rise building
Scientific congresses and symposiums : Paper published in a journal
Engineering, computing & technology : Civil engineering
http://hdl.handle.net/2268/220645
Mixture model in high-order statistics for peak factor estimation on low-rise building
English
Rigo, François mailto [Université de Liège - ULiège > Département d'aérospatiale et mécanique > Interactions Fluide-Structure - Aérodynamique expérimentale >]
Andrianne, Thomas mailto [Université de Liège - ULiège > Département d'aérospatiale et mécanique > Interactions Fluide-Structure - Aérodynamique expérimentale >]
Denoël, Vincent mailto [Université de Liège - ULiège > Département ArGEnCo > Analyse sous actions aléatoires en génie civil >]
10-Dec-2018
Proceedings of the XV Conference of the Italian Association for Wind Engineering
Springer
Yes
International
Berlin
In Vento, ANIV - XV Conference of the Italian Association for Wind Engineering
from 09-09-2018 to 12-09-2018
Italian Association for Wind Engineering
Naples
Italy
[en] Unsteady ; Pressure ; Peak factor ; Wind Engineering ; Statistics ; Gaussian ; Non-Gaussian ; Correlation ; Probability ; Decomposition
[en] To design reliable structures, extreme pressures and peak factors are required. In many applications of Wind Engineering, their statistical analysis has to be performed taking into account the non-Gaussianity of the wind pressures. With the increasing precision and sampling frequency of pressure sensors, large short and local peak events are more usually captured. Their relevance is naturally questioned in the context of a structural design.
Furthermore, the increasing computational power allows for accumulation and analysis of larger data sets revealing the detailed nature of wind flows around bluff bodies. In particular, in the shear layers and where local vortices form, it is commonly admitted that the Probability Density Function (PDF) of measured pressures might exhibit two or more significant components. These mixed flows can be modelled with mixture models [Cook (2016)]. Whenever several processes coexist, and when one of them is leading in the tail of the statistical distribution, as will be seen next in the context of corner vortices over a flat roof, it is natural to construct the extreme value model with this leading process and not with the mixed observed pressures. It is therefore important to separate the different processes that can be observed in the pressure histories. Once this is done, specific analytical formulations of non-Gaussian peak factors can be used to evaluate the statistics of extreme values [Kareem and Zhao (1994), Chen (2009)].
The separation of mixed processes is usually done by means of the PDF of the signals [Cook (2016)]. This information is of course essential to perform an accurate decomposition but it might be facilitated by considering higher rank information like auto-correlations and higher correlations like the triple or quadruple correlation. Indeed, the two phenomena that need to be separated and identified might be characterized by significantly different timescales, 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 edge.
The full paper will finally show that an accurate decomposition of the recorded pressures into their underlying modes provides a more meaningful evaluation of the extreme pressures.
Wind Tunnel Laboratory - Structural & Stochastic Dynamics Lab (Faculty of Applied Sciences, University of Liege)
Researchers ; Professionals
http://hdl.handle.net/2268/220645

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