A generic gust definition and detection method based on wavelet-analysis

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Knoop, H.; Ament, F.; Maronga, B.: A generic gust definition and detection method based on wavelet-analysis. In: Advances in Science and Research 16 (2019), S. 143-149. DOI: https://doi.org/10.5194/asr-16-143-2019

Version im Repositorium

Zum Zitieren der Version im Repositorium verwenden Sie bitte diesen DOI: https://doi.org/10.15488/8820

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Kleine Vorschau
Zusammenfassung: 
Wind gusts can have destructive effects on many structures and objects deemed valuable to humans. The aviation industry, for example, views gusts as a major hazard. Their destructive effect is proportional to the momentum that a specific gust imposes onto an object. The actual definition of a gust has a strong influence on how its impact can be quantified. Existing gust definitions, however, are largely based on fixed parameters describing shape requirements and thresholds and are often developed only for specific use cases. These gust definitions do not provide a direct link to the physical impact a particular gust has on a structure or object. The overall goal of this study is to provide such a direct link. The application of a wavelet-analysis to a turbulence-resolving wind velocity signal allows for the localization of signal amplitudes in the period as well as in the time domain. In this paper, we use wavelet-analysis in order to develop a straight-forward method of deriving information about gusts from a wind velocity signal. In order to define what a particular gust might be, we suggest the specification of a characteristic period and amplitude in the time-domain. We define a generic gust as a section of a wind velocity signal, where the wavelet-analysis detects that characteristic amplitude to be matched or exceeded within that characteristic period. The characteristic amplitudes and periods are generic and span a two-dimensional space of generic gust definitions. The method can be applied to turbulence resolving simulation data as well as high-resolution wind velocity measurement data. It can detect gusts of any shape, it is unbiased regarding any specific use case and invariant to changes of the mean wind. We provide a detailed description of the method, its capabilities and demonstrate its application to high resolution wind velocity measurement data.
Lizenzbestimmungen: CC BY 4.0 Unported
Publikationstyp: Article
Publikationsstatus: publishedVersion
Erstveröffentlichung: 2019
Die Publikation erscheint in Sammlung(en):Fakultät für Mathematik und Physik

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