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Open AccessArticle

Modeling of Stochastic Wind Based on Operational Flight Data Using Karhunen–Loève Expansion Method

1
Institute of Flight System Dynamics, Technical University of Munich, 85748 Garching, Germany
2
Applied Mathematical Statistics, Technical University of Munich, 85748 Garching, Germany
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(16), 4634; https://doi.org/10.3390/s20164634
Received: 15 July 2020 / Revised: 12 August 2020 / Accepted: 15 August 2020 / Published: 18 August 2020
(This article belongs to the Special Issue Sensor Data Fusion and Analysis for Automation Systems)
Wind has a significant influence on the operational flight safety. To quantify the influence of the wind characteristics, a wind series generator is required in simulations. This paper presents a method to model the stochastic wind based on operational flight data using the Karhunen–Loève expansion. The proposed wind model allows us to generate new realizations of wind series, which follow the original statistical characteristics. To improve the accuracy of this wind model, a vine copula is used in this paper to capture the high dimensional dependence among the random variables in the expansions. Besides, the proposed stochastic model based on the Karhunen–Loève expansion is compared with the well-known von Karman turbulence model based on the spectral representation in this paper. Modeling results of turbulence data validate that the Karhunen–Loève expansion and the spectral representation coincide in the stationary process. Furthermore, construction results of the non-stationary wind process from operational flights show that the generated wind series have a good match in the statistical characteristics with the raw data. The proposed stochastic wind model allows us to integrate the new wind series into the Monte Carlo Simulation for quantitative assessments. View Full-Text
Keywords: wind model; operational flight data; stochastic process; Karhunen–Loève Expansion; vine copula; spectral representation wind model; operational flight data; stochastic process; Karhunen–Loève Expansion; vine copula; spectral representation
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MDPI and ACS Style

Wang, X.; Beller, L.; Czado, C.; Holzapfel, F. Modeling of Stochastic Wind Based on Operational Flight Data Using Karhunen–Loève Expansion Method. Sensors 2020, 20, 4634.

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