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Related Experiment Video

Updated: Dec 11, 2025

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing
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Modeling of Stochastic Wind Based on Operational Flight Data Using Karhunen-Loève Expansion Method.

Xiaolong Wang1, Lukas Beller1, Claudia Czado2

  • 1Institute of Flight System Dynamics, Technical University of Munich, 85748 Garching, Germany.

Sensors (Basel, Switzerland)
|August 23, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel stochastic wind model using Karhunen-Loève expansion and vine copulas for improved flight safety simulations. The model accurately generates realistic wind series from operational flight data for quantitative assessments.

Keywords:
Karhunen–Loève Expansionoperational flight dataspectral representationstochastic processvine copulawind model

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Area of Science:

  • Aerospace Engineering
  • Atmospheric Science
  • Computational Statistics

Background:

  • Operational flight safety is significantly impacted by wind conditions.
  • Accurate wind modeling is crucial for flight simulations and risk assessment.
  • Existing models may not fully capture complex, non-stationary wind characteristics.

Purpose of the Study:

  • To develop a stochastic wind model for generating realistic wind series based on operational flight data.
  • To enhance the accuracy of wind modeling using advanced statistical techniques.
  • To enable quantitative assessments of wind's influence on flight safety through simulation.

Main Methods:

  • Utilized Karhunen-Loève expansion for stochastic wind modeling.
  • Employed vine copulas to capture high-dimensional dependencies in wind data.
  • Compared the proposed model with the established von Karman turbulence model.
  • Validated results using operational flight turbulence data.

Main Results:

  • The Karhunen-Loève expansion and spectral representation methods show agreement for stationary wind processes.
  • The proposed model successfully generates non-stationary wind series that match the statistical properties of operational data.
  • Vine copulas effectively model complex dependencies among random variables.

Conclusions:

  • The developed stochastic wind model provides a robust method for generating realistic wind series.
  • This model enhances the accuracy of simulations for assessing wind's impact on flight safety.
  • The approach facilitates the integration of generated wind data into Monte Carlo simulations for quantitative analysis.