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

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.