Flexible aircraft flight dynamics and loads model identification from flight test data in unsteady conditions

In this paper, a flight dynamics model was identified for a scaled Diana 2 glider aircraft that included the influences from its flexible structure and the delayed effects caused by unsteady aerodynamics. Flight tests were conducted to collect response measurements to various excitation manoeuvres. A method was developed to obtain the pole values for aerodynamic lag states from the flight test measurements. These poles characterise the delays in aerodynamic force and moment responses caused by unsteady aerodynamics and are necessary to be able to reconstruct the aerodynamic lag states. Ordinary least squares regression was applied to estimate the aerodynamic force and moment coefficients. The inclusion of structural modes and aerodynamic lag states led to improvements in the fitting accuracy, when compared to a rigid aircraft model with the largest improvement of 32% achieved for aircraft roll moment coefficient.

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Publication Details

Published
2025-09-03
DOI
https://doi.org/10.82439/ceas-ifasd-2024-198
Primary Topic
Market Dynamics and Volatility
Type
article
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article

Flexible aircraft flight dynamics and loads model identification from flight test data in unsteady conditions

Market Dynamics and Volatility
article

Flexible aircraft flight dynamics and loads model identification from flight test data in unsteady conditions

article en

Abstract

In this paper, a flight dynamics model was identified for a scaled Diana 2 glider aircraft that included the influences from its flexible structure and the delayed effects caused by unsteady aerodynamics. Flight tests were conducted to collect response measurements to various excitation manoeuvres. A method was developed to obtain the pole values for aerodynamic lag states from the flight test measurements. These poles characterise the delays in aerodynamic force and moment responses caused by unsteady aerodynamics and are necessary to be able to reconstruct the aerodynamic lag states. Ordinary least squares regression was applied to estimate the aerodynamic force and moment coefficients. The inclusion of structural modes and aerodynamic lag states led to improvements in the fitting accuracy, when compared to a rigid aircraft model with the largest improvement of 32% achieved for aircraft roll moment coefficient.

Delft University of Technology (NL)
Affordable and clean energy
Openalex Percentile: Top 34%
Market Dynamics and Volatility
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