Abstract

How to estimate the stochastic aerodynamic parametric uncertainty on aeroelastic stability is studied in this current work. The aerodynamic uncertainty is more complicated than the structural one, and it takes more significant effect on the flutter boundary. First, the nominal unsteady aerodynamic influence coefficients were calculated with the doublet lattice method. Based on this nominal model, the stochastic uncertainty model for unsteady aerodynamic pressure coefficients was constructed with physical meaning. Afterwards, the methodology for flutter uncertainty quantification due to aerodynamic perturbation was developed, based on the nonintrusive polynomial chaos expansion theory. In order to enhance the computational efficiency, the integration algorithm, namely, Smolyak sparse grids, was employed to calculate the coefficients of the stochastic polynomial basis. Finally, the flutter uncertainty analysis methodology was applied to an aircraft's wing model. The influence of uncertainty with uniform distribution for aerodynamic pressure coefficients on flutter boundary was quantified. The numerical results indicate that, the influence of unsteady aerodynamic pressure due to the motion of coupling modes takes significant effect on flutter boundary. It is validated that the flutter uncertainty analysis based on Smolyak sparse grids integration is efficient and accurate for quantifying input uncertainty with high dimensions.

Highlights

  • Flutter is an aeroelastic instability phenomenon, which involves the interaction of elastic structures, aerodynamic force, and inertial force

  • The results indicated that the Polynomial Chaos Expansion (PCE) based on full tensor-production is more advantageous than the one based on Monte Carlo Simulation (MCS) sampling

  • The stochastic uncertainty model for unsteady aerodynamic pressure coefficients is constructed in this paper

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Summary

Introduction

Flutter is an aeroelastic instability phenomenon, which involves the interaction of elastic structures, aerodynamic force, and inertial force. The Monte Carlo Simulation (MCS) [4, 5], the Polynomial Chaos Expansion (PCE) [6], and the Stochastic Collocation [7] methods are the well-known theories in probabilistic uncertainty quantification. Whether they are applicable in flutter uncertainty quantification is not clear. Discrete Dynamics in Nature and Society due to its high efficiency In this method, the uncertainties of inputs and outputs of aeroelastic system are projected into a stochastic space. A numerical example of aircraft’s wing model is applied to validate the flutter uncertainty quantification framework

Nominal Flutter Solution: pk Method
Stochastic Uncertainty
Numerical Example
Findings
Conclusions
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