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Generalized Reduced Order Modeling of Aeroservoelastic Systems

  • Author(s): Gariffo, James Michael
  • Advisor(s): Bendiksen, Oddvar O.
  • et al.
Abstract

Transonic aeroelastic and aeroservoelastic (ASE) modeling presents a significant technical and computational challenge. Flow fields with a mixture of subsonic and supersonic flow, as well as moving shock waves, can only be captured through high-fidelity CFD analysis. With modern computing power, it is realtively straightforward to determine the flutter boundary for a single structural configuration at a single flight condition, but problems of larger scope remain quite costly. Some such problems include characterizing a vehicle's flutter boundary over its full flight envelope, optimizing its structural weight subject to aeroelastic constraints, and designing control laws for flutter suppression. For all of these applications, reduced-order models (ROMs) offer substantial computational savings.

ROM techniques in general have existed for decades, and the methodology presented in this dissertation builds on successful previous techniques to create a powerful new scheme for modeling aeroelastic systems, and predicting and interpolating their transonic flutter boundaries. In this method, linear ASE state-space models are constructed from modal structural and actuator models coupled to state-space models of the linearized aerodynamic forces through feedback loops. Flutter predictions can be made from these models through simple eigenvalue analysis of their state-transition matrices for an appropriate set of dynamic pressures. Moreover, this analysis returns the frequency and damping trend of every aeroelastic branch. In contrast, determining the critical dynamic pressure by direct time-marching CFD requires a separate run for every dynamic pressure being analyzed simply to obtain the trend for the critical branch. The present ROM methodology also includes a new model interpolation technique that greatly enhances the benefits of these ROMs. This enables predictions of the dynamic behavior of the system for flight conditions where CFD analysis has not been explicitly performed, thus making it possible to characterize the overall flutter boundary with far fewer CFD runs.

A major challenge of this research is that transonic flutter boundaries can involve multiple unstable modes of different types. Multiple ROM-based studies on the

ONERA M6 wing are shown indicating that in addition to classic bending-torsion (BT) flutter modes. which become unstable above a threshold dynamic pressure after two natural modes become aerodynamically coupled, some natural modes are able to extract energy from the air and become unstable by themselves. These single-mode instabilities tend to be weaker than the BT instabilities, but have near-zero flutter boundaries (exactly zero in the absence of structural damping). Examples of hump modes, which behave like natural mode instabilities before stabilizing, are also shown, as are cases where multiple instabilities coexist

at a single flight condition.

The result of all these instabilities is a highly sensitive flutter boundary, where small changes in Mach number, structural stiffness, and structural damping can

substantially alter not only the stability of individual aeroelastic branches, but also which branch is critical. Several studies are shown presenting how the flutter

boundary varies with respect to all three of these parameters, as well as the number of structural modes used to construct the ROMs.

Finally, an investigation of the effectiveness and limitations of the interpolation scheme is presented. It is found that in regions where the flutter boundary is relatively smooth, the interpolation method produces ROMs that predict the flutter characteristics of the corresponding directly computed models to a high degree of accuracy, even for relatively coarsely spaced data. On the other hand, in the transonic dip region, the interpolated ROMs show significant errors at points where the boundary changes rapidly; however, they still give a good qualitative estimate of where the largest jumps occur.

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