Aerodynamic impulse response of a panel method

Guendel, Randal; Cesnik, Carlos E. S.

doi:10.2514/6.2001-1210

Cited by 5 publications

(7 citation statements)

References 10 publications

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“…Therefore, in an attempt to improve the accuracy of the step or impulse response, by decreasing the time step (without regard to the number of time steps), the overall predictive capability of the step or impulse response may in fact be compromised. Guendel and Cesnik (2001) and Raveh and Mavris (2001) indicate that decreasing the time step resulted in decreased predictive accuracy of the impulse response. The preceding observation may explain this counterintuitive and unexpected result.…”

Section: Appendix Amentioning

confidence: 96%

Development of reduced-order models for aeroelastic analysis and flutter prediction using the CFL3Dv6.0 code

Silva

Bartels

2004

Journal of Fluids and Structures

150

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Section: Appendix Amentioning

confidence: 96%

Development of reduced-order models for aeroelastic analysis and flutter prediction using the CFL3Dv6.0 code

Silva

Bartels

2004

Journal of Fluids and Structures

150

View full text Add to dashboard Cite

“…Many data-driven modelling techniques may be categorized as system identification, where a model for the input-output behaviour is constructed based on data. Time domain techniques are especially common, such as the aerodynamic impulse response (AIR) [52][53][54][55], where the system is perturbed with an impulsive input, and the output response may be used to predict the response to future input manoeuvres via convolution. Although these techniques are typically linearized, nonlinear kernels may also be employed in a Volterra series [53,56].…”

Section: (B) Reduced-order Aeroelastic Modelsmentioning

confidence: 99%

“…Time domain techniques are especially common, such as the aerodynamic impulse response (AIR) [52–55], where the system is perturbed with an impulsive input, and the output response may be used to predict the response to future input manoeuvres via convolution. Although these techniques are typically linearized, nonlinear kernels may also be employed in a Volterra series [53,56]. State-space realizations are becoming increasingly common, especially for control applications [45,47].…”

Section: Introductionmentioning

confidence: 99%

Data-driven nonlinear aeroelastic models of morphing wings for control

2020

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Accurate and efficient aeroelastic models are critically important for enabling the optimization and control of highly flexible aerospace structures, which are expected to become pervasive in future transportation and energy systems. Advanced materials and morphing wing technologies are resulting in next-generation aeroelastic systems that are characterized by highly coupled and nonlinear interactions between the aerodynamic and structural dynamics. In this work, we leverage emerging data-driven modelling techniques to develop highly accurate and tractable reduced-order aeroelastic models that are valid over a wide range of operating conditions and are suitable for control. In particular, we develop two extensions to the recent dynamic mode decomposition with control (DMDc) algorithm to make it suitable for flexible aeroelastic systems: (1) we introduce a formulation to handle algebraic equations, and (2) we develop an interpolation scheme to smoothly connect several linear DMDc models developed in different operating regimes. Thus, the innovation lies in accurately modelling the nonlinearities of the coupled aerostructural dynamics over multiple operating regimes, not restricting the validity of the model to a narrow region around a linearization point. We demonstrate this approach on a high-fidelity, three-dimensional numerical model of an airborne wind energy system, although the methods are generally applicable to any highly coupled aeroelastic system or dynamical system operating over multiple operating regimes. Our proposed modelling framework results in real-time prediction of nonlinear unsteady aeroelastic responses of flexible aerospace structures, and we demonstrate the enhanced model performance for model predictive control. Thus, the proposed architecture may help enable the widespread adoption of next-generation morphing wing technologies.

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“…Many data-driven modeling techniques may be categorized as system identification, where a model for the input-output behavior is constructed based on data. Time domain techniques are especially common, such as the aerodynamic impulse response (AIR) [50][51][52][53], where the system is perturbed with an impulsive input, and the output response may be used to predict the response to future input maneuvers via convolution. Although these techniques are typically linearized, nonlinear kernels may also be employed in a Volterra series [51,54].…”

Section: Reduced-order Aeroelastic Modelsmentioning

confidence: 99%

Data-driven nonlinear aeroelastic models of morphing wings for control

Fonzi,

Brunton,

Fasel

2020

Preprint

View full text Add to dashboard Cite

Accurate and efficient aeroelastic models are critically important for enabling the optimization and control of highly flexible aerospace structures, which are expected to become pervasive in future transportation and energy systems. Advanced materials and morphing wing technologies are resulting in next-generation aeroelastic systems that are characterized by highly-coupled and nonlinear interactions between the aerodynamic and structural dynamics. In this work, we leverage emerging data-driven modeling techniques to develop highly accurate and tractable reduced-order aeroelastic models that are valid over a wide range of operating conditions and are suitable for control. In particular, we develop two extensions to the recent dynamic mode decomposition with control (DMDc) algorithm to make it suitable for flexible aeroelastic systems: 1) we introduce a formulation to handle algebraic equations, and 2) we develop an interpolation scheme to smoothly connect several linear DMDc models developed in different operating regimes. Thus, the innovation lies in accurately modeling the nonlinearities of the coupled aerostructural dynamics over multiple operating regimes, not restricting the validity of the model to a narrow region around a linearization point. We demonstrate this approach on a high-fidelity, three-dimensional numerical model of an airborne wind energy (AWE) system, although the methods are generally applicable to any highly coupled aeroelastic system or dynamical system operating over multiple operating regimes. Our proposed modeling framework results in real-time prediction of nonlinear unsteady aeroelastic responses of flexible aerospace structures, and we demonstrate the enhanced model performance for model predictive control. Thus, the proposed architecture may help enable the widespread adoption of next-generation morphing wing technologies.

show abstract

Aerodynamic impulse response of a panel method

Cited by 5 publications

References 10 publications

Development of reduced-order models for aeroelastic analysis and flutter prediction using the CFL3Dv6.0 code

Development of reduced-order models for aeroelastic analysis and flutter prediction using the CFL3Dv6.0 code

Data-driven nonlinear aeroelastic models of morphing wings for control

Data-driven nonlinear aeroelastic models of morphing wings for control

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