Generation of Aeroservoelastic Reduced Order Models Using Time Scaling

HammerandJames, Daniel C.; Gariffo, M.; Roughen, Kevin M.; Baker, Myles L.; Bendiksen, Oddvar O.

doi:10.2514/6.2010-2947

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…(8), (14), (15), and (24) and converting them to state-space form. The overall models have the following general form: …”

Section: Iid Assembly Of Aeroservoelastic State Space Modelmentioning

confidence: 99%

“…One such method, based on proper orthogonal decompositions, was developed by Amsallem,et al 20,21 and Lieu et al 22 Another method, based on Roger's RFA, was developed by the authors, in collaboration with Hammerand, Roughen, and Baker. [23][24][25][26] In this paper, the flutter boundary of an aeroelastic model of the ONERA M6 wing is investigated across a range of aerodynamic and structural parameters, using an extension of the ROM methodology from Refs. 23-26. In particular, the studies presented here examine techniques to rapidly estimate aeroelastic stability for multiple values of a given parameter such as air density or wing thickness based on linearized representations of data obtained from a small number of full-order nonlinear analyses.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Prediction of Transonic Flutter Boundaries with Linearized Aeroservoelastic Reduced-Order Models

Gariffo

Bendiksen

2013

54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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A computational aeroelastic model based on the ONERA M6 wing configuration is studied over a range of transonic flight conditions and structural stiffness parameters. A reduced-order modeling (ROM) approach is used, consisting of linear state-space models based on nonlinear CFD results. The structural component of the models is generated by converting a finite-element model to modal form, while the aerodynamic component is generated via Roger's Rational Function Approximation (RFA). It is shown that although the state-space models are linear, they are effective at predicting conditions where the full nonlinear model shows instabilities, including instabilities unique to the transonic regime such as high-altitude flutter and single-degree of freedom flutter. Nomenclature[A] State matrix of a state-space model [A 0 ] Constant term in Roger's Rational Function Approximation [A 1 ] Linear term in Roger's Rational Function Approximation [A 2 ] Quadratic term in Roger's Rational Function Approximation [A A ] Aerodynamic state matrix [a] Matrix of actuator delay constants [B l ] Curve fit term corresponding to aerodynamic lag term l in Roger's Rational Function Approximation [B 0 A ] Aerodynamic input matrix corresponding to displacement input [B 1 A ] Aerodynamic input matrix corresponding to velocity input [B 2 A ] Aerodynamic input matrix corresponding to acceleration input {B A } N l × 1 vector fully populated with values of 1.0 [C] Output matrix of a state-space model [C A ] Aerodynamic output matrix [C d ] Nodal damping matrix from structural model C d Modal damping matrix c Wing chord length [D] Feedthrough matrix of a state-space model [D 0 A ] Aerodynamic feedthrough matrix corresponding to displacement input [D 1 A ] Aerodynamic feedthrough matrix corresponding to velocity input [D 2 A ] Aerodynamic feedthrough matrix corresponding to acceleration input {d} Nodal displacement vector from structural model {F } Force vector from structural model {F } Modal force vector F (k) Generalized aerodynamic force as function of reduced frequency f (t) Generalized aerodynamic force as function of time f Aeroservoelastic modal frequency

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“…(8), (14), (15), and (24) and converting them to state-space form. The overall models have the following general form: …”

Section: Iid Assembly Of Aeroservoelastic State Space Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Prediction of Transonic Flutter Boundaries with Linearized Aeroservoelastic Reduced-Order Models

Gariffo

Bendiksen

2013

54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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An Overview of the Semi-Span Super-Sonic Transport (S4T) Wind-Tunnel Model Program

Silva

Perry²,

Florance³

et al. 2012

53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference

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A summary of computational and experimental aeroelastic (AE) and aeroservoelastic (ASE) results for the Semi-Span Super-Sonic Transport (S 4 T) wind-tunnel model is presented. A broad range of analyses and multiple AE and ASE wind-tunnel tests of the S 4 T wind-tunnel model have been performed in support of the ASE element in the Supersonics Program, part of the NASA Fundamental Aeronautics Program. This paper is intended to be an overview of multiple papers that comprise a special S 4 T technical session. Along those lines, a brief description of the design and hardware of the S 4 T wind-tunnel model will be presented. Computational results presented include linear and nonlinear aeroelastic analyses, and rapid aeroelastic analyses using CFD-based reduced-order models (ROMs). A brief survey of some of the experimental results from two open-loop and two closed-loop wind-tunnel tests performed at the NASA Langley Transonic Dynamics Tunnel (TDT) will be presented as well.

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Generation of Aeroservoelastic Reduced Order Models Using Time Scaling

Cited by 2 publications

References 7 publications

Efficient Prediction of Transonic Flutter Boundaries with Linearized Aeroservoelastic Reduced-Order Models

Efficient Prediction of Transonic Flutter Boundaries with Linearized Aeroservoelastic Reduced-Order Models

An Overview of the Semi-Span Super-Sonic Transport (S4T) Wind-Tunnel Model Program

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