A reduced order system ID approach to the modelling of nonlinear structural behavior in aeroelasticity

Attar, Peter J.; Dowell, Earl H.

doi:10.1016/j.jfluidstructs.2005.08.012

Cited by 21 publications

(8 citation statements)

References 33 publications

(29 reference statements)

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“…Several model reduction techniques have been proposed in the literature for aeroelastic systems, including Volterra series (Raveh, 2001), proper orthogonal decomposition (POD) (Feng and Soulaïmani, 2007), balanced truncation and co-prime factorisation (Hesse and Palacios, 2014). POD has also been widely used in the postprocessing of results for unsteady aerodynamics (Rowley, 2005), structural dynamics (Kerschen et al, 2005) and, in a more limited way, fluid-structure interaction problems (Attar and Dowell, 2005;Lieu et al, 2006;Liberge and Hamdouni, 2010). Some efforts have already been done in the use of POD to analyse the flow field around a membrane wing .…”

Section: Introductionmentioning

confidence: 99%

“…Some efforts have already been done in the use of POD to analyse the flow field around a membrane wing . Linearity is often assumed (Khalil et al, 2007) in this case but some works have proposed parameter dependent functions to take into account the variation of the system properties with its states (Attar and Dowell, 2005). This work will investigate the potential gain in performance of dynamically actuated membrane wings in low Reynolds number flows and the challenges associated to full-system simulation.…”

Section: Introductionmentioning

confidence: 99%

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Electro-aeromechanical modelling of actuated membrane wings

Buoso

Palacios

2015

Journal of Fluids and Structures

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a b s t r a c tThis paper presents a numerical investigation on the aeromechanical performance of dynamically actuated membrane wings made of dielectric elastomers. They combine the advantages of membrane shape adaptability, which produces increased lift and delayed stall, with the benefits of simple, lightweight but high-authority control mechanism offered by integral actuation. High-fidelity numerical models have been developed to predict their performance and include a fluid solver based on the direct numerical integration of the unsteady Navier-Stokes equations, an electromechanical constitutive material model and a non-linear membrane structural model. Numerical results show that harmonic actuation can either increase or reduce the overall aerodynamic efficiency of the wing, measured as the mean lift-to-drag ratio, depending on the ratio between the actuation frequency and the natural frequency of the membrane. In addition, the definition of a reduced-order model based on POD modes of the complete high-fidelity system provides an insight of the main characteristics of the dynamics of the coupled system.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Electro-aeromechanical modelling of actuated membrane wings

Buoso

Palacios

2015

Journal of Fluids and Structures

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“…In the real world, many systems exhibit nonlinearities that cannot be ignored such as in nonlinear control systems, equalization of nonlinear communication channels, poly-spectrum modeling and many others (Attar and Dowell, 2005;Tan and Cloe, 2000;Ozertem and Erolagnus, 2009;Kalouptsidis and Konkoulas, 2005;Tsoulkas et al, 2001). This is why the popularity of nonlinear signal processing has increased over the last decade.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear System Identification with a Real–Coded Genetic Algorithm (RCGA)

Cherif

Fnaiech

2015

International Journal of Applied Mathematics and Computer Science

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This paper is devoted to the blind identification problem of a special class of nonlinear systems, namely, Volterra models, using a real-coded genetic algorithm (RCGA). The model input is assumed to be a stationary Gaussian sequence or an independent identically distributed (i.i.d.) process. The order of the Volterra series is assumed to be known. The fitness function is defined as the difference between the calculated cumulant values and analytical equations in which the kernels and the input variances are considered. Simulation results and a comparative study for the proposed method and some existing techniques are given. They clearly show that the RCGA identification method performs better in terms of precision, time of convergence and simplicity of programming.

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“…For example, Thomas, et al characterized the shape of the transonic dip with a nonlinear ROM approach, 15 and several authors have used nonlinear ROMs to predict LCO. [16][17][18][19] Methods for interpolating ROMs over flight conditions have also been developed. 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.…”

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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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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A reduced order system ID approach to the modelling of nonlinear structural behavior in aeroelasticity

Cited by 21 publications

References 33 publications

Electro-aeromechanical modelling of actuated membrane wings

Electro-aeromechanical modelling of actuated membrane wings

Nonlinear System Identification with a Real–Coded Genetic Algorithm (RCGA)

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

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