Reduced Order Modeling Using Proper Orthogonal Decomposition (POD) and Wavenet System Identification of a Free Shear Layer

Fagley, Casey; Seidel, Jürgen; Siegel, Stefan; McLaughlin, Thomas

doi:10.1007/978-3-642-11735-0_21

Cited by 8 publications

(5 citation statements)

References 25 publications

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“…The surrogate model is identified from transient forced motion training data. The training set used in this paper consists of three analyses with superimposed excitations of scaled structural mode shapes u s of the first ten eigenmodes, which is shown in Equation (15).…”

Section: Training Datamentioning

confidence: 99%

“…Chen et al (14) utilise support vector machines for the prediction of limit cycle oscillations (LCO). Fagley et al (15) use POD in combination with wavelet neural networks (wavenet) to predict the non-linear dynamic behaviour of a free shear layer. Zhang et al (16) show that radial basis function artificial neural networks (RBF-ANN) are able to predict LCO behaviour of the NACA64A010 aerofoil in the time domain.…”

Section: Introductionmentioning

confidence: 99%

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Reduced-order modelling of non-linear, transient aerodynamics of the HIRENASD wing

2016

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In this paper, a surrogate model approach for non-linear aerodynamics is presented in order to reduce the computational effort of coupled aeroelastic analyses. The usability of the approach is demonstrated in static as well as transient aeroelastic analyses of the HIRENASD wingfuselage configuration. Furthermore, it is shown that the surrogate model approach is able to cover variations of flow conditions at a fixed Mach and Reynolds number.

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Section: Training Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reduced-order modelling of non-linear, transient aerodynamics of the HIRENASD wing

2016

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“…The system identification step as presented in Fagley et al [2010] is a crucial step in formulating the low dimensional model. Numeric decomposition parameters such as: forcing parameters, spatial domain, and numerical decomposition method (DPOD, SPOD, POD..etc.)…”

Section: System Identificationmentioning

confidence: 99%

Flow Control

Fagley¹

2013

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“…Hence, several nonlinear ROMs have been developed and applied successfully as nonlinear surrogates, for example: Artificial Neural Network Auto-Regressive Exogenous (ANN-ARX) [13,14], Kriging-ARX [15],Continuous Time Recurrent Neural Network [16].…”

mentioning

confidence: 99%

“…Fagley et al used POD and ANN-ARX to investigate the free shear layer transient effects [13]. Following the same idea, Lindhorst et al employed…”

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confidence: 99%

Nonlinear Aerodynamic and Aeroelastic Model Reduction Using a Discrete Empirical Interpolation Method

Yao

Marques²

2017

AIAA Journal

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A novel surrogate model is proposed in lieu of Computational Fluid Dynamics (CFD) solvers, for fast nonlinear aerodynamic and aeroelastic modeling. A nonlinear function is identified on selected interpolation points by a discrete empirical interpolation method (DEIM). The flow field is then reconstructed using a least square approximation of the flow modes extracted by proper orthogonal decomposition (POD). The aeroelastic reduce order model (ROM) is completed by introducing a nonlinear mapping function between displacements and the DEIM points. The proposed model is investigated to predict the aerodynamic forces due to forced motions using a N ACA 0012 airfoil undergoing a prescribed pitching oscillation. To investigate aeroelastic problems at transonic conditions, a pitch/plunge airfoil and a cropped delta wing aeroelastic models are built using linear structural models. The presence of shock-waves triggers the appearance of limit cycle oscillations (LCO), which the model is able to predict.For all cases tested, the new ROM shows the ability to replicate the nonlinear aerodynamic forces, structural displacements and reconstruct the complete flow field with sufficient accuracy at a fraction of the cost of full order CFD model.

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Reduced Order Modeling Using Proper Orthogonal Decomposition (POD) and Wavenet System Identification of a Free Shear Layer

Cited by 8 publications

References 25 publications

Reduced-order modelling of non-linear, transient aerodynamics of the HIRENASD wing

Reduced-order modelling of non-linear, transient aerodynamics of the HIRENASD wing

Flow Control

Nonlinear Aerodynamic and Aeroelastic Model Reduction Using a Discrete Empirical Interpolation Method

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