Adaptation of Aeroelastic Reduced-Order Models and Application to an F-16 Configuration

By Thuan Lieu, Charbel Farhat. 2007

177 citations recorded by the source corpus.

Subfields: Computational Mechanics, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d3cd9f66dcd67eb13b536f94c721873e31e4d162

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In Computational Mechanics

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Citations in the corpus

Stabilization of projection‐based reduced‐order models (2012, methodology): “For a given free-stream pressure p1, the LTI system associated with the aeroelastic ROM obtained by coupling the fluid and structure ROMs (see References [1, 2, 28, 29] for details on the coupling formulation) can be written as dxr dt .t/D Ar.p1/xr.t/ D 2 4 p p1Ar ,11 Ar ,12 p p1Ar ,13 p1Ar ,21 0kS ”
A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems (2015, unknown): “For example, parametric reduced models capturing the coupled ﬂuid dynamic and structural dynamic behavior of an aircraft conﬁguration provide rapid evaluation of aeroelastic performance over a range of aircraft operating conditions [8, 158, 159].”
Reduced-Order Nonlinear Unsteady Aerodynamic Modeling Using a Surrogate-Based Recurrence Framework (2010, background): “Examples of such approaches include proper orthogonal decomposition (POD) [4–7], Volterra series [4,8], and surrogate-based approaches [9–13].”
Reduced order unsteady aerodynamic modeling for stability and control analysis using computational fluid dynamics (2014, background): “For example, Lieu and Farhat [65] proposed a ROM based on POD for evaluating aeroelastic frequencies and damping ratio coefficients of the F-16 aircraft.”
Interpolation among reduced‐order matrices to obtain parameterized models for design, optimization and probabilistic analysis (2009, methodology): “This subspace angle interpolation has been used for parameterized ROMs for large-scale aeroelastic simulations [29, 31–33], but proved difficult to extend for variation of more than one parameter and is limited to interpolation between two sets of basis vectors at a time [34].”
The GNAT method for nonlinear model reduction: Effective implementation and application to computational fluid dynamics and turbulent flows (2012, unknown): “Within these contexts, projection-based model-reduction methods have been successfully applied to problems in structural dynamics [3], aerodynamics [9, 10, 11, 12, 13] and aeroelasticity [14, 15, 16, 17, 18, 19], among others.”
An Online Method for Interpolating Linear Parametric Reduced-Order Models (2011, methodology): “Furthermore, for aerodynamic applications, these methods were shown to fail to produce a ROM that can operate in the transonic regime, for example, when the parameter set includes the free-stream Mach number [9].”