Reduced-order fluid/structure modeling of a complete aircraft configuration

By Thuan Lieu, Charbel Farhat, M. Lesoinne. 2006

353 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computational Mechanics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/79b7f573e6140caf4c60bb5c520e36775801433c

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In Aerospace Engineering

In Computational Mechanics

In Statistical and Nonlinear Physics

Citations in the corpus

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 parametrized ROMs of large-scale aeroelastic simulations [29, 31, 32, 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].”
Local improvements to reduced-order models using sensitivity analysis of the proper orthogonal decomposition (2009, methodology): “Other approaches based on the idea of mode interpolation can be found in the literature (Lehmann et al. 2005; Lieu et al. 2006; Morzyński et al. 2007).”
Structure‐preserving, stability, and accuracy properties of the energy‐conserving sampling and weighting method for the hyper reduction of nonlinear finite element dynamic models (2015, background): “It is also essential for those what if? problem scenarios where real-time simulation responses are needed [1, 2].”
A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems (2015, neutral): “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].”
Efficient non‐linear model reduction via a least‐squares Petrov–Galerkin projection and compressive tensor approximations (2011, methodology): “Within these contexts, the projection-based model reduction technique has been successfully applied to problems in aerodynamics [8, 9, 10, 11, 12], aeroelasticity [13, 14, 15, 16, 17], control [18], structural dynamics [19, 20, 21, 22], and non destructive evaluation and parameter estimation [23], t”
The GNAT method for nonlinear model reduction: Effective implementation and application to computational fluid dynamics and turbulent flows (2012, methodology): “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, background): “For many computational engineering applications, the construction of a projectionbased ROM can, however, be computationally expensive, as it typically relies on querying the underlying HFM (see [1] for sample CPU details).”
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 ”