Efficient reduced-order aerodynamic modeling for fast prediction of transonic flutter boundary
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Efficient reduced-order aerodynamic modeling for fast prediction of transonic flutter boundary Haojie Liu1 · Xiumin Gao2 · Rui Huang1 Received: 2 July 2020 / Revised: 24 August 2020 / Accepted: 12 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract In this study, a reduced-order aerodynamic modeling framework by integration of a novel training data interpolation approach and the Eigensystem realization algorithm (ERA) is presented for fast prediction of transonic flutter boundary over a range of flight parameters. First, aerodynamic impulse responses are computed by using the technique of computational fluid dynamics (CFD) at grid points within parameter space, which are selected to cover the transonic regime of concern. Next, the training data interpolation approach by combining the discrete empirical interpolation method with the Kriging technique is used to generate the aerodynamic impulse response at arbitrary flight condition, without performing unsteady CFD simulations. Finally, the interpolated impulse response is used by ERA to extract the linear state-space aerodynamic model, which is coupled with the structural model for flutter characteristics analysis. To illustrate the proposed approach, a NACA 0012 airfoil of two degrees of freedom at zero mean angle of attack is investigated. The transonic flutter boundaries of the airfoil agree well with those obtained by using CFD-based technique. Keywords Reduced-order aerodynamic modeling · Eigensystem realization algorithm · Discrete empirical interpolation method · Transonic flutter boundary
1 Introduction The last decade has witnessed various studies on CFD-based reduced-order modeling of transonic aerodynamic system, which can maintain similar accuracy and significantly lower the computational costs of computational fluid dynamics (CFD) [1]. In general, these aerodynamic reduced-order models (ROMs) can be classified into intrusive and nonintrusive approaches. For the intrusive type, such as the proper orthogonal decomposition (POD)/Galerkin projection [2, 3] and the harmonic balance approach [4], it is necessary to project the flow governing equations on a reduced basis and change the flow solvers accordingly. On the other hand, the nonintrusive type is data-driven and more user-friendly for aeroelasticians [5–10]. A few studies have been conducted to
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Rui Huang [email protected]
1
State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, People’s Republic of China
2
School of Physical and Mathematical Sciences, Nanjing Tech University, Nanjing, People’s Republic of China
establish nonintrusive aerodynamic ROMs, both linear and nonlinear, such as Eigensystem realization algorithm (ERA) [11], Volterra theory [12], subspace identification method [13], auto regressive-moving-average (ARMA) model [14], surrogate models via artificial neural networks [15], Kriging technique [16], and Wiener-type cascade model [17]. As one representative nonintru
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