Reduced-Order Models for Computational-Fluid-Dynamics-Based Nonlinear Aeroelastic Problems

Mannarino, Andrea; Dowell, Earl H.

doi:10.2514/1.j053775

Cited by 32 publications

(6 citation statements)

References 29 publications

(48 reference statements)

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“…Chen et al 184 used the support vector machine method to establish a nonlinear aerodynamic reduced‐order model, and studied the transonic flutter problem of a binary wing. Mannarino and Dowell 185 and Zhang et al 186 applied the hierarchical identification to establish the nonlinear aerodynamic reduced‐order models of the wings. In the model, the linear part was constructed by a linear reduction model, and the nonlinear part was identified by a neural network model.…”

Section: Research Status Of Aeroelastic Analysismentioning

confidence: 99%

Aeroelastic analysis and flutter control of wings and panels: A review

Chai

Gao

Ankay

et al. 2021

Int Journal of Mech Sys Dyn

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Flutter is a self-excited vibration under the interaction of the inertial force, aerodynamic force, and elastic force of the structure. After the flutter occurs, the aircraft structures will exhibit limit cycle oscillation, which will cause catastrophic accidents or fatigue damage to the structures. Therefore, it is of great theoretical and practical significance to study the aeroelastic characteristics and flutter control for improving the aeroelastic stability of aircraft structures. This paper reviews the recent advances in aeroelastic analysis and flutter control of wings and panel structures. The mechanism of aeroelastic flutter of wings and panels is presented. The research methods of aeroelastic flutter for different structures developed in recent years are briefly summarized. Various control strategies including the linear and nonlinear control algorithms as well as the active flutter control results of wings and panels are presented. Finally, the paper ends with conclusions, which highlight challenges of the development in aeroelastic analysis and flutter control, and provide a brief outlook on the future investigations. This study aims to present a comprehensive understanding of aeroelastic analysis and flutter control. It can also provide guidance on the design of new wings and panel structures for improving their aeroelastic stability.

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“…A perturbation of sc around ̃ 0 sc following a prescribed time-dependent input profile is imposed throughout the transient CFD simulation that collects the GAF responses f arising from the perturbation. Several input profiles capable of capturing wide frequency contents were proposed and widely used for training data generation for aeroelastic modeling, including Walsh functions [53]; random-like and noisy sweep signal [16]; filtered white Gaussian noise [17]; and 3-2-1-1 profile [15,32]. In this paper, the 3-2-1-1 profile verified by our previous work [62] is used, which is able to excite a relatively broad frequency range that accommodates the targeted frequency in this study.…”

Section: Aerodynamic Reduced Order Modelingmentioning

confidence: 99%

“…Chen et al [15] developed an autoregressive model with exogenous input (ARX) and took into account CFD-induced uncertainties in aerodynamic ROM (A-ROM) development. Mannarino and Dowell [16] proposed an approach to identify a nonlinear state space ROM for unsteady aerodynamic responses in aeroelasticity. The ROM is obtained through a two-step procedure, viz., subspace projection to identify the linear part and output error minimization to fit the coefficients of the nonlinear terms that are a function of aerodynamic states.…”

Section: Introductionmentioning

confidence: 99%

State consistence of data-driven reduced order models for parametric aeroelastic analysis

et al. 2021

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This paper investigates the state consistence of parametric data-driven reduced order models (ROMs) in a state-space form obtained by various system identification methods, including autoregressive exogenous (ARX) and subspace identification (N4SID), for aeroelastic analysis in varying flight conditions. The target flight envelop is first partitioned into discrete grid points, on each of which an aerodynamic ROM is constructed using system identification to capture the dependence of the generalized aerodynamic force on the generalized displacement of structural modes. High-fidelity aeroelastic modal perturbation simulations are used to generate the ROM training and verification data. Aerodynamic ROMs not on the grid point are obtained by interpolating those at neighboring grid points. Through a thorough analysis of the model coefficients and pole migration, it is found that only the ARX-based aerodynamic ROM preserves the state consistence, and hence, allowing direct interpolation of system matrices at the non-grid point and rapid aerodynamic ROM database development in the entire flight parameter space. In contrast, N4SID-based ROM destroys the state consistence and yields physically meaningless results when ROMs are interpolated. The origin of the difference in the state consistence caused by both methods is also discussed. The interpolated ARX aerodynamic ROMs coupled with the structural ROM for parametric aeroelastic analysis exhibit excellent agreement with the high fidelity full order model (mostly <5% relative error) and salient computational efficiency.

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“…A parameter-varying estimation framework was proposed to predict flutter speed [14]. Computational fluid dynamics (CFD) based reduced-order-models, were used to study the control surface limit cycle oscillation (LCO) and the structure of aerodynamic model equations, as a combination of linear and nonlinear contributions [15,16]. Unfortunately, the actual wing is a complex nonlinear system, influenced by many uncertain factors.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Aeroelastic System Identification Based on Neural Network

et al. 2018

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This paper focuses on the nonlinear aeroelastic system identification method based on an artificial neural network (ANN) that uses time-delay and feedback elements. A typical two-dimensional wing section with control surface is modelled to illustrate the proposed identification algorithm. The response of the system, which applies a sine-chirp input signal on the control surface, is computed by time-marching-integration. A time-delay recurrent neural network (TDRNN) is employed and trained to predict the pitch angle of the system. The chirp and sine excitation signals are used to verify the identified system. Estimation results of the trained neural network are compared with numerical simulation values. Two types of structural nonlinearity are studied, cubic-spring and friction. The results indicate that the TDRNN can approach the nonlinear aeroelastic system exactly.

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“…The autoregressive with exogenous input (ARX) model can be utilized for stability analysis of linear systems [26,27]. Additionally, combinations of linear and nonlinear models in constructing ROM have been applied in several studies [28,29], in order to capture both linear and nonlinear dynamics. Moreover, there is another efficient method of calculating aerodynamic loads called the harmonic balance (HB) technique [30].…”

Section: Introductionmentioning

confidence: 99%

Novel Wiener models with a time-delayed nonlinear block and their identification

2016

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Popular Wiener models are a combination of dominant linear behavior and static nonlinear characteristics. However, they are less accurate in dealing with problems with strong nonlinear time-delayed effects. Hence, a group of novel nonlinear models based on Wiener models are proposed in this study. The static nonlinear part of general Wiener models is replaced by a time-delayed nonlinear block. Thus, two novel Wiener models with a quasi-dynamic nonlinear block are constructed, called the Wiener-QDN models. The autoregressive with exogenous input (ARX) model is utilized to identify the dynamic linear block, and the radial basis function neural network is used to build the nonlinear part of Wiener and the Wiener-QDN models. Results reveal the effectiveness of these models in nonlinear system identification. The first test cases are systems governed by simple equations. The Wiener-QDN models can identify these systems with or without measurement noise. These models are further applied in unsteady aerodynamic modeling cases. Test cases of a NACA 0012 airfoil pitching in transonic flow indicate that both linearity and nonlinearity are well captured by the proposed Wiener-QDN models.

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Reduced-Order Models for Computational-Fluid-Dynamics-Based Nonlinear Aeroelastic Problems

Cited by 32 publications

References 29 publications

Aeroelastic analysis and flutter control of wings and panels: A review

State consistence of data-driven reduced order models for parametric aeroelastic analysis

Nonlinear Aeroelastic System Identification Based on Neural Network

Novel Wiener models with a time-delayed nonlinear block and their identification

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