Development of Aeroelastic and Aeroservoelastic Reduced Order Models for Active Structural Control

Song, Hongjun; Qian, Jing; Wang, Yi; Pant, Kapil; Chin, Alexander W.; Brenner, Marty

doi:10.2514/6.2015-2055

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…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. In this simulation, a realistic and conservative value (zero) of structural damping is used [61,63].…”

Section: Aerodynamic Reduced Order Modelingsupporting

confidence: 68%

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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Section: Aerodynamic Reduced Order Modelingsupporting

confidence: 68%

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

et al. 2021

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“…Chen et al [24] proposed an active flutter control law design method based on an improved POD-BT/ROM and demonstrated by the Goland wing+/store system, and balanced truncation method was applied to further reduce the dimension of the time-domain POD/ROM. Song et al [25] presented a holistic methodology and framework that uniquely integrates the aerodynamics ROM, structural dynamics model, and active structural control into a single platform to enable integrated, ultra-fast ROMs for efficient aeroelastic and aeroservoelastic analysis, and controller design. Nie et al [26] used a CFD-based ROM coupled with computational structural dynamics (CSD) to predict the flutter.…”

Section: Introductionmentioning

confidence: 99%

An efficient reduced-order framework for active/passive hybrid flutter suppression

Wang

Ronch

et al. 2022

Aeronaut. j.

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Flutter suppression is an important measure to improve fatigue life and enhance the performance of aircraft in modern aircraft design. In order to design more effective controllers for flutter suppression with high efficiency, an efficient reduced-order framework for active/passive hybrid flutter suppression is proposed. The traditional CFD-based ROMs have been successfully applied to active flutter suppression with high accuracy and efficiency. But, when a structure modification is made such as in aeroelastic tailoring and aeroelastic structural optimisation, the structural model should be updated, and the expensive, time-consuming CFD-based ROMs have to be reconstructed; such a process is impractical for passive flutter suppression. To overcome the realistic challenge, an efficient reduced-order framework for active/passive hybrid flutter suppression is proposed by extending an efficient aeroelastic CFD-based POD/ROM which we have developed. The proposed framework is demonstrated and evaluated using an improved AGARD 445.6 wing model. The results show that the proposed framework can accurately predict the aeroelastic response for active/passive hybrid flutter suppression with high efficiency. It provides a powerful tool for active/passive hybrid flutter suppression, and therefore, is ideally suited to design more effective controllers, and may have the potential to reduce the overall cost of aircraft design.

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“…These results [10] represent the first time that the ERA/SOCIT algorithms were used for the identification of unsteady aerodynamic systems. It is valuable to point out that this method is now being applied at several organizations around the world [11][12][13][14][15][16]. In the area of fluid modal decompositions using, primarily, the Proper Orthogonal Decomposition (POD), the application of the ERA algorithm has become standard, with an initial appearence in the literature by Ma, Ahuja, and Rowley [17].…”

Section: Introductionmentioning

confidence: 99%

AEROM: NASA’s Unsteady Aerodynamic and Aeroelastic Reduced-Order Modeling Software

Silva

2018

Aerospace

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The origins, development, implementation, and application of AEROM, NASA's patented reduced-order modeling (ROM) software, are presented. Using the NASA FUN3D computational fluid dynamic (CFD) code, full and ROM aeroelastic solutions are computed at several Mach numbers and presented in the form of root locus plots. The use of root locus plots will help reveal the aeroelastic root migrations with increasing dynamic pressure. The method and software have been applied successfully to several configurations including the Lockheed-Martin N+2 supersonic configuration and the Royal Institute of Technology (KTH, Sweden) generic wind-tunnel model, among others. The software has been released to various organizations with applications that include CFD-based aeroelastic analyses and the rapid modeling of high-fidelity dynamic stability derivatives. We present recent results obtained from the application of the method to the AGARD 445.6 wing that reveal several interesting insights.

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Development of Aeroelastic and Aeroservoelastic Reduced Order Models for Active Structural Control

Cited by 4 publications

References 11 publications

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

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

An efficient reduced-order framework for active/passive hybrid flutter suppression

AEROM: NASA’s Unsteady Aerodynamic and Aeroelastic Reduced-Order Modeling Software

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