A Reduced Order Model Based on Block Arnoldi Method for Aeroelastic System

Zhou, Qiang; Chen, Gang; Li, Yueming

doi:10.1142/s1758825114500690

Cited by 8 publications

(8 citation statements)

References 26 publications

(34 reference statements)

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“…The agreement between the ROM and the large computational aeroelastic solver is good for all Mach numbers considered (0.499 to 1.141), including the well-known transonic dip of the flutter speed. The accuracy of both methods have been evaluated over the years in a number of aeroelastic studies (Chen et al, 2012;Zhou et al, 2014;Zhou et al, 2016b). (Yates, 1987).…”

Section: Pod/rom Solver Validationmentioning

confidence: 99%

An efficient implementation of aeroelastic tailoring based on efficient computational fluid dynamics-based reduced order model

Gong

Ronch

et al. 2019

Journal of Fluids and Structures

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Current and future trends in the aerospace industry leverage on the potential benefits provided by lightweight materials that can be tailored to realize desired mechanical characteristics when loaded. For aircraft design, the deployment of aeroelastic tailoring is hindered by the need to re-compute, for any possible modification of the structure, the dependence of the aerodynamic field on the underlying structural properties. To make progress in this direction, the work presents a rapid computational fluid dynamics based aeroelastic tool which is built around a reduced order model for the aerodynamics that is updated for any modification of the structure by using the structural dynamics reanalysis method. The aeroelastic tailoring tool is demonstrated in transonic flow for the AGARD 445.6 wing, suitably modified with composite materials. It was found that the proposed method provides accurate engineering predictions for the aeroelastic response and stability when the structure is modified from the baseline model.

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Section: Pod/rom Solver Validationmentioning

confidence: 99%

An efficient implementation of aeroelastic tailoring based on efficient computational fluid dynamics-based reduced order model

Gong

Ronch

et al. 2019

Journal of Fluids and Structures

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“…The coupled aeroelastic solver has been exercised on several two and three-dimensional aeroelastic models. The reader is referred to [19,23,44] for more details.…”

Section: Coupling Algorithmmentioning

confidence: 99%

“…The POD method, in particular, has been successfully applied to the aeroelastic analysis of turbine blades [11,12]，helicopter rotor blade [13], wings [14][15][16] and complete aircraft configurations [17,18]. More recently, the POD has been exercised for transonic aeroelastic analysis [19], active aeroelastic control [20], LCO control [21], gust response analysis [22], and transonic flutter suppression with control delay [23].…”

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confidence: 99%

Efficient aeroelastic reduced order model with global structural modifications

Chen

Zhou

et al. 2018

Aerospace Science and Technology

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Time domain aeroelastic analysis has high computing costs when using computational fluid dynamics. These costs become prohibitive when the structural model undergoes large changes from the baseline design, as within an aircraft design process. To overcome this realistic challenge, we have developed, implemented, and demonstrated an efficient method that is robust in the presence of global modifications of the structure. The method consists of: a) a reduced order model of the linearized Navier-Stokes equations generated around an aeroelastic equilibrium that depends, in turn, on the structural model; b) an approximate structural dynamic reanalysis method valid for global modifications of the structure; and c) a mechanism to exchange information between fluid and structural solvers without need for calculating at each iteration of the structural design an eigenvalue problem of the modified structure. The resulting aeroelastic reduced order model is demonstrated for the AGARD 445.6 wing, and material properties are varied up to 100% from their original values. It is found that: a) predictions of the time domain aeroelastic response and of the flutter speed are accurate for all modifications of the structure; and b) the computational efficiency of the proposed aeroelastic reduced order model is linearly proportional to the number of structural configurations considered. The method, therefore, is ideally suited for optimization and uncertainty studies.

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“…The interested reader may find more details on the linearized process and POD projection used in the present study in Refs. (Chen et al, 2010;Zhou et al, 2014).…”

Section: Model Reduction For Aeroelastic Problemmentioning

confidence: 99%

“…The aerofoil section and the aeroelastic parameters differ from those presented in Section 4.1, and are summarized in Table 3 for the present test case. The authors carried out a systematic analysis for this problem, see (Zhou et al, 2014) and (Da Ronch et al, 2012), making it a suitable problem for control design synthesis. The trailing-edge control rotation is denoted by β, and is determined from an output feedback action…”

Section: Active Flutter Suppressionmentioning

confidence: 99%

Computational fluid dynamics-based transonic flutter suppression with control delay

Zhou

Ronch

et al. 2016

Journal of Fluids and Structures

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This work investigates the effects of control input time delay on closed-loop transonic computational aeroelastic analysis. Control input time delays are becoming critical as the demand for high frequency control actions is increasing. The flow in transonic conditions exhibits strong nonlinearities which require accurate physical modelling techniques, in turn resulting in large dimensional systems that are computationally costly to solve. A unified framework is demonstrated for the robust and efficient generation of reduced order models. Once generated, the reduced order model is employed for the flutter boundary search, and excellent agreement with the large order coupled model is demonstrated. The aero-servo-elastic reduced order model is then exploited to design a feedback control law, which is implemented in the fully coupled computational fluid/structural dynamics solver. As expected, the controller effectiveness is found to degrade for increasing time delay, up to a critical value where the controller fails to suppress flutter. It is shown that a controller for a time-delay system may be designed using the same aero-servo-elastic reduced order model, incurring in no extra costs or complications. The new controller is found to achieve excellent flutter suppression characteristics. The aero-servo-elastic reduced order model may also be used to identify, for a given feedback controller, the critical value of control input time delay at which the closed-loop aero-servo-elastic system loses its stability. The test cases are for a two-dimensional pitch-plunge aerofoil section and the AGARD 445.6 wing modified with a trailing-edge control surface.

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A Reduced Order Model Based on Block Arnoldi Method for Aeroelastic System

Cited by 8 publications

References 26 publications

An efficient implementation of aeroelastic tailoring based on efficient computational fluid dynamics-based reduced order model

An efficient implementation of aeroelastic tailoring based on efficient computational fluid dynamics-based reduced order model

Efficient aeroelastic reduced order model with global structural modifications

Computational fluid dynamics-based transonic flutter suppression with control delay

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