Unsteady aerodynamic modeling based on POD-observer method

Yang, Chao; Liu, Xiaoyan; Wu, Zhigang

doi:10.1007/s11431-010-3178-2

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…An appropriate number of modes are very essential for guaranteeing the accuracy of earthquake ground motion identification and reducing the computational complexity. In this study, the POD method is utilized to determine the mode order.…”

Section: Methodsmentioning

confidence: 99%

Identification of earthquake ground motion based on limited acceleration measurements of structure using Kalman filtering technique

Luo

Wan

et al. 2019

Struct Control Health Monit

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Summary Identification of earthquake ground motion from structural health monitoring (SHM) data provides a good means to reconstruct seismic loads that are essential for postearthquake safety assessments and disaster simulations of structures. Because the data measured by an SHM system are structural absolute response, they cannot be directly applied to the structural motion equation, which is established in relative coordinate system. As such, this paper originally derives the motion equation in absolute coordinate system and then expands the equation into modal space. In addition, the proposed method allows for identifying earthquake ground motion using incomplete modal information and limited measurements through the standard Kalman filter. Subsequently, a numerical two‐dimensional frame is used to validate the feasibility of the proposed method, and the influences of modal parameters and measurement noise on the identification accuracy are also fully investigated. The results show that the proposed method is sensitive to frequency and measurement noise but insensitive to modal shape and damping ratio. It is also found that the identified ground motion subjected to certain measure noise can still be reliably employed for postseismic response calculations of medium‐ and long‐period structures. Finally, a shaking table test performing on a five‐floor frame further demonstrates the effectiveness and accuracy of the proposed identification algorithm for practical application.

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Section: Methodsmentioning

confidence: 99%

Identification of earthquake ground motion based on limited acceleration measurements of structure using Kalman filtering technique

Luo

Wan

et al. 2019

Struct Control Health Monit

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“…Recently, a POD-based state observer was investigated for analysis of the concentration of contaminants in the air flow in a building system by a 2D advection-diffusion equation (John et al 2010) and the aerodynamic response of a 2D airfoil undergoing unsteady motion (Yang et al 2010). Milovanovic and Aamo (2013) dealt with a flow control problem using a state observer to attenuate vortex shedding from a cylinder.…”

Section: State Observersmentioning

confidence: 99%

Numerical simulation of real-world flows

Hayase

2015

Fluid Dyn. Res.

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Obtaining real flow information is important in various fields, but is a difficult issue because measurement data are usually limited in time and space, and computational results usually do not represent the exact state of real flows. Problems inherent in the realization of numerical simulation of real-world flows include the difficulty in representing exact initial and boundary conditions and the difficulty in representing unstable flow characteristics. This article reviews studies dealing with these problems. First, an overview of basic flow measurement methodologies and measurement data interpolation/ approximation techniques is presented. Then, studies on methods of integrating numerical simulation and measurement, namely, four-dimensional variational data assimilation (4D-Var), Kalman filters (KFs), state observers, etc are discussed. The first problem is properly solved by these integration methodologies. The second problem can be partially solved with 4D-Var in which only initial and boundary conditions are control parameters. If an appropriate control parameter capable of modifying the dynamical structure of the model is included in the formulation of 4D-Var, unstable modes are properly suppressed and the second problem is solved. The state observer and KFs also solve the second problem by modifying mathematical models to stabilize the unstable modes of the original dynamical system by applying feedback signals. These integration methodologies are now applied in simulation of real-world flows in a wide variety of research fields. Examples are presented for basic fluid dynamics and applications in meteorology, aerospace, medicine, etc.

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“…In addition, some combinations between POD and system identification methods have also been proposed in recent years [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Unsteady Aerodynamic Modeling Based on POD-ARX

Wang

Zhang

et al. 2018

International Journal of Aerospace Engineering

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The lack of stability is a problem encountered when applying the classical POD-Galerkin method to problems of unsteady compressible flows around a moving structure. To solve this problem, a hybrid reduced-order model named POD-ARX is constructed in this paper. The construction of this model involves two steps, including first extracting the fluid modes with the POD technique and then identifying the modal coefficients with the ARX model. The POD modes with the block of all modified primitive variables are extracted from the system response to the training signal. Once the POD modes are obtained, the snapshots are projected on these modes to determine the time history of modal coefficients and the resulting modal coefficients are used to identify the parameters of ARX model. Then, the ARX model is used to predict the modal coefficients of the system response to the validation signal. Sample two-dimensional aerodynamic force calculations are conducted to demonstrate this method. Results show that this method can produce a stable and accurate prediction to the aerodynamic response with significant improvement of computational efficiency for linear and even some nonlinear aerodynamic problems. In addition, this method also shows good wide-band characteristics by using the “3211” multistep signal as the training signal.

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Unsteady aerodynamic modeling based on POD-observer method

Cited by 7 publications

References 23 publications

Identification of earthquake ground motion based on limited acceleration measurements of structure using Kalman filtering technique

Identification of earthquake ground motion based on limited acceleration measurements of structure using Kalman filtering technique

Numerical simulation of real-world flows

Unsteady Aerodynamic Modeling Based on POD-ARX

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