Eigensystem Realization Algorithm Method Research Articles

Experimental updating of a segmented wind turbine blade numerical model using the substructure method

During the development of the wind turbine system, blades are considered as the most critical components. The blades’ dynamic characteristics must be investigated during the design process to improve their mechanical performance. This work presents an experimental updating of a segmented wind turbine blade numerical model. For this purpose, the blade segments were manufactured using the fused deposition 3D printing technology and assembled using a threaded spar and nut. For different values of the segments assembly load, the natural frequencies and damping ratios were identified using the eigensystem realization algorithm method. The dynamic behavior of the segmented blade was examined numerically using the three-node shell element, taking into consideration the additional stiffness generated by the applied assembly load. In this study, numerical and experimental modal analysis were used to identify the first five eigenfrequencies of the blade. To update the numerical model parameters, through the identified experimental results, an iterative method was developed based on the Craig–Bampton substructure approach. Results show that the blade natural frequencies are proportional to the segments assembly load. The application of the proposed method on the segmented blade numerical model updating shows that the present method is computationally efficient.

A comparison among modal parameter extraction methods

An effective comparison among several methods for extraction of the modal parameters from the frequency response functions measurements is presented in this research. In particular, several curve fitting methods, which are the peak amplitude method, the circle fit method, the least square complex exponential method, the eigensystem realization algorithm method and the rational fraction polynomial method were implemented in Matlab environment and compared in terms of natural frequencies, modal damping and mode shapes. Measurements were performed on a carcass of the gearbox in free–free condition. A hammer has been used with a periodic impulsive excitation signal. The natural frequencies values obtained by all methods were very similar and the differences between the results were insignificant. The peak amplitude and the circle fit gave good results for the damping ratios. The rational fraction polynomial method did the best job in detecting the damping and frequency values. The results obtained by the least square complex exponential method and the eigensystem realization algorithm method were reasonable for both frequency and damping.

Efficient Nonlinear Reduced-Order Model for Computational Fluid Dynamics-Based Aeroelastic Analysis

In this work, an efficient nonlinear state-space reduced-order model (ROM) is introduced. The nonlinear state-space ROM consists of both linear terms and nonlinear terms, the former of which is obtained based on Volterra series theory and eigensystem realization algorithm method, whereas the latter is determined by an output error-minimization procedure. Two approaches are proposed to determine the training signal for identification of the nonlinear terms. Aeroelastic analysis of a weakened wall-mounted AGARD 445.6 wing is conducted and it is demonstrated that 1) the nonlinear state-space ROM can get more accurate unsteady aeroelastic response than traditional linear Volterra-based ROM with computational fluid dynamics coupling with computational structural dynamic as a reference method for comparison; 2) the nonlinear state-space ROM performs well in range of subsonic, transonic, and supersonic; and 3) the nonlinear state-space ROM can better characterize the nonlinear aeroelastic system than traditional linear Volterra-based ROM without the need of identifying second-order Volterra kernels, which thus can be easily applied to solve engineering problems for its concise form.

Control of large space structures using GPS—modal parameter identification and attitude and deformation estimation

AbstractThis paper proposes a method for identifying the modal parameters of large space structures and estimating their attitude and deformation using kinematic GPS. The Eigensystem Realization Algorithm method is used for the modal parameter identification, and for attitude and deformation estimation, we adopt the Kalman filter based on the identified system dynamics. The proposed method was evaluated through computer simulations and in ground‐based experiments, and the results confirmed its feasibility. © 2002 Wiley Periodicals, Inc. Electron Comm Jpn Pt 1, 86(4): 63–71, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecja.10045