Topic
Modal analysis using FEM
About: Modal analysis using FEM is a research topic. Over the lifetime, 1538 publications have been published within this topic receiving 26128 citations.
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TL;DR: By introducing a decomposition of the spectral density function matrix, the response spectra can be separated into a set of single degree of freedom systems, each corresponding to an individual mode, and close modes can be identified with high accuracy even in the case of strong noise contamination of the signals.
Abstract: In this paper a new frequency domain technique is introduced for the modal identification of output-only systems, i.e. in the case where the modal parameters must be estimated without knowing the input exciting the system. By its user friendliness the technique is closely related to the classical approach where the modal parameters are estimated by simple peak picking. However, by introducing a decomposition of the spectral density function matrix, the response spectra can be separated into a set of single degree of freedom systems, each corresponding to an individual mode. By using this decomposition technique close modes can be identified with high accuracy even in the case of strong noise contamination of the signals. Also, the technique clearly indicates harmonic components in the response signals.
1,312 citations
TL;DR: In this paper, the voltage stability analysis of large power systems by using a modal analysis technique is discussed, using a steady-state system model, a specified number of the smallest eigenvalues and the associated eigenvectors of a reduced Jacobian matrix.
Abstract: The authors discuss the voltage stability analysis of large power systems by using a modal analysis technique. The method computes, using a steady-state system model, a specified number of the smallest eigenvalues and the associated eigenvectors of a reduced Jacobian matrix. The eigenvalues, each of which is associated with a mode of voltage/reactive power variation, provide a relative measure of proximity to voltage instability. The eigenvectors are used to describe the mode shape and to provide information about the network elements and generators which participate in each mode. A simultaneous iteration method, which is well suited to applications involving large power systems, is used for selective calculation of appropriate eigenvalues. Results obtained using a 3700 bus test system are presented illustrating the applicability of the approach. >
1,002 citations
TL;DR: In this paper, a methodology is presented which extends to non-linear systems the concept of normal modes of motion which is well developed for linear systems and demonstrates how an approximate nonlinear version of superposition can be employed to reconstruct the overall motion from the individual nonlinear modal dynamics.
568 citations
TL;DR: Improved formulation of modal matching utilizes a new type of finite element formulation that allows for an object's eigenmodes to be computed directly from available image information, and is applicable to data of any dimensionality.
Abstract: Modal matching is a new method for establishing correspondences and computing canonical descriptions. The method is based on the idea of describing objects in terms of generalized symmetries, as defined by each object's eigenmodes. The resulting modal description is used for object recognition and categorization, where shape similarities are expressed as the amounts of modal deformation energy needed to align the two objects. In general, modes provide a global-to-local ordering of shape deformation and thus allow for selecting which types of deformations are used in object alignment and comparison. In contrast to previous techniques, which required correspondence to be computed with an initial or prototype shape, modal matching utilizes a new type of finite element formulation that allows for an object's eigenmodes to be computed directly from available image information. This improved formulation provides greater generality and accuracy, and is applicable to data of any dimensionality. Correspondence results with 2D contour and point feature data are shown, and recognition experiments with 2D images of hand tools and airplanes are described. >
527 citations
TL;DR: The Modal Assurance Criterion (MAC) as discussed by the authors is a statistical indicator that is most sensitive to large differences and relatively insensitive to small differences in the mode shapes, which can only indicate consistency and does not indicate validity or orthogonality.
449 citations