Parametric identification of nonlinearity in structural systems using describing function inversion

Date

2013-10-01

Author

Aykan, Murat
Özgüven, Hasan Nevzat

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

160
views

0
downloads

Most engineering structures include nonlinearity to some degree. Depending on the dynamic conditions and level of external forcing, sometimes a linear structure assumption may be justified. However, design requirements of sophisticated structures such as satellites may require nonlinear behavior to be considered for better performance. Therefore, it is very important to successfully detect, localize and parametrically identify nonlinearity in such cases. In engineering applications, the location of nonlinearity and its type may not be always known in advance. Furthermore, in most of the applications in structural dynamics, linear FRF matrices constructed from experimental measurements will not be complete. These handicaps make most of the methods given in the literature difficult to apply to engineering structures. The aim of this study is to improve a previously developed method considering these practical limitations. The approach proposed can be used for detection, localization, characterization and parametric identification of nonlinear elements by using incomplete FRF data. In order to reduce the effort and avoid the limitations in using footprint graphs for identification of nonlinearity, describing function inversion is used. Thus, it is made possible to identify the restoring force of more than one type of nonlinearity which may co-exist at the same location. The validation of the method is demonstrated with case studies based on simulated experiments, as well as real experiments with two nonlinear structures. It is concluded in this study that the approach proposed improves the previously developed method by avoiding the use of footprint graphs in nonlinear identification and also by making it possible to identify more than one type of nonlinearity that may co-exist at the same location.

Subject Keywords

Control and Systems Engineering, Signal Processing, Mechanical Engineering, Civil and Structural Engineering, Aerospace Engineering, Computer Science Applications

URI

https://hdl.handle.net/11511/47555

Journal

MECHANICAL SYSTEMS AND SIGNAL PROCESSING

DOI

https://doi.org/10.1016/j.ymssp.2013.03.016

Collections

Department of Mechanical Engineering, Article

Suggestions

OpenMETU
Core

Identification of localized nonlinearity for dynamic analysis of structures Aykan, Murat; Özgüven, Hasan Nevzat; Department of Mechanical Engineering (2013) Most engineering structures include nonlinearity to some degree. Depending on the dynamic conditions and level of external forcing, sometimes a linear structure assumption may be justified. However, design requirements of sophisticated structures such as satellites, stabilized weapon systems and radars may require nonlinear behavior to be considered for better performance. Therefore, it is very important to successfully detect, localize and parametrically identify nonlinearity in such cases. In engineering ...
A novel modal superposition method with response dependent nonlinear modes for periodic vibration analysis of large MDOF nonlinear systems Ferhatoglu, Erhan; Ciğeroğlu, Ender; Özgüven, Hasan Nevzat (Elsevier BV, 2020-01-01) Design of complex mechanical structures requires to predict nonlinearities that affect the dynamic behavior considerably. However, finding the forced response of nonlinear structures is computationally expensive, especially for large ordered realistic finite element models. In this paper, a novel approach is proposed to reduce computational time significantly utilizing Response Dependent Nonlinear Mode (RDNM) concept in determining the steady state periodic response of nonlinear structures. The method is ap...
Model updating of nonlinear structures from measured FRFs Canbaloglu, Guvenc; Özgüven, Hasan Nevzat (Elsevier BV, 2016-12-01) There are always certain discrepancies between modal and response data of a structure obtained from its mathematical model and experimentally measured ones. Therefore it is a general practice to update the theoretical model by using experimental measurements in order to have a more accurate model. Most of the model updating methods used in structural dynamics are for linear systems. However, in real life applications most of the structures have nonlinearities, which restrict us applying model updating techn...
Nonlinear structural modification and nonlinear coupling Kalaycioglu, Taner; Özgüven, Hasan Nevzat (Elsevier BV, 2014-06-03) Structural modification methods were proved to be very useful for large structures, especially when modification is local. Although there may be inherent nonlinearities in a structural system in various forms such as clearances, friction and cubic stiffness, most of the structural modification methods are for linear systems. The method proposed in this work is a structural modification/coupling method developed previously, and extended to systems with nonlinear modification and coupling. The method is most ...
Calibration of the finite element model of a long span cantilever through truss bridge using artificial neural networks Yücel, Ömer Burak; Hasançebi, Oğuzhan; Department of Civil Engineering (2008) In recent years, Artificial Neural Networks (ANN) have become widely popular tools in various disciplines of engineering, including civil engineering. In this thesis, Multi-layer perceptron with back-propagation type of network is utilized in calibration of the finite element model of a long span cantilever through truss called Commodore Barry Bridge (CBB). The essence of calibration lies in the phenomena of comparing and correlating the structural response of an analytical model with experimental results a...

Citation Formats

M. Aykan and H. N. Özgüven, “Parametric identification of nonlinearity in structural systems using describing function inversion,” MECHANICAL SYSTEMS AND SIGNAL PROCESSING, pp. 356–376, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47555.