Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-11T08:18:49.242Z Has data issue: false hasContentIssue false
  • English
  • Français

Evaluation of Nonlinear System Parameters by Stochastic Spectral Method

Published online by Cambridge University Press:  05 May 2011

T.-P. Chang  and
M.-F. Liu
T.-P. Chang*
Affiliation:
Department of Construction Engineering, National Kaohsiung First University of Science & Technology, Kaohsiung, Taiwan 81164, R.O.C.
M.-F. Liu*
Affiliation:
Department of Applied Mathematics, I-Shou University, Kaohsiung, Taiwan 84001, R.O.C.
*
*Professor
**Associate Professor
Get access

Abstract

A stochastic spectral method is proposed to estimate the nonlinear parameters of a system subjected to random excitation. Sometimes, it is impossible to measure the excitation process; therefore, estimation must then be based on the response measurement only, in conjunction with a stochastic model of the excitation. The concept of this method is to establish an objecting function involving the unknown parameters through frequency domain analysis, and then we can minimize the objecting function to estimate the unknown parameters by using standard nonlinear optimization algorithm. Finally, the validation of the proposed method is accomplished through many simulations and it can be concluded that the proposed method is applicable to a variety of different problems.

Keywords

Stochastic spectral methodNonlinear parametersRandom excitationObjecting function.
Type
Articles
Information
Journal of Mechanics , Volume 23 , Issue 4 , December 2007 , pp. 269 - 274
Copyright
Copyright © The Society of Theoretical and Applied Mechanics, R.O.C. 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Gawthrop, P. J., Kountzeris, A. and Roberts, J. B., M“Parametric Identification of Non-Linear Ship Roll Motion from Forced Roll Data,” SNAME J. Ship Research, 32, pp. 101–111 (1988).CrossRefGoogle Scholar
2.Kountzeris, A., Roberts, J. B. and Gawthrop, P. J., “Estimation of Ship Roll Parameters from Motion in Irregular Seas,” Trans. Royal Institute of Naval Architects, 132, pp. 253–266(1991).Google Scholar
3.Rice, H. J., and Fitzpatrick, J. A., “The Measurement of Nonlinear Damping in Single-Degree-Of-Freedom System,” ASME, J. of Vibration and Acoustics, 113, pp. 132–140(1991).CrossRefGoogle Scholar
4.Rice, H. J., and Fitzpatrick, J. A., “A Procedure for the Identification of Linear and Non-Linear Multi-Degreeof-Freedom Systems,” J. of Sound and Vibration, 149, pp. 397–411 (1991).CrossRefGoogle Scholar
5.Roberts, J. B., “A Stochastic Theory for Non-Linear Ship Rolling in Irregular Seas,” SNAME J. Ship Research, 26, pp. 229–245 (1982).CrossRefGoogle Scholar
6.Roberts, J. B., and Dacunha, N. M. C., “Roll Motion of a Ship in Random Beam Waves Comparison Between Theory and Experiment,” SNAME, J. of Ship Research, 29, pp. 112–126.(1985).CrossRefGoogle Scholar
7.Kountzeris, A., Roberts, J. B. and Gawthrop, P. J., “Estimation of Ship Roll Parameters from Motion in Irregular Seas,” Transaction of the Royal Institute of Naval Architects, 132, pp. 253–266 (1991).Google Scholar