Surrogate model approach for investigating the stability of a friction-induced oscillator of Duffing’s type

authored by

Jan N. Fuhg, Amélie Fau

Abstract

Parametric studies are required to detect instability regimes of dynamic systems. This prediction can be computationally demanding as it requires a fine exploration of large parametric space due to the disrupted mechanical behavior. In this paper, an efficient surrogate strategy is proposed to investigate the behavior of an oscillator of Duffing’s type in combination with an elasto-plastic friction force model. Relevant quantities of interest are discussed. Sticking time is considered using a machine learning technique based on Gaussian processes called kriging. The largest Lyapunov exponent is considered as an efficient indicator of chaotic motion. This indicator is estimated using a perturbation method. A dedicated adaptive kriging strategy for classification called MiVor is utilized and appears to be highly proficient in order to detect instabilities over the parametric space and can furthermore be used for complex response surfaces in multi-dimensional parametric domains.

Organisation(s)

Institute of Continuum Mechanics
Institute of Mechanics and Computational Mechanics
PhoenixD: Photonics, Optics, and Engineering - Innovation Across Disciplines

Type

Article

Journal

Nonlinear dynamics

Volume

98

Pages

1709-1729

No. of pages

21

ISSN

0924-090X

Publication date

11.2019

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Control and Systems Engineering, Aerospace Engineering, Ocean Engineering, Mechanical Engineering, Applied Mathematics, Electrical and Electronic Engineering

Electronic version(s)

https://arxiv.org/abs/1907.02208 (Access: Open)
https://doi.org/10.1007/s11071-019-05281-2 (Access: Closed)