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

verfasst von

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.

Organisationseinheit(en)

Institut für Kontinuumsmechanik
Institut für Baumechanik und Numerische Mechanik
PhoenixD: Simulation, Fabrikation und Anwendung optischer Systeme

Typ

Artikel

Journal

Nonlinear dynamics

Band

98

Seiten

1709-1729

Anzahl der Seiten

21

ISSN

0924-090X

Publikationsdatum

11.2019

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Steuerungs- und Systemtechnik, Luft- und Raumfahrttechnik, Meerestechnik, Maschinenbau, Angewandte Mathematik, Elektrotechnik und Elektronik

Elektronische Version(en)

https://arxiv.org/abs/1907.02208 (Zugang: Offen)
https://doi.org/10.1007/s11071-019-05281-2 (Zugang: Geschlossen)