Details
| Original language | English |
|---|---|
| Pages (from-to) | 1709-1729 |
| Number of pages | 21 |
| Journal | Nonlinear dynamics |
| Volume | 98 |
| Issue number | 3 |
| Early online date | 9 Oct 2019 |
| Publication status | Published - Nov 2019 |
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.
Keywords
- Adaptive sampling, Dry friction, Lyapunov exponents, Machine learning, Non-smooth system, Stick–slip instability, Surrogate model
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Aerospace Engineering
- Engineering(all)
- Ocean Engineering
- Engineering(all)
- Mechanical Engineering
- Mathematics(all)
- Applied Mathematics
- Engineering(all)
- Electrical and Electronic Engineering
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In: Nonlinear dynamics, Vol. 98, No. 3, 11.2019, p. 1709-1729.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Surrogate model approach for investigating the stability of a friction-induced oscillator of Duffing’s type
AU - Fuhg, Jan N.
AU - Fau, Amélie
N1 - Funding Information: The authors acknowledge the financial support from the Deutsche Forschungsgemeinschaft under Germany?s Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453). The results presented in this paper were partially carried out on the cluster system at the Leibniz University of Hannover, Germany.
PY - 2019/11
Y1 - 2019/11
N2 - 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.
AB - 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.
KW - Adaptive sampling
KW - Dry friction
KW - Lyapunov exponents
KW - Machine learning
KW - Non-smooth system
KW - Stick–slip instability
KW - Surrogate model
UR - http://www.scopus.com/inward/record.url?scp=85074435106&partnerID=8YFLogxK
U2 - 10.1007/s11071-019-05281-2
DO - 10.1007/s11071-019-05281-2
M3 - Article
AN - SCOPUS:85074435106
VL - 98
SP - 1709
EP - 1729
JO - Nonlinear dynamics
JF - Nonlinear dynamics
SN - 0924-090X
IS - 3
ER -