Details
| Original language | English |
|---|---|
| Article number | 113851 |
| Journal | Computer Methods in Applied Mechanics and Engineering |
| Volume | 381 |
| Early online date | 23 Apr 2021 |
| Publication status | Published - 1 Aug 2021 |
Abstract
The ultimate strength prediction of textile composite materials requires high-fidelity FE modeling with information-passing multiscale schemes and damage initiation and propagation algorithms. The numerical demand of this procedure together with the complexity of the observed response surface, hampers the quantification of uncertainties contributing to the scatter of strength values. This study proposes a surrogate methodology able to efficiently emulate the nonlinear multiscale procedure, based on a combination of artificial neural networks and Kriging modeling under a variable-fidelity framework. A triaxially braided textile under longitudinal tension is used as a use-case and the methodology is employed to identify the most critical parameters in terms of variance via a global sensitivity analysis technique. Results show strong interaction effects between the uncertain parameters. The approach is non-intrusive and can be easily extended to other types of textiles and load cases.
Keywords
- Braided composites, Failure prediction, Sensitivity analysis, Surrogate modeling, Uncertainty quantification, Variable-fidelity
ASJC Scopus subject areas
- Engineering(all)
- Computational Mechanics
- Engineering(all)
- Mechanics of Materials
- Engineering(all)
- Mechanical Engineering
- Physics and Astronomy(all)
- General Physics and Astronomy
- Computer Science(all)
- Computer Science Applications
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In: Computer Methods in Applied Mechanics and Engineering, Vol. 381, 113851, 01.08.2021.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A variable-fidelity hybrid surrogate approach for quantifying uncertainties in the nonlinear response of braided composites
AU - Balokas, Georgios
AU - Kriegesmann, Benedikt
AU - Czichon, Steffen
AU - Rolfes, Raimund
N1 - Funding Information: The provided financial support from the European Union’s Horizon 2020 research and innovation programme FULLCOMP under the Marie Skłodowska-Curie grant agreement No 642121 is gratefully acknowledged by the authors. Funding Information: The provided financial support from the European Union's Horizon 2020 research and innovation programme FULLCOMP under the Marie Skłodowska-Curie grant agreement No 642121 is gratefully acknowledged by the authors.
PY - 2021/8/1
Y1 - 2021/8/1
N2 - The ultimate strength prediction of textile composite materials requires high-fidelity FE modeling with information-passing multiscale schemes and damage initiation and propagation algorithms. The numerical demand of this procedure together with the complexity of the observed response surface, hampers the quantification of uncertainties contributing to the scatter of strength values. This study proposes a surrogate methodology able to efficiently emulate the nonlinear multiscale procedure, based on a combination of artificial neural networks and Kriging modeling under a variable-fidelity framework. A triaxially braided textile under longitudinal tension is used as a use-case and the methodology is employed to identify the most critical parameters in terms of variance via a global sensitivity analysis technique. Results show strong interaction effects between the uncertain parameters. The approach is non-intrusive and can be easily extended to other types of textiles and load cases.
AB - The ultimate strength prediction of textile composite materials requires high-fidelity FE modeling with information-passing multiscale schemes and damage initiation and propagation algorithms. The numerical demand of this procedure together with the complexity of the observed response surface, hampers the quantification of uncertainties contributing to the scatter of strength values. This study proposes a surrogate methodology able to efficiently emulate the nonlinear multiscale procedure, based on a combination of artificial neural networks and Kriging modeling under a variable-fidelity framework. A triaxially braided textile under longitudinal tension is used as a use-case and the methodology is employed to identify the most critical parameters in terms of variance via a global sensitivity analysis technique. Results show strong interaction effects between the uncertain parameters. The approach is non-intrusive and can be easily extended to other types of textiles and load cases.
KW - Braided composites
KW - Failure prediction
KW - Sensitivity analysis
KW - Surrogate modeling
KW - Uncertainty quantification
KW - Variable-fidelity
UR - http://www.scopus.com/inward/record.url?scp=85104581368&partnerID=8YFLogxK
U2 - 10.1016/j.cma.2021.113851
DO - 10.1016/j.cma.2021.113851
M3 - Article
AN - SCOPUS:85104581368
VL - 381
JO - Computer Methods in Applied Mechanics and Engineering
JF - Computer Methods in Applied Mechanics and Engineering
SN - 0045-7825
M1 - 113851
ER -