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 -