Details
| Original language | English |
|---|---|
| Article number | 108845 |
| Journal | Composites science and technology |
| Volume | 211 |
| Early online date | 8 May 2021 |
| Publication status | Published - 28 Jul 2021 |
Abstract
Uncertainty quantification is critical for the full exploitation of composite materials’ potential. Inverse methods offer the possibility of indirectly characterizing the uncertainty of microscopic parameters by employing data sets from standard structural tests in higher scales. Two crucial requirements though, are the efficient modeling especially for the nonlinear prediction, and the measurement error availability from the tests which affects the updated scatter. This study employs effective stiffness and strength experimental data in order to quantify uncertainties of a carbon fiber UD composite in the microscale. A polynomial chaos surrogate model is trained from finite element simulations, able to efficiently predict the homogenized stiffness and strength for the uncertainty quantification procedure. The random parameters which are influential enough to be updated, are identified via a variance-based global sensitivity analysis. The inverse problem is solved with the Bayesian inference method, which updates any prior estimation of the probability models of the input parameters, based on output observations from the tests. Results show significant uncertainty reduction in comparison with typically used variance values in the literature and can be used to enrich the composite material databases. The proposed methodology is applied for the transverse tensile load case, although its non-intrusive nature allows applications for more load cases and various setups.
Keywords
- Bayesian inference, Polynomial chaos, Sensitivity analysis, Surrogate modeling, UD fiber composites, Uncertainty quantification
ASJC Scopus subject areas
- Materials Science(all)
- Ceramics and Composites
- Engineering(all)
- General Engineering
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In: Composites science and technology, Vol. 211, 108845, 28.07.2021.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Data-driven inverse uncertainty quantification in the transverse tensile response of carbon fiber reinforced composites
AU - Balokas, Georgios
AU - Kriegesmann, Benedikt
AU - Rolfes, Raimund
N1 - Funding Information: The provided financial support from the European Union's Horizon 2020 programmes FULLCOMP (GA No 642121 ) and SuCoHS (GA No 769178 ) is gratefully acknowledged by the authors.
PY - 2021/7/28
Y1 - 2021/7/28
N2 - Uncertainty quantification is critical for the full exploitation of composite materials’ potential. Inverse methods offer the possibility of indirectly characterizing the uncertainty of microscopic parameters by employing data sets from standard structural tests in higher scales. Two crucial requirements though, are the efficient modeling especially for the nonlinear prediction, and the measurement error availability from the tests which affects the updated scatter. This study employs effective stiffness and strength experimental data in order to quantify uncertainties of a carbon fiber UD composite in the microscale. A polynomial chaos surrogate model is trained from finite element simulations, able to efficiently predict the homogenized stiffness and strength for the uncertainty quantification procedure. The random parameters which are influential enough to be updated, are identified via a variance-based global sensitivity analysis. The inverse problem is solved with the Bayesian inference method, which updates any prior estimation of the probability models of the input parameters, based on output observations from the tests. Results show significant uncertainty reduction in comparison with typically used variance values in the literature and can be used to enrich the composite material databases. The proposed methodology is applied for the transverse tensile load case, although its non-intrusive nature allows applications for more load cases and various setups.
AB - Uncertainty quantification is critical for the full exploitation of composite materials’ potential. Inverse methods offer the possibility of indirectly characterizing the uncertainty of microscopic parameters by employing data sets from standard structural tests in higher scales. Two crucial requirements though, are the efficient modeling especially for the nonlinear prediction, and the measurement error availability from the tests which affects the updated scatter. This study employs effective stiffness and strength experimental data in order to quantify uncertainties of a carbon fiber UD composite in the microscale. A polynomial chaos surrogate model is trained from finite element simulations, able to efficiently predict the homogenized stiffness and strength for the uncertainty quantification procedure. The random parameters which are influential enough to be updated, are identified via a variance-based global sensitivity analysis. The inverse problem is solved with the Bayesian inference method, which updates any prior estimation of the probability models of the input parameters, based on output observations from the tests. Results show significant uncertainty reduction in comparison with typically used variance values in the literature and can be used to enrich the composite material databases. The proposed methodology is applied for the transverse tensile load case, although its non-intrusive nature allows applications for more load cases and various setups.
KW - Bayesian inference
KW - Polynomial chaos
KW - Sensitivity analysis
KW - Surrogate modeling
KW - UD fiber composites
KW - Uncertainty quantification
UR - http://www.scopus.com/inward/record.url?scp=85105556869&partnerID=8YFLogxK
U2 - 10.1016/j.compscitech.2021.108845
DO - 10.1016/j.compscitech.2021.108845
M3 - Article
AN - SCOPUS:85105556869
VL - 211
JO - Composites science and technology
JF - Composites science and technology
SN - 0266-3538
M1 - 108845
ER -