Details
| Original language | English |
|---|---|
| Title of host publication | Advances in Predictive Models and Methodologies for Numerically Efficient Linear and Nonlinear Analysis of Composites |
| Editors | Marco Petrolo |
| Publisher | Springer Nature |
| Pages | 179-193 |
| Number of pages | 15 |
| Edition | 1. |
| ISBN (electronic) | 978-3-030-11969-0 |
| ISBN (print) | 978-3-030-11968-3 |
| Publication status | Published - 25 Feb 2019 |
Publication series
| Name | PoliTO Springer Series |
|---|---|
| ISSN (Print) | 2509-6796 |
| ISSN (electronic) | 2509-7024 |
Abstract
This chapter presents an uncertainty quantification framework for triaxially braided composites simulation, dealing with the stochastic stiffness prediction via numerical multiscale analysis. Efficiency is achieved by using various metamodeling techniques, such as neural networks, polynomial chaos expansion and Kriging modeling. Uncertainties accounting for material and geometric randomness are propagating through the scales to the final scatter of the mechanical properties of the macroscale. Information about the stochastic input and the dominating uncertain parameters is offered via application of a variance-based global sensitivity analysis. All methods employed in this work are non-intrusive, hence the framework can be used for all sorts of composite materials and numerical models. The need for realistic uncertainty quantification is highlighted.
ASJC Scopus subject areas
- Engineering(all)
- General Engineering
- Chemistry(all)
- General Chemistry
- Mathematics(all)
- General Mathematics
- Computer Science(all)
- General Computer Science
- Physics and Astronomy(all)
- General Physics and Astronomy
Cite this
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Advances in Predictive Models and Methodologies for Numerically Efficient Linear and Nonlinear Analysis of Composites. ed. / Marco Petrolo. 1. ed. Springer Nature, 2019. p. 179-193 (PoliTO Springer Series).
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review
}
TY - CHAP
T1 - Metamodel-Based Uncertainty Quantification for the Mechanical Behavior of Braided Composites
AU - Balokas, G.
AU - Kriegesmann, Benedikt
AU - Czichon, Steffen
AU - Böttcher, A.
AU - Rolfes, Raimund
PY - 2019/2/25
Y1 - 2019/2/25
N2 - This chapter presents an uncertainty quantification framework for triaxially braided composites simulation, dealing with the stochastic stiffness prediction via numerical multiscale analysis. Efficiency is achieved by using various metamodeling techniques, such as neural networks, polynomial chaos expansion and Kriging modeling. Uncertainties accounting for material and geometric randomness are propagating through the scales to the final scatter of the mechanical properties of the macroscale. Information about the stochastic input and the dominating uncertain parameters is offered via application of a variance-based global sensitivity analysis. All methods employed in this work are non-intrusive, hence the framework can be used for all sorts of composite materials and numerical models. The need for realistic uncertainty quantification is highlighted.
AB - This chapter presents an uncertainty quantification framework for triaxially braided composites simulation, dealing with the stochastic stiffness prediction via numerical multiscale analysis. Efficiency is achieved by using various metamodeling techniques, such as neural networks, polynomial chaos expansion and Kriging modeling. Uncertainties accounting for material and geometric randomness are propagating through the scales to the final scatter of the mechanical properties of the macroscale. Information about the stochastic input and the dominating uncertain parameters is offered via application of a variance-based global sensitivity analysis. All methods employed in this work are non-intrusive, hence the framework can be used for all sorts of composite materials and numerical models. The need for realistic uncertainty quantification is highlighted.
UR - http://www.scopus.com/inward/record.url?scp=85083964992&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-11969-0_11
DO - 10.1007/978-3-030-11969-0_11
M3 - Contribution to book/anthology
AN - SCOPUS:85083964992
SN - 978-3-030-11968-3
T3 - PoliTO Springer Series
SP - 179
EP - 193
BT - Advances in Predictive Models and Methodologies for Numerically Efficient Linear and Nonlinear Analysis of Composites
A2 - Petrolo, Marco
PB - Springer Nature
ER -