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 | 143-158 |
| Number of pages | 16 |
| 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 introduces an approach to systematically analyze stochastic distributions of spatially varying material properties in structures. The approach gives insight into how spatial variations of material properties affect the mechanical response of a structure. If sufficient knowledge of the production processes is available, this allows designers to analyze the probability that a certain design criterion (e.g. a certain buckling load level) is met. Stochastic structural analyses can be used to analyze how variations are correlated to a structural measure. This gives information on the sensitivity of the structure with respect to variations. In the present work, this is used to improve the structural performance by distributing a material pattern according to a pattern based on the sensitivity topology. This approach is illustrated by redistributing the material properties of an axially loaded panel on the basis of the correlation of the spatially varying Young’s modulus with the linear buckling load of the panel.
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. 143-158 (PoliTO Springer Series).
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review
}
TY - CHAP
T1 - Improving the Static Structural Performance of Panels with Spatially Varying Material Properties Using Correlations
AU - van den Broek, Sander Friso
AU - Minera, Sergio
AU - Jansen, Eelco Luc
AU - Pirrera, Alberto
AU - Weaver, Paul M.
AU - Rolfes, Raimund
N1 - Funding information: This work was carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol—http://www.bris.ac.uk/acrc/ as well as computational facilities within the Institute of Structural Analysis at Leibniz University Hannover.
PY - 2019/2/25
Y1 - 2019/2/25
N2 - This chapter introduces an approach to systematically analyze stochastic distributions of spatially varying material properties in structures. The approach gives insight into how spatial variations of material properties affect the mechanical response of a structure. If sufficient knowledge of the production processes is available, this allows designers to analyze the probability that a certain design criterion (e.g. a certain buckling load level) is met. Stochastic structural analyses can be used to analyze how variations are correlated to a structural measure. This gives information on the sensitivity of the structure with respect to variations. In the present work, this is used to improve the structural performance by distributing a material pattern according to a pattern based on the sensitivity topology. This approach is illustrated by redistributing the material properties of an axially loaded panel on the basis of the correlation of the spatially varying Young’s modulus with the linear buckling load of the panel.
AB - This chapter introduces an approach to systematically analyze stochastic distributions of spatially varying material properties in structures. The approach gives insight into how spatial variations of material properties affect the mechanical response of a structure. If sufficient knowledge of the production processes is available, this allows designers to analyze the probability that a certain design criterion (e.g. a certain buckling load level) is met. Stochastic structural analyses can be used to analyze how variations are correlated to a structural measure. This gives information on the sensitivity of the structure with respect to variations. In the present work, this is used to improve the structural performance by distributing a material pattern according to a pattern based on the sensitivity topology. This approach is illustrated by redistributing the material properties of an axially loaded panel on the basis of the correlation of the spatially varying Young’s modulus with the linear buckling load of the panel.
UR - http://www.scopus.com/inward/record.url?scp=85083958886&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-11969-0_9
DO - 10.1007/978-3-030-11969-0_9
M3 - Contribution to book/anthology
AN - SCOPUS:85083958886
SN - 978-3-030-11968-3
T3 - PoliTO Springer Series
SP - 143
EP - 158
BT - Advances in Predictive Models and Methodologies for Numerically Efficient Linear and Nonlinear Analysis of Composites
A2 - Petrolo, Marco
PB - Springer Nature
ER -