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2019 | OriginalPaper | Chapter

9. Improving the Static Structural Performance of Panels with Spatially Varying Material Properties Using Correlations

Authors : S. van den Broek, S. Minera, E. Jansen, A. Pirrera, P. M. Weaver, R. Rolfes

Published in: Advances in Predictive Models and Methodologies for Numerically Efficient Linear and Nonlinear Analysis of Composites

Publisher: Springer International Publishing

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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.

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previous chapter Structural Health Monitoring: Numerical Simulation of Lamb Waves Via Higher-Order Models
next chapter Multiscale Identification of Material Properties for Anisotropic Media: A General Inverse Approach
Footnotes
1
These are classified as discrete approaches as they are evaluated at discrete points, and do not result in a continuous function.
 
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Metadata
Title
Improving the Static Structural Performance of Panels with Spatially Varying Material Properties Using Correlations
Authors
S. van den Broek
S. Minera
E. Jansen
A. Pirrera
P. M. Weaver
R. Rolfes
Copyright Year
2019
Book
Advances in Predictive Models and Methodologies for Numerically Efficient Linear and Nonlinear Analysis of Composites

Print ISBN: 978-3-030-11968-3

Electronic ISBN: 978-3-030-11969-0

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-11969-0_9

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