Journal
MATERIALS
Volume 14, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/ma14112706
Keywords
sensitivity; multiscale; stochastic fields; Polynomial Chaos; layer-wise models; Unified Formulation
Categories
Funding
- European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme [850437]
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This paper investigates the buckling response of Variable Stiffness Composite plates subjected to spatially varying factors, utilizing Carrera Unified Formulation and layer-wise modeling, as well as conducting multiscale sensitivity analysis. The study shows that the multiscale sensitivity approach can be accelerated with the use of sampling techniques and surrogate models like Polynomial Chaos Expansion.
It is well known that fabrication processes inevitably lead to defects in the manufactured components. However, thanks to the new capabilities of the manufacturing procedures that have emerged during the last decades, the number of imperfections has diminished while numerical models can describe the ground truth designs. Even so, a variety of defects has not been studied yet, let alone the coupling among them. This paper aims to characterise the buckling response of Variable Stiffness Composite (VSC) plates subjected to spatially varying fibre volume content as well as fibre misalignments, yielding a multiscale sensitivity analysis. On the one hand, VSCs have been modelled by means of the Carrera Unified Formulation (CUF) and a layer-wise (LW) approach, with which independent stochastic fields can be assigned to each composite layer. On the other hand, microscale analysis has been performed by employing CUF-based Mechanics of Structure Genome (MSG), which was used to build surrogate models that relate the fibre volume fraction and the material elastic properties. Then, stochastic buckling analyses were carried out following a multiscale Monte Carlo analysis to characterise the buckling load distributions statistically. Eventually, it was demonstrated that this multiscale sensitivity approach can be accelerated by an adequate usage of sampling techniques and surrogate models such as Polynomial Chaos Expansion (PCE). Finally, it has been shown that sensitivity is greatly affected by nominal fibre orientation and the multiscale uncertainty features.
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