A machine learning assisted multifidelity modelling methodology to predict 3D stresses in the vicinity of design features in composite structures
File(s)
Published version
Author(s)
Imran Azeem, Omar Ahmed
Pinho, Silvestre Taveira
Type
Journal Article
Abstract
Multifidelity global–local finite element (FE) analyses are typically used to predict damage initiation hotspots around repetitive design features in large composite structures, such as composite airframes. We propose the use of machine learning (ML) methods to accelerate these analyses. We demonstrate this ML assisted framework for the stress analysis of a hole in plate feature in an aerospace C-spar structure. To enable this framework, we develop the following original features: a computationally efficient sampling scheme; a work-equivalent boundary condition homogenisation scheme; a volume averaged ply-by-ply stress approach; and a sequential long-short term memory neural network reformulated from a time basis to a stacking sequence basis with further bi-directionality customisation. Overall, we show that the developed method results in high-accuracy prediction of 3D stresses, with over two orders of magnitude reduction in modelling and simulation time compared to FE analyses.
Date Issued
2024-09-01
Date Acceptance
2024-06-23
Citation
International Journal of Solids and Structures, 2024, 301
ISSN
0020-7683
Publisher
Elsevier
Journal / Book Title
International Journal of Solids and Structures
Volume
301
Copyright Statement
© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
https://www.sciencedirect.com/science/article/pii/S0020768324003056
Publication Status
Published
Article Number
112946
Date Publish Online
2024-06-24