Physics-guided machine learning for the failure prediction of a novel Z-beam composite wing structural concept
File(s)
Published version
Author(s)
Li, Runze
Miranda, Mário
Iorga, Lucian
Pinho, Silvestre T
Type
Journal Article
Abstract
To enhance the structural and aerodynamic performance of next-generation civil aircraft, novel composite wing concepts are being explored for aeroelastic tailoring and weight reduction. However, conventional analytical and empirical tools struggle to evaluate these complex configurations at early design stages, limiting the exploration of their full potential. This study develops physics-guided machine learning models for the prediction of mechanical response and failure behaviour of innovative Z-beam-based composite wing structures. The Z-beam concept provides increased geometric freedom, enabling tailored bending–torsion coupling. A dataset of representative Z-beam elements is generated using finite element simulations with periodic boundary conditions. Local stress concentrations are analysed using refined sub-models with LaRC05 failure criteria to compute detailed failure indices. A physics-guided learning framework is proposed to predict failure indices, dominant failure modes, and critical locations within the wing box. The framework embeds the linearity of elastic response and the monotonic relation between strain and failure indices to enhance data efficiency and prediction robustness. A second model is trained to estimate bending, torsional, and coupling stiffness properties. Results show that the proposed models deliver high predictive accuracy, providing an effective tool for early-stage design exploration and optimisation of composite wing structures.
Date Issued
2026-08-01
Date Acceptance
2026-07-12
Citation
Composite Structures, 2026, 394
ISSN
0263-8223
Publisher
Elsevier BV
Journal / Book Title
Composite Structures
Volume
394
Copyright Statement
© 2026 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Publication Status
Published
Article Number
120662
Date Publish Online
2026-07-21