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Hygromorph double–double flax/epoxy laminates: curvature authority, diffusion anisotropy and homogenization

Abdulla Alhembar*, Fabrizio Scarpa, Charles M Y De Kergariou, Imad Barsoum

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

Hygromorph biocomposites based on natural fibers exhibit moisture induced strains that can be exploited for passive actuation, but at the cost of degraded mechanical properties over time. Existing studies focused on conventional quad laminates, even though such layups impose design restrictions. Double-double (DD) laminates, constructed from repeated four ply blocks offer a much flexible design space with only two fiber angles and a repetition parameter. However, their hygromorph behavior has not been explored. This work investigates the curvature response of DD laminates made from unidirectional flax/epoxy laminates. Classical Laminate Theory (CLT) is used to map bending and twist authorities over the full design space using an equivalent bending energy measure and a twist ratio. Steady state in plane moisture diffusivities are derived analytically, together with an anisotropy index that reveals combinations of certain fiber angles maximize bending and maintain near isotropic diffusion. A transient through thickness diffusion model is developed to predict time dependent curvature histories. Curvature based homogenization measures are also introduced to quantify convergence of bending magnitude and twist ratio with the number of repeats. Laminate-level analysis show that selective DD layups deliver higher bending authority and greater in plane shear stiffness than selected quad baselines.
Original languageEnglish
Article number120521
JournalComposite Structures
Early online date1 Jun 2026
DOIs
Publication statusE-pub ahead of print - 1 Jun 2026

Bibliographical note

The authors gratefully acknowledge financial support from Khalifa University through its faculty-track postdoctoral program for A. Alhembar. C.d.K. and F.S. acknowledge funding from the European Research Council under the ERC-2020 Advanced Grant (Grant No. 101020715, NEUROMETA project). C.d.K. further acknowledges support from the EPSRC Doctoral Prize Fellowship (Grant No. EP/W524414/1).

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