In the field of bionic soft robots and microrobots, artificial muscle materials have exhibited unique potential for cutting-edge applications. However, current mainstream thermal-responsive artificial muscles based on semicrystalline polymers (SCPs), despite their excellent physical properties, suffer from the limitation of environmental stimuli in practice, while their photodriven counterparts adopting liquid crystal elastomers (LCEs) lack ductility. Herein, a novel multifunctional programmable artificial muscle with a unique patch-sewing structure formed by π−π stacking between azobenzene groups was designed, which combined the advantages of SCPs and LCEs. The nanocomposite demonstrated a unique combination between artificial muscle performance (46.5 times the energy density and 26.6 times the power density of human skeletal muscles) and programmability (274.84% strain and 100% shape-memory recovery rate within 1 s). Meanwhile, coupling the photoisomerization of azobenzene and the photothermal conversion of gold nanorods, the cycle of deformation triggered by ultraviolet light and restoring by infrared light could be accomplished rapidly within 30 s. A COMSOL Multiphysics model was established and the corresponding finite element analysis verified the photoactuation and captured the general principle of light initiation in elastomers. These demonstrate that the multifunctional programmable elastomer is promising for artificial muscle applications, especially for photoinduced actuation.
The multiscale simulation of heterogeneous materials is a popular and important subject in solid mechanics and materials science due to the wide application of composite materials. However, the classical FE 2 (finite element 2) scheme can be costly, especially when the microproblem is nonlinear. In this paper, we consider the case when the microproblem is the phase field formulation for fracture. We adopt the locally linear embedding (LLE) manifold learning approach, a method for non-linear dimension reduction, to extract the manifold that contains a collection of phase-field-represented initial microcrack patterns in the representative volume element (RVE). Then the output data corresponding to any other microcrack pattern, e.g., the evolved phase field at a fixed load, can be accurately reconstructed using the learned manifold with minimum computation. The method has two features: a minimum number of parameters for the scheme, and an input-specific error bar. The latter feature enables an adaptive strategy for any new input on whether to use the proposed, less expensive reconstruction, or to use an accurate but costly high-fidelity computation instead.
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