RESUMEN

The extraordinary quasi-static mechanical properties of nacre-like composite metamaterials, such as high specific strength, stiffness, and toughness, are due to the periodic arrangement of two distinct phases in a "brick and mortar" structure. It is also theorized that the hierarchical periodic structure of nacre structures can provide wider band gaps at different frequency scales. However, the function of hierarchy in the dynamic behavior of metamaterials is largely unknown, and most current investigations are focused on a single objective and specialized applications. Nature, on the other hand, appears to develop systems that represent a trade-off between multiple objectives, such as stiffness, fatigue resistance, and wave attenuation. Given the wide range of design options available to these systems, a multidisciplinary strategy combining diverse objectives may be a useful opportunity provided by bioinspired artificial systems. This paper describes a class of hierarchically-architected block lattice metamaterials with simultaneous wave filtering and enhanced mechanical properties, using deep learning based on artificial neural networks (ANN), to overcome the shortcomings of traditional design methods for forward prediction, parameter design, and topology design of block lattice metamaterial. Our approach uses ANN to efficiently describe the complicated interactions between nacre geometry and its attributes, and then use the Bayesian optimization technique to determine the optimal geometry constants that match the given fitness requirements. We numerically demonstrate that complete band gaps, that is attributed to the coupling effects of local resonances and Bragg scattering, exist. The coupling effects are naturally influenced by the topological arrangements of the continuous structures and the mechanical characteristics of the component phases. We also demonstrate how we can tune the frequency of the complete band gap by modifying the geometrical configurations and volume fraction distribution of the metamaterials. This research contributes to the development of mechanically robust block lattice metamaterials and lenses capable of controlling acoustic and elastic waves in hostile settings.

Asunto(s)

Nácar , Nácar/química , Teorema de Bayes , Sonido , Acústica

RESUMEN

Accurate and efficient numerical simulation of highly nonlinear ultrasound propagation is essential for a wide range of therapeutic and physical ultrasound applications. However, due to large domain sizes and the generation of higher harmonics, such simulations are computationally challenging, particularly in 3-D problems with shock waves. Current numerical methods are based on computationally inefficient uniform meshes that resolve the highest harmonics across the entire spatial domain. To address this challenge, we present an adaptive numerical algorithm for computationally efficient nonlinear acoustic holography. At each propagation step, the algorithm monitors the harmonic content of the acoustic signal and adjusts its discretization parameters accordingly. This enables efficient local resolution of higher harmonics in areas of high nonlinearity while avoiding unnecessary resolution elsewhere. Furthermore, the algorithm actively adapts to the signal's nonlinearity level, eliminating the need for prior reference simulations or information about the spatial distribution of the harmonic content of the acoustic field. The proposed algorithm incorporates an upsampling process in the frequency domain to accommodate the generation of higher harmonics in forward propagation and a downsampling process when higher harmonics are decimated in backward propagation. The efficiency of the algorithm was evaluated for highly nonlinear 3-D problems, demonstrating a significant reduction in computational cost with a nearly 50-fold speedup over a uniform mesh implementation. Our findings enable a more rapid and efficient approach to modeling nonlinear high-intensity focused ultrasound (HIFU) wave propagation.

RESUMEN

Fibrous shape memory polymers (SMPs) have received growing interest in various applications, especially in biomedical applications, which offer new structures at the microscopic level and the potential of enhanced shape deformation of SMPs. In this paper, we report on the development and investigation of the properties of acrylate-based shape memory polymer fibers, fabricated by electrospinning technology with the addition of polystyrene (PS). Fibers with different diameters are manufactured using four different PS solution concentrations (25, 30, 35, and 40 wt%) and three flow rates (1.0, 2.5, and 5.0 µL min-1) with a 25 kV applied voltage and 17 cm electrospinning distance. Scanning electron microscope (SEM) images reveal that the average fiber diameter varies with polymer concentration and flow rates, ranging from 0.655 ± 0.376 to 4.975 ± 1.634 µm. Dynamic mechanical analysis (DMA) and stress-strain testing present that the glass transition temperature and tensile values are affected by fiber diameter distribution. The cyclic bending test directly proves that the electrospun SMP fiber webs are able to fully recover; additionally, the recovery speed is also affected by fiber diameter. With the combination of the SMP material and electrospinning technology, this work paves the way in designing and optimizing future SMP fibers properties by adjusting the fiber diameter.

RESUMEN

Experiments show that high-intensity focused ultrasound (HIFU) is a promising stimulus with multiple superior and unique capabilities to induce localized heating and achieve temporal and spatial thermal effects in the polymers, noninvasively. When polymers are subjected to HIFU, they heat up differently compared to the case they are subjected to heat sources directly; however, the origins of this difference are still entirely unknown. We hypothesize that the difference in the macroscale response of polymers subjected to HIFU strongly depends on the polymer chains, composition, and structure, i.e. being crystalline or amorphous. In this work, this hypothesis is investigated by molecular dynamics studies at the atomistic level and verified by experiments at the macroscopic scale. The results show that the viscoelasticity, measured by stress-strain phase lag, the reptation motion of the chains, and the vibration-induced local mobility quantified by the root mean square fluctuation contribute to the observed difference in the HIFU-induced thermal effects. This unravels the unknown mechanisms behind stimulating the polymers by HIFU, and paves the way in front of using this method in future applications.

RESUMEN

In recent years, shape-memory polymers (SMPs) have received extensive attention to be used as actuators in a broad range of applications such as medical and robotic devices. Their ability to recover large deformations and their capability to be stimulated remotely have made SMPs a superior choice among different smart materials in various applications. In this study, a ductile SMP composite with enhanced shape recovery ability is synthesized and characterized. This SMP composite is made by a mixture of acrylate-based crosslinkers and monomers, as well as polystyrene (PS) with UV curing. The composite can achieve almost 100% shape recovery in 2 s by hot water or hot air. This shape recovery speed is much faster than typical acrylate-based SMPs. In addition, the composite shows excellent ductility and viscoelasticity with reduced hardness. Molecular dynamics (MD) simulations are performed for understanding the curing mechanism of this composite. With the combination of the experimental and computational works, this study paves the way in front of designing and optimizing the future SMP devices.