Extreme Mechanics Letters最新文献

The common belief that animals with larger heads are more tolerated to brain injury faces challenges under the extreme conditions of blast loading. Recent studies indicate that humans, who have notably larger heads than other species of similar body weight, exhibit a unique vulnerability. Integrating animal experimental data, advanced head modeling, and pressure propagation theories, this research elucidates the injury mechanisms across species as the blast wave transitions from the extremely hard skull to the extremely soft brain. We propose a new interspecies scaling law based on consistent peaks of intracranial pressure, rather than head size, to redefine the translation from animal exposure thresholds to human risk assessment. This shift in perspective underscores the imperative to comprehensively consider both head geometry and size in predicting tolerance to blast brain injury, moving beyond simplistic size-based comparisons. Our study's insights contribute significantly to redefining injury risk models and fostering innovative prevention strategies against blast-induced traumatic brain injury (bTBI).

Tailored reinforcement architectures in discontinuous metal matrix composites (DMMCs) offer superior mechanical performance with broad scientific and financial interests. This study presents a domain-knowledge enhanced machine learning approach to efficiently explore the design space of Al-SiC DMMCs for optimization. A substantial dataset containing 140,000 instances, resembling characteristic reinforcement configurations and variants, is generated using a series of algorithms. Employing high-throughput finite element analysis, the elastic properties of each configuration are estimated. Statistical analysis reveals that a more homogeneous distributed reinforcement contributes to mechanical stability, whereas configurations with extreme performance tend to have inhomogeneous reinforcement distribution. A deep residual neural network trained on this dataset accurately learns the structure-property correlations. Coupled with a genetic algorithm, the framework identifies optimal configurations across different volume fractions for maximizing/minimizing properties including tensile modulus, shear modulus, and Poisson's ratio. Comparative analysis shows the incorporation of domain knowledge improves data quality, facilitating more effective design space exploration. This study contributes to advancing composite materials design, particularly for next-generation high-performance DMMCs.

Two-dimensional materials, such as phosphorene, exhibit exceptional electrical and mechanical properties, offering promising prospects for both electronic and mechanical applications. To design more mechanically reliable devices using phosphorene, exploring its mechanical performance, especially impact resistance, is necessary. Here, coarse-grained molecular dynamics simulations are presented to study the mechanical responses of phosphorene under ballistic impact. Interestingly, size-dependent behaviors have been observed, which could be attributed to a coupling effect of cone wave reflection and membrane size. Owing to significant differences in Young’s modulus between the armchair and zigzag direction in phosphorene, mechanical wave propagation exhibits substantial anisotropy in a single-layer phosphorene membrane. A critical membrane size has been identified, below which cone wave reflections from the boundaries can induce perforation: a phenomenon particularly relevant to micro-ballistic testing of two-dimensional material membranes. The effect of boundary shape on reduction in ballistic limit has been studied, in which all the phosphorene sheets in the study are elliptical while the axial ratio of the ellipses is varied from 0.54 to 1.85. The axial ratio 0.69 is proven to maximize the strain amplification induced by cone wave reflection, thus leading to the biggest reduction in ballistic impact for phosphorene. A unitless indicator based on atomic Green-Lagrange strain has been proposed, which can effectively quantify the boundary shape effect on the reduced ballistic limit. Our findings provide timely guidance for the design of future nanodevices using phosphorene with high impact resistance.

It is well known that spiders have an extraordinary auditory sensitivity. However, significant differences in the acoustic impedance between air and solids (spiders) would reduce the acoustic energy transmitted from air to spiders, and by intuition this might result in a significant decrease in the acoustic sensitivity of spiders. This mechanism has been long troubled in researchers’ minds that how hunting spiders could have an outstanding auditory sensitivity. In this paper, the auditory sensing mechanisms of hunting spiders are studied by theoretical analysis and simulation. The results show that the acoustic impedance can be adjusted by spiders’ hairs with particular features to realize the acoustic impedance matching between air and spiders, which could make spiders’ hairs easily send signals to the nervous system of spiders, thus significantly promoting the acoustic energy transfer from air to spiders. Both the appropriate length and deflection angle of hairs are critical to determine the acoustic impedance/acoustic transmission coefficient. In parallel, verification test is carried out on an innovative bionic hair array. The experiment result shows that the acoustic impedance is significantly descended by the bionic hair array with the spiders' acoustic hairs' features, which provides a sufficient proof of the acoustic impedance matching by spiders' hairs. Consequently, this work clearly discloses the acoustic sensing mechanism for the extraordinary auditory sensitivity of hunting spiders, which may have a great significance for the development of artificial auditory technology and sound stealth devices.

Predicting the behavior of nanomaterials under various conditions presents a significant challenge due to their complex microstructures. While high-fidelity modeling techniques, such as molecular dynamics (MD) simulations, are effective, they are also computationally demanding. Machine learning (ML) models have opened new avenues for the rapid exploration of design spaces. In this work, we developed a deep learning framework based on a conditional generative adversarial network (cGAN) to predict the evolution of grain boundary (GB) networks in nanocrystalline materials under mechanical loads, incorporating both morphological and atomic details. We conducted MD simulations on nanocrystalline tungsten and used the resulting ground-truth data to train our cGAN model. We assessed the performance of our cGAN model by comparing it to a Convolutional Autoencoder (ConvAE) model and examining the impact of changes in geometric morphology and loading conditions on the model's performance. Our cGAN model demonstrated high accuracy in predicting GB network evolution under a variety of conditions. This developed framework shows potential for predicting various materials' behaviors across a wide range of nanomaterials.

A data-driven computational framework is established to implement surrogate constitutive models for porous elastomers undergoing large deformation. Explicit finite element (FE) simulations are conducted to compute the homogenised response of a cubic unit cell of a porous compressible elastomer, subject to a random set of imposed multiaxial strain states. The FE predictions are used to assemble a training dataset for two different surrogate models, based on simple neural networks. The first establishes a non-linear correspondence between six-dimensional strain and stress vectors; the second provides a strain energy potential from which to derive the stress versus strain response. The accuracy of the surrogate models is quantified, and their predictions are compared to those of the Hyperfoam model; it is found that the surrogate models can significantly outperform this well-known phenomenological model.

Accurate calibration of constitutive models is vital for predicting the mechanical behavior of engineering materials under various loading conditions. Traditionally, the calibration process involves a series of experiments on specimens with simple geometries to capture the complexities in the constitutive models. Each single test conveys a small amount of information that a well-trained human brain can handle, resulting in a large number of experiments needed for a complete calibration. Therefore, traditional calibration approaches are usually costly and time-consuming. With recent advancements in computational techniques, there is an emerging opportunity to leverage geometrically complex specimens in experiments to obtain a larger amount of information for computers to learn and calibrate the model. Despite some initial success, the most important question remains unsettled: How much information does a mechanical test convey? In this work, we answer this question by incorporating information entropy as a quantitative measure in the design of mechanical test specimens. We demonstrate the viability of the proposed approach by comparing the performance of selected test specimens for learning the plasticity model of sheet metal, e.g., the Hill48 anisotropic elastic-plastic model in this case. An optimal entropy criterion is proposed for selecting the appropriate heterogeneous test specimen for inverse calibration, depending on the cardinality of the stress state space considered in the model. Finally, Bayesian optimization is applied to uniaxial and biaxial tension specimens, using the stress state entropy as an objective function, to investigate the general principles of designing specimens with maximum information for learning constitutive models.

In this paper, based on the original Gray–Scott model, we propose a modified Gray–Scott model by introducing a target term into the reaction–diffusion equations. We apply this modified model in the context of shape transformation problems. To expedite the process from the source shape to the target shape, we utilize the explicit Euler method to solve our proposed modified Gray–Scott model, making our approach simpler and more efficient. To validate the feasibility of our method, we conduct simulation experiments in both two-dimensional (2D) and three-dimensional (3D) spaces. By progressing through experiments of increasing complexity, we demonstrate the natural effectiveness of our simulation method as a viable approach for shape transformation. To demonstrate the efficiency of the method, we provide the runtime consumed by the simulated shape transformation experiment. Additionally, to assess the correspondence between the ground truth values of the target shape and the simulated results, we calculate the corresponding area change rate and volume change rate in 2D and 3D spaces to prove that our proposed method can effectively transform into the target shape.

The performance and evolution characteristics of the friction interface are crucial for the design optimization of material friction and wear, as well as the revelation of the mechanism of seismic sliding motion. To understand the friction behavior of rough surfaces, it is essential to understand the physical process of interaction among asperities. However, visualizing the contact of asperities is challenging because materials are typically opaque, and the dimensions of asperities are usually in the micron range. This study developed an in-situ scanning electron microscope friction device and a micro speckle fabrication method to measure the strain field of regular asperities from the side, and synchronously measure the macroscopic friction coefficient. In-situ friction experiments were conducted on brass and silicon materials. The results indicate that directly correlating the friction coefficient with the deformation and failure images of asperities can effectively explain the evolution process and differences in friction coefficient for the two materials, validating the performance of the device. Different failure modes of asperities were observed, including severe plastic deformation in brass asperities and fracture-producing large particles in silicon asperities. The critical transition of asperity failure modes in the experiments was analyzed based on a theoretical model. The phase diagram of asperity failure modes and friction coefficient evolution was plotted, providing potential explanation for the evolution of friction coefficient in friction experiments on randomly rough surfaces. The developed device in this study can be used for non-transparent materials and helps reveal the microscopic mechanisms behind experimental phenomena.