International Journal of Applied Mechanics最新文献

Machine learning has sparked significant interest in the realm of designing mechanical metamaterials. These metamaterials derive their unique properties from microstructures rather than the constituent materials themselves. In this context, we introduce a novel data-driven approach for the design of an orthotropic cellular metamaterials with specific target properties. Our methodology leverages a Bézier curve framework with strategically placed control points. A machine learning model harnesses the positions of these control points to achieve the desired material properties. This process consists of two main steps. Initially, we establish a forward model capable of predicting material properties based on given designs. Then, we construct an inverse model that takes material properties as inputs and produces corresponding design parameters as outputs. Our results demonstrate that the dataset generated using the Bézier curve-based strategy shows a wide range of elastic distributions. Describing the geometry in terms of design parameters, rather than pixel-based figures, enhances the training efficiency of the networks. The dual-network training approach helps avoid contradictions where specific elastic properties may correspond to various geometric designs. We verify the prediction accuracy of the inverse model concerning elastic properties and relative density. The presented approach holds promise for accelerating the design of cellular metamaterials with desired properties.

The application of the discrete element method (DEM) to continuous medium problems is becoming increasingly widespread. In this work, a parallel computing method with multiple time steps based on overlapping particles is proposed. The domain decomposition method (DDM) with overlapping particles method is used to increase the speed up and shorten the computation time and meet the consistency requirements during data transmission. The multi-time-step method (MTSM) is adopted to tackle the matching of asynchronous step boundary. Data are exchanged at the boundaries in each subdomain with a message passing interface (MPI). The computational efficiency of different step ratios in both serial and parallel computing is studied respectively. Numerical examples show that the DEM can effectively handle large structure deformation problems, and provides a shorter calculation time than that of the finite element method (FEM). The DEM with multiple time steps in different subdomains effectively reduces the computation time than that with a single time step in the entire domain. Under fixed step ratio conditions, using parallel computing can save more time than serial computing. This work develops ideas for expanding the application of DEM for large engineering problems.

Contact between solids is a ubiquitous phenomenon in engineering and an enduring topic in tribology. However, material yield plateau and strain hardening are common in ductile metals but rarely considered in contact mechanics. This work develops a three-phase constitutive model that accurately describes the elastic and plastic behaviors considering both yield plateau and strain hardening, and then constructs a finite element model for the contact of a rigid flat and a corresponding elastoplastic hemisphere. The Taguchi method is employed to conduct numerical simulations of material parameters for finding generalized empirical formulations of dimensionless contact load and area versus dimensionless contact interference in the range of ${\omega }^{\ast }\le 120$. The presented empirical formulations demonstrate good accuracy verified with KE, JG, and Ghaednia’s models. This work fills the gap that the yield plateau has not ever been explored in contact mechanics and provides a basic model for describing the contact behavior of engineering rough surfaces for ductile metal.

Seismic inversion is an important part of the modern geological exploration process. Novel applications of deep learning are capable of handling heterogeneous media, but require too much data for training. In this paper, we focus on the prediction of fracture inclusion location and its parameters in rock media and approach the problem in the multi-task manner. For this, several multi-task convolutional neural network (CNN) architectures are proposed and compared. The direct seismic problem is considered in the heterogeneous fractured geological model based on the well-known Marmousi2 model in a two-dimensional case. The model of the deformable solid medium containing slip planes with nonlinear slip conditions on them and explicit–implicit numerical method is applied to obtain the synthetic seismic dataset for CNN training and validation.