Accurate prediction of discontinuous crack paths in random porous media via a generative deep learning model.

ABSTRACT: Pore structures provide extra freedoms for the design of porous media, leading to desirable properties, such as high catalytic rate, energy storage efficiency, and specific strength. This unfortunately makes the porous media susceptible to failure. Deep understanding of the failure mechanism in microstructures is a key to customizing high-performance crack-resistant porous media. However, solving the fracture problem of the porous materials is computationally intractable due to the highly complicated configurations of microstructures. To bridge the structural configurations and fracture responses of random porous media, a unique generative deep learning model is developed. A two-step strategy is proposed to deconstruct the fracture process, which sequentially corresponds to elastic deforma

SUBMITTER: He Y 

PROVIDER: S-EPMC11459186 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature