报告题目：An application of Reduced-Order Model for polycrystalline plasticity
报告时间：2023年10月23日 星期一 10:30-12:00
报告摘要：In the realm of data-driven simulations, as the amount of multi-scale experimental data grows, new modeling methods become imperative. Beyond the conventional mechanical analysis on a large scale, various techniques such as nano-indentation, Transmission Electron Microscopy (TEM), and Electron Backscatter Diffraction (EBSD) are used to explore how polycrystalline textures affect the elastic and plastic behavior of metals. Nevertheless, due to challenges like computational cost, integrating all this data into finite element simulations remains a huge challenge. This work stands in the field of data-driven simulations using a manifold learning approach, which shows promising results. Within this context, we introduce an innovative use of Proper Orthogonal Decomposition (POD) to reduce the dimensionality of texture information. Since material textures illustrate the orientation of all the crystals in a given specimen using Euler angle, it is crucial to take into account the periodicity of data. The considered method for handling this particular type of data is presented through an academic case study.
经费资助信息：2023.1-2026.12，国家自然科学基金面上项目‘大规模结构动力学拓扑优化的动态多保真缩减理论与方法’，（Grant No. 12272302）