Simulating the scatter of a polycrystalline in synchrotron diffraction using crystal plasticity simulation

在过去的十年中，应用高能同步加速器衍射来表征工业多晶金属材料的行为得到了极大的关注。至关重要的是，模拟安全关键行业（如核工作）中材料的苛刻环境的现场实验，有助于支撑其本构定律。然而，由于x射线穿透深度的限制，这样的实验通常只能检查一小部分材料。在这个项目中，多晶材料的真正代表将使用微观结构的晶体塑性模型来开发。该模型将使用以前和将来的实验进行校准。传统的晶体塑性有限元模型运行速度较慢。该项目将利用谱法代替所需的数值计算，这将比传统方法快几个数量级。利用光谱方法的这一优势，将对微观结构变化对大尺度材料响应边界的影响进行统计分析。虽然这项工作的主要目标是量化材料在停顿时的行为可变性，但也将研究单个颗粒作为其环境函数的响应。我们将利用机器学习对模拟结果的应用来建立晶体塑性的代理模型，以加快分析过程。

Application of high energy synchrotron diffraction to characterise the behaviour of industrial polycrystalline metallic materials has gained significant attention in the past decade. Critically, the in-situ experiments simulating the demanding environments under which the materials in safety critical industries such as nuclear work, have been instrumental in underpinning their constitutive laws. However, such experiments often examine only a small volume of materials due to the restrictions associated with X-ray depth penetration.In this project a true representative of polycrystalline material will be developed using a microstructurally informed crystal plasticity model. The model will be calibrated using previous and future experiments. The crystal plasticity model traditionally used within a finite element framework are slow to run. This project will take advantage of spectral method instead to perform numerical calculations required which will be orders of magnitude faster than the traditional methods. This advantage of spectral methods will be exploited to carry out statistical analysis on the effects of microstructure variability on the bounds of large-scale material response.While the main objective of the work is to quantify the material behaviour variability at balk, the response of single grains as a function of their environment will be also investigated. We will take advantage of Machine Learning applied on the results of the simulating to build surrogate models of the crystal plasticity to accelerate the analysis process.