It is important to accurately model materials' properties at lower length scales (micro-level) while translating the effects to the components and/or system level (macro-level) can significantly reduce the amount of experimentation required to develop new technologies. Robustness analysis of fuel and structural performance for harsh environments (such as power uprated reactor systems or aerospace applications) using machine learning-based multi-scale modeling and robust optimization under uncertainties are required. The fiber and matrix material characteristics are potential sources of uncertainty at the microscale. The stacking sequence (angles of stacking and thickness of layers) of composite layers causes meso-scale uncertainties. It is also possible for macro-scale uncertainties to arise from system properties, like the load or the initial conditions. This chapter demonstrates advanced data-driven methods and outlines the specific capability that must be developed/added for the multi-scale modeling of advanced composite materials. This chapter proposes a multi-scale modeling method for composite structures based on a finite element method (FEM) simulation driven by surrogate models/emulators based on microstructurally informed meso-scale materials models to study the impact of operational parameters/uncertainties using machine learning approaches. To ensure optimal composite materials, composite properties are optimized with respect to initial materials volume fraction using data-driven numerical algorithms.
翻译:必须精确地以较低长度尺度(微观级)模拟材料的特性,同时将影响转化为组件和/或系统级(宏观级),从而可以大大减少开发新技术所需的实验量; 利用机械学习的多尺度模型和在不确定情况下进行强力优化,对严酷环境(例如动力升级反应堆系统或航空航天应用)的燃料和结构性能进行强力分析; 纤维和矩阵材料特性是微尺度不确定性的潜在来源; 复合层的叠叠叠序列(堆叠和厚层的交织)造成中间尺度的不确定性; 也有可能从系统特性(如负荷或初始条件)中产生宏观级不确定性; 本章展示先进的数据驱动方法,并概述为在高级复合材料的多尺度模型制作而必须开发/增加的具体能力; 本章提议一种基于有限元素方法(FEM)的复合结构模拟模型/模拟方法,由微结构知情的中间尺度材料模型,以研究操作参数/模型的影响; 使用最优化的复合参数/模型分析方法,确保使用最优化的复合材料的压压压分析质量; 使用最优化的复合材料,确保采用最优化的复合的模型的压压压压压的压。