GOALI: Random Fiber Structures: Material Characterization & Adaptive Damage Response

该项目将开发一种基于微观结构信息的自适应、尺度不变的材料模型，该模型将有效地解释和预测随机纤维聚合物复合材料(RAFC)固有的材料和力学行为。该框架独一无二地结合了代表性体积元素(RVE)和独立状态变量(ISV)。该模型的成功开发和实施依赖于：(I)ISV的基本理论，它提供了一个载体和不变量算法，将微观现象与宏观行为联系起来；(Ii)通过一组广义的RVE对几何和结构进行定量的微观描述，这些广义RVE组成了用于构造和表征RAFC的统一的几何基元。这些基元也为系统研究微结构和损伤机制之间的关系提供了一种理想的方法。主要的智力优势在于建立了一个跨学科的计划，以创建和定制一种最先进的计算模型，该模型整合了材料科学和应用力学，为RAFC提供了一个全面的分析工具。该项目将为了解更多的纤维增强材料系统，如生物材料和碳纳米管复合材料提供基础。这项工作的社会影响在于，这项工作采取的创新和全面的方法创造了能够更好地描述材料特性的技术。改进的材料造型带来了创造性的产品设计，提高了效率，增加了适用性，并减少了环境负担。教育影响也将是巨大的，因为参与其中的学生将在福特研究实验室实习。积极招募本科生、女生和少数民族学生参加，开展实验和建模工作。

This project will develop an adaptive, scale invariant material model based on microstructural information that will efficiently account for and predict the material and mechanical behaviors inherent in Random Fiber polymer Composites (RaFC). The proposed framework uniquely incorporates representative volume elements (RVE) and independent state variables (ISV) The successful development and implementation of the proposed model depends on: (i) the fundamental theory of ISV, which provides a carrier and invariant algorithm to bridge the micro phenomena building up to macroscopic behaviors; (ii) a quantitative micro description of the geometry and the structure by a set of generalized RVEs, which compose unified geometric primitives used to construct and characterize RaFCs. These primitives also provide an ideal approach for systematically investigating the relationship between microstructure and damage mechanisms. The major intellectual merit lies on establishing an interdisciplinary program to create and tailor a state-of-the-art computational model that integrates material science and applied mechanics to provide a comprehensive analysis tool for RaFCs. This program will provide the underpinnings for understanding more fiber reinforced material systems such as biomaterials and carbon nanotube composites. The societal impact lies in that the innovative and comprehensive approach taken by this work creates enabling technologies for better material characterization. Improved material modeling results in creative product design, improved efficiency, increased applicability and decreased environmental burden. Educational impact will be also significant since the students involved will intern at Ford Research Labs. Participation of undergraduate female and minority students will be actively recruited to perform experimental and modeling tasks.