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

目标：随机纤维结构：材料表征

• 批准号: 0728049
• 负责人: Assimina Pelegri
• 金额: $ 24.24万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0728049&HistoricalAwards=false
• 关键词: GOALI; Random; Fiber; Structures; Material

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.

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）中固有的材料和机械行为。该模型的成功开发和实现依赖于：（1）独立状态变量（ISV）的基本理论，它提供了一个载体和不变的算法，将微观现象和宏观行为联系起来;（ii）通过一组广义RVE对RaFC的几何和结构进行定量的微观描述，RVE构成用于构造和表征RaFC的统一几何图元。这些基元也为系统研究微观结构与损伤机制之间的关系提供了一种理想的方法。主要的智力价值在于建立一个跨学科的计划，以创建和定制一个最先进的计算模型，集成材料科学和应用力学，为RaFC提供一个全面的分析工具。该计划将为理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。社会影响在于，这项工作所采取的创新和全面的方法为更好的材料表征创造了技术。改进的材料建模可以实现创新的产品设计，提高效率，增加适用性并减少环境负担。教育影响也将是显着的，因为参与的学生将在福特研究实验室实习。 将积极招募女大学生和少数民族大学生参与实验和建模任务。