CAREER: Diffusion-Driven Degradation and Failure Mechanisms in Architected Composites

该学院早期职业发展(CALEAR)奖将支持基础研究，阐明增强聚合物复合材料中的纤维结构如何影响水分渗透，以及在使用时随之而来的退化和失效。由于其高强度重量比和显著的在役能源节约，复合材料在航空航天、汽车和海洋工业以及民用基础设施中的轻量化结构应用方面具有巨大的前景。然而，这些材料不可避免地暴露在环境中的湿气中并受到影响，甚至空气中的湿度，这使得它们容易降解和损坏，从而严重限制了它们在关键应用中的使用。这项研究将为结构用复合材料的设计和制造提供必要的指导。研究活动将辅之以一系列综合教育活动，以培训下一代复合材料劳动力，例如，通过为在职专业人员和研究生设计的混合在线课程，以及为不同的本科生群体提供研究机会。此外，该奖项将用于通过各种机构计划，如4-H和青年会议，广泛传播研究成果，从而在公共空间提高人们的意识。这项研究的多重目标是：1)确定纤维结构如何影响机织纤维增强聚合物复合材料(FRPC)中水分扩散路径的曲折；2)建立水分扩散系数和有效力学性能之间的关系；以及3)阐明水分扩散路径和伴随的降解对机械失效模式的影响。这项研究将使用一个集成的计算和实验框架，解决机织FRPC中常见的多个长度尺度的纤维结构。该研究方法依赖于有限元建模和基于卷积神经网络的机器学习，以探索纤维结构的曲折性、水分扩散率和机械性能之间的相关性；使用纺织品编织技术制造选定的样品；以及通过具有数字体积相关和映射傅里叶变换红外光谱的X射线显微断层扫描对扩散路径进行表征。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This Faculty Early Career Development (CAREER) award will support fundamental research in elucidating how fiber architecture in reinforced polymer composite materials influences moisture ingression, and consequent degradation and failure when used in service. Owing to their high strength-to-weight ratios and significant in-service energy savings, composites have tremendous promise for lightweight structural applications in the aerospace, automotive, and marine industries, as well as civil infrastructure. However, these materials are inevitably exposed to and affected by moisture in the environment, even humidity in the air, which makes them vulnerable to degradation and damage, thus severely limiting their use in critical applications. This research will provide essential guidance for designing and fabricating composites for structural use. The research activities will be complemented with a series of integrated educational activities to train the next generation composites workforce, e.g., through a blended online course designed for working professionals and graduate students alike and research opportunities for a diverse group of undergraduate students. Additionally, the award will be used to create awareness in the public space through broad dissemination of research outcomes via various institutional programs, such as 4-H and Youth Conferences. The multi-fold objectives of this research are to: 1) determine how fiber architecture influences the tortuosity of moisture diffusion pathways within woven fiber-reinforced polymer composites (FRPCs); 2) establish the relationship between moisture diffusivity and effective mechanical properties; and 3) elucidate the impact of moisture diffusion pathways and concomitant degradation on mechanical failure modes. This research will use an integrated computational and experimental framework that resolves fiber architectures at multiple length scales common in woven FRPCs. The research approach relies on finite element modeling combined with convolutional neural network-based machine learning for exploring the correlations between tortuosity of fiber architecture, moisture diffusivity, and mechanical performance; fabrication of the selected specimens using textile weaving technology; and characterization of diffusion pathways through X-ray microtomography with digital volume correlation and mapped Fourier-transform infrared spectroscopy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.