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

职业：建筑复合材料中扩散驱动的降解和失效机制

• 批准号: 2046476
• 负责人: Pavana Prabhakar
• 金额: $ 53.94万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046476&HistoricalAwards=false
• 关键词: CAREER; Diffusion; Driven; Degradation; Failure

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.

该学院早期职业发展（Career）奖将支持基础研究，以阐明增强聚合物复合材料中的纤维结构如何影响受潮，以及在使用时随之而来的降解和失效。由于其高强度重量比和显著的节能性能，复合材料在航空航天、汽车、海洋工业以及民用基础设施的轻量化结构应用中具有巨大的前景。然而，这些材料不可避免地暴露于环境中的水分，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。该研究将为结构用复合材料的设计和制造提供重要的指导。研究活动将辅以一系列综合教育活动，以培养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，以及为不同群体的本科生提供研究机会。此外，该奖项将通过各种机构项目，如4-H和青年会议，通过广泛传播研究成果，在公共空间中提高认识。本研究的多重目标是：1)确定纤维结构如何影响织物纤维增强聚合物复合材料（frpc）内水分扩散路径的扭曲度；2)建立水分扩散系数与有效力学性能之间的关系；3)阐明水分扩散路径和伴随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来解决编织frp中常见的多种长度尺度的纤维结构。该研究方法依靠有限元建模结合基于卷积神经网络的机器学习来探索纤维结构扭曲度、水分扩散率和力学性能之间的相关性；采用纺织编织技术制备所选试样；以及利用数字体积相关和傅立叶变换红外光谱的x射线微层析成像来表征扩散路径。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Role of material directionality on the mechanical response of Miura-Ori composite structures

• DOI: 10.1016/j.compstruct.2022.116606
• 发表时间: 2022-06
• 期刊: Composite Structures
• 影响因子: 6.3
• 作者: Haotian Feng;Guanjin Yan;P. Prabhakar