Stochastic Prepreg Platelet Molded Composites

随机预浸料片状模塑复合材料

• 批准号: 1953907
• 负责人: R. Byron Pipes
• 金额: $ 49.5万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1953907&HistoricalAwards=false
• 关键词: Stochastic; Prepreg; Platelet; Molded; Composites

The prepreg platelet molded composite (PPMC) is a discontinuous fiber composite material form produced by molding of unidirectional fiber prepreg tape platelets. The PPMC material meso-structure allows molding of complex geometries to achieve enhanced specific strength and stiffness and thereby compete with performance of conventional structural metals at significant weight reduction. Large-scale heterogeneity of the stochastic meso-structure in a PPMC produces substantial uncertainty in macroscopic mechanical properties and a range of size effects and “atypical” macro-structural responses, wherein macro-structural geometry does not exclusively define the failure location. These characteristics are poorly understood in the absence of physics-based predictive capabilities and this creates significant obstacles to widespread adoption of PPMCs for structural applications. Insights from this project will lead to improved understanding of how the probabilistic and deterministic parameters of PPMC meso-structure translate into uncertainties in macroscopic performance of the composite and which parameters have the most impact. The research will promote the discovery of new, efficient PPMC material compositions and designs tailored to specific structural applications needed by the aerospace and automotive industries, thus advancing national prosperity. This research effort will train a diverse group of students in various aspects of composites engineering and contribute to the formation of the next generation of scientists that the U.S. composites industry critically needs to compete globally. In addition, this award will support inclusion of undergraduate students from underrepresented minority groups to serve a mission of broadening educational opportunities.The research program aims to study the fundamental failure mechanics of PPMC by developing high-fidelity computational models. The objective of this activity is to derive the material structure-property relationships associated with the interactions between the macro-structural geometry, stochastic meso-scale morphology, and platelet geometry. Probability distributions of effective mechanical properties and relevant failure modes are required inputs to deduce the structure-property relationships. The property distributions will be obtained via the statistically validated numerical simulations (“digital twins”) of standard mechanical tests for composites following the design of experiments principles. The methods that will be employed for virtual material testing are probabilistic modeling (Monte-Carlo approach) and physics-based progressive failure analysis; thereby, leveraging computational damage mechanics and finite-element methods. Statistical significance methods will be utilized to investigate the effect of the control parameters on the composite property distributions. The results of probabilistic modeling will be statistically validated by the experimental data.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.

预浸料片晶模压复合材料（PPMC）是由单向纤维预浸料带片晶模压而成的不连续纤维复合材料。PPMC材料介观结构允许模制复杂的几何形状以实现增强的比强度和刚度，从而在显著减轻重量的情况下与常规结构金属的性能竞争。PPMC中随机细观结构的大尺度非均匀性在宏观力学性能和一系列尺寸效应和“非典型”宏观结构响应中产生了很大的不确定性，其中宏观结构几何形状并不专门定义失效位置。在缺乏基于物理的预测能力的情况下，这些特征知之甚少，这对PPMC在结构应用中的广泛采用造成了重大障碍。从这个项目的见解将导致更好地理解PPMC细观结构的概率和确定性参数如何转化为复合材料的宏观性能的不确定性，以及哪些参数具有最大的影响。该研究将促进发现新的，高效的PPMC材料成分和设计，以满足航空航天和汽车工业所需的特定结构应用，从而促进国家繁荣。这项研究工作将培养复合材料工程各个方面的多元化学生，并为美国复合材料行业在全球竞争中急需的下一代科学家的形成做出贡献。此外，该奖项还将支持来自代表性不足的少数群体的本科生入学，以实现扩大教育机会的使命。该研究计划旨在通过开发高保真计算模型来研究PPMC的基本失效机制。该活动的目的是推导与宏观结构几何形状、随机细观尺度形态和血小板几何形状之间的相互作用相关的材料结构-性能关系。有效力学性能和相关失效模式的概率分布是推导结构-性能关系所需的输入。性能分布将通过统计学验证的数值模拟（“数字孪生”）的标准力学测试的复合材料的实验设计原则。虚拟材料试验将采用的方法是概率建模（蒙特-卡罗方法）和基于物理的渐进失效分析;从而利用计算损伤力学和有限元方法。将利用统计显著性方法来研究控制参数对复合材料性能分布的影响。概率建模的结果将通过实验数据进行统计验证。该奖项反映了NSF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。