Stochastic Prepreg Platelet Molded Composites

预浸料片状模塑复合材料(PPMC)是由单向纤维预浸料带片材成型而成的不连续纤维复合材料。PPMC材料的介观结构允许成型复杂的几何形状，以实现更高的比强度和硬度，从而在显著减轻重量的情况下与传统结构金属的性能竞争。PPMC中随机细观结构的大尺度非均质性在宏观力学性质、尺寸效应和“非典型”宏观结构响应的范围内产生了很大的不确定性，其中宏观结构几何并不唯一地定义了失效位置。在缺乏基于物理的预测能力的情况下，人们对这些特征知之甚少，这对结构应用中广泛采用PPMCs造成了巨大的障碍。从这个项目中得到的见解将有助于更好地理解PPMC细观结构的概率和确定性参数如何转化为复合材料宏观性能的不确定性，以及哪些参数具有最大的影响。这项研究将促进发现新的、高效的PPMC材料组成和设计，以适应航空航天和汽车行业所需的特定结构应用，从而促进国家繁荣。这项研究工作将在复合材料工程的各个方面培训一批不同的学生，并有助于形成美国复合材料行业在全球竞争中至关重要的下一代科学家。此外，该奖项将支持纳入来自未被充分代表的少数群体的本科生，以服务于扩大教育机会的使命。研究计划旨在通过开发高保真计算模型来研究PPMC的基本失效机理。本活动的目标是推导出与宏观结构几何、随机中观形态和片状几何之间的相互作用相关的材料结构-性质关系。有效力学性能的概率分布和相关的破坏模式是推导结构-性能关系所必需的输入。性能分布将通过符合实验设计原则的复合材料标准力学试验的统计验证数值模拟(“数字孪生”)来获得。用于虚拟材料测试的方法是概率建模(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。利用统计显著性方法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。这一奖项反映了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。

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.