Stochastic Prepreg Platelet Molded Composites

预浸液片状模压复合材料（PPMC）是一种由单向纤维预浸液带片状模压而成的不连续纤维复合材料。PPMC材料的细观结构允许复杂几何形状的成型，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传统结构金属的性能相竞争。PPMC中随机细观结构的大规模非均匀性产生了宏观力学性能的大量不确定性，以及一系列尺寸效应和“非典型”宏观结构响应，其中宏观结构几何形状不能完全定义破坏位置。在缺乏基于物理的预测能力的情况下，人们对这些特性知之甚少，这对ppmc在结构应用中的广泛采用造成了重大障碍。该项目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，以及哪些参数影响最大。这项研究将促进发现新的、高效的PPMC材料成分和设计，以适应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。这项研究工作将在复合材料工程的各个方面培养多样化的学生群体，并为美国复合材料行业在全球竞争中迫切需要的下一代科学家的形成做出贡献。此外，该奖项将支持来自未被充分代表的少数群体的本科生，以扩大教育机会。本研究计划旨在透过建立高保真的计算模型，研究PPMC的基本失效机制。这项活动的目的是推导出与宏观结构几何、随机中尺度形态和血小板几何之间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失效模式是推断结构-性能关系的输入。根据实验设计原则，通过统计验证的复合材料标准力学测试数值模拟（“数字孪生”）获得性能分布。将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；因此，利用计算损伤力学和有限元方法。统计显著性方法将用于研究控制参数对复合属性分布的影响。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。

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.