CAREER: Investigating the Role of Microstructure in the High Strain Rate Behavior of Stable Nanocrystalline Alloys

能够在恶劣环境中生存的先进轻质材料对于设计航空航天、能源和国防应用的下一代结构至关重要。稳定的纳米晶合金正成为这类材料的理想候选材料。这些合金利用工程力学的基本概念来提供理想的性能，如抵抗恶劣环境。该学院早期职业发展（Career）奖支持基础工程研究工作，为理解、开发和设计稳定的纳米晶体合金提供知识。目前的方法需要改进，以了解这些材料在恶劣环境下如何变形和失效。这项工作开发了由机器学习辅助的先进计算方法来克服这些限制。因此，这项工作可以彻底改变航空航天、能源和国防工业，推动美国经济和社会的发展。这个多学科的研究跨越了材料力学、制造、计算物理和机器学习。多学科方法将吸引来自不同背景的学生，并帮助培养下一代国家STEM劳动力。稳定型纳米晶合金在高温和高应变速率载荷下，利用溶质团簇稳定微观组织。溶质团簇固定晶界，防止缺陷扩展。虽然晶界和溶质团簇在准静态加载下的作用已经得到了研究，但在激波加载等高动态环境下还需要做很多工作。这项工作发展了一种新的并发原子连续体方法来研究晶界和溶质团簇在激波诱导缺陷产生和小片断裂中的作用。基于图神经网络的方法将有助于开发微观结构敏感的降阶模型来预测连续级冲击特性。该团队将执行广泛的验证和验证测试，以确保并发模型和图神经网络的有效性。PI将通过潜在的波传播、材料失效和机器学习概念整合教育和研究。该团队将与当地科学博物馆合作，开发一个独特的学习平台Mechblocks，为K-12学生提供有趣的实践教育，让他们了解材料和结构力学的基本概念。该项目由材料和结构力学（mom）计划和促进竞争研究的既定计划（EPSCoR）共同资助。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。

Advanced lightweight materials capable of surviving harsh environments are critical to designing next-generation structures for aerospace, energy, and defense applications. Stable nanocrystalline alloys are emerging as ideal candidates for such materials. These alloys leverage fundamental concepts in engineering mechanics to provide desirable properties such as resistance against harsh environments. This Faculty Early Career Development (CAREER) award supports fundamental engineering research efforts needed to provide the knowledge to understand, develop, and design stable nanocrystalline alloys. Current methods need to be improved in their ability to understand how these materials deform and fail under harsh environments. This work develops advanced computational methods assisted by machine learning to overcome these limitations. As such, this work could revolutionize the aerospace, energy, and defense industries and advance the US economy and society. This multidisciplinary research spans mechanics of materials, manufacturing, computational physics, and machine learning. The multidisciplinary approach will engage students from diverse backgrounds and help train the next generation of the nation’s STEM workforce.Stable nanocrystalline alloys use solute clusters to stabilize the microstructure under high temperatures and high strain rate loading. Solute clusters pin grain boundaries and prevent defect propagation. While the role of grain boundaries and solute clusters have been studied under quasi-static loading, much work is needed for highly dynamic environments like shock loading. This work develops a novel concurrent atomistic continuum approach to investigate the role of grain boundaries and solute clusters on shock-induced defect generation and spall fracture. A graph neural network-based approach will help develop a microstructurally sensitive reduced-order model to predict continuum-level shock properties. The team will perform extensive validation and verification tests to ensure the validity of the concurrent model and the graph neural network. The PI will integrate education and research through underlying wave propagation, material failure, and machine learning concepts. In partnership with local science museums, the team will develop a unique learning platform, Mechblocks, to provide fun and hands-on education to K-12 students on underlying concepts of the mechanics of materials and structures.This project is jointly funded by the Mechanics of Materials and Structures (MoMS) program and the Established Program to Stimulate Competitive Research (EPSCoR).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.