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在成为此类材料的理想候选者。这些合金利用工程力学中的基本概念来提供所需的性能，如耐恶劣环境。这一学院早期职业发展(CALEAR)奖支持基础工程研究工作，以提供了解、开发和设计稳定的纳米晶合金所需的知识。目前的方法需要改进，以了解这些材料在恶劣环境下是如何变形和失效的。这项工作开发了机器学习辅助的先进计算方法，以克服这些限制。因此，这项工作可能会给航空航天、能源和国防工业带来革命性的变化，并推动美国经济和社会的发展。这项多学科研究横跨材料力学、制造、计算物理和机器学习。这种多学科的方法将吸引来自不同背景的学生，并帮助培训国家的下一代STEM劳动力。稳定的纳米晶合金使用溶质团簇来稳定高温和高应变率加载下的微观结构。溶质团簇钉扎晶界，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被研究，但对于像冲击加载这样的高动态环境，还需要做大量的工作。这项工作发展了一种新的并行原子连续体方法来研究晶界和溶质团簇在冲击诱导缺陷产生和层裂断裂中的作用。基于图神经网络的方法将有助于开发一个微结构敏感的降阶模型来预测连续体级冲击特性。该团队将进行广泛的验证和验证测试，以确保并发模型和图形神经网络的有效性。PI将通过潜在的波传播、材料破坏和机器学习概念将教育和研究结合起来。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--机械块，为K-12年级的学生提供材料和结构力学基本概念的有趣和实践教育。该项目由材料和结构力学(MOMS)计划和既定的激励竞争研究计划(EPSCoR)联合资助。该奖项反映了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。

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.