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

职业：研究微观结构在稳定纳米晶合金高应变率行为中的作用

• 批准号: 2338296
• 负责人: Vinamra Agrawal
• 金额: $ 64.77万
• 依托单位: Auburn University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2029-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2338296&HistoricalAwards=false
• 关键词: CAREER; Investigating; Role; Microstructure; Strain

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.

能够承受恶劣环境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材料，这些合金利用工程力学的基本概念来提供所需的性能。作为对恶劣环境的抵抗力，该学院早期职业发展（职业）奖支持提供理解、开发和设计稳定的纳米晶合金的知识所需的基础工程研究工作，需要提高当前方法的理解能力。这些材料在恶劣的环境下会变形和失效。这项工作开发了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，并推动美国经济和社会的发展。涵盖材料力学、制造、计算物理和机器学习的多学科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簇来稳定微观结构。在高温和高应变率负载下，溶质团簇高度固定晶界并防止缺陷扩展。这项工作开发了一种新颖的并发原子连续体方法来研究晶界和溶质簇对冲击引起的缺陷产生和剥落断裂的作用，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。该团队将进行广泛的验证和验证测试，以确保并行模型和图神经网络的有效性，并将通过基础波传播、材料失效和机器学习概念来整合教育和研究。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 学生提供有关材料和结构力学基本概念的有趣和实践教育。该项目由美国力学学会联合资助材料和结构 (MoMS) 计划和刺激竞争研究既定计划 (EPSCoR)。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。