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

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.