CAREER: Mesoscale Modeling of Defect Structure Evolution in Metallic Materials

University Of Connecticut, Storrs CT

Investigators

Abstract

This Faculty Early Career Development (CAREER) Program project focusses on research in advanced computational mechanics for the virtual analysis of structural metallic materials for use in extreme environments. It supports research on advancing the understanding of the factors that control the evolution of defects and their structure, the micromechanisms for their evolution, and their collective influence on material performance. Understanding the links between the evolution of defect structures during operation and material performance and survivability is a key question in the mechanics of materials. The research will define a clear rationale for why a particular material results in improved toughness or improved strengths or both simultaneously. Such insight would support the development of materials for next generation automotive, aerospace, and defense applications. The virtual analysis contributes to the goals of the Materials Genome Initiative by supplementing physical experiments to reduce costs and time in materials deployment. Educational initiatives through this award will focus on active involvement of undergraduate students and the establishment of a field of study specialization in computational materials science at the University of Connecticut. The integration of materials science, mechanical engineering, and computer science in this framework will help to stimulate an interest in the undergraduate students involved to pursue higher education in science and engineering. Outreach activities will introduce mechanics of materials into pre-college education through leadership roles in local chapters of professional societies and encourage active participation of underrepresented groups to promote diversity in science and engineering education. The objective of this research is to establish insight into the effects of microstructure and loading conditions on the micromechanisms responsible for the nucleation, accumulation, and interaction of defect structures (dislocations, twins, interfaces) as well as nucleation, growth, and coalescence of voids to form cracks (damage). The research employs a newly developed quasi-coarse-grained dynamics (QCGD) method that is able to retain the atomic scale physics of processes involved during deformation and failure but extends the time and length scale capabilities of molecular dynamics simulations. This approach bridges the gap between the atomistic and continuum simulations, and is located at the mesoscale. Machine learning algorithms will be used to map the evolution and distribution of defect structures to the macroscale stress-strain response and identify the distributions that trigger critical events such as damage initiation. This will allow direct connections between the microstructural evolution during deformation and the strength and toughness response for structural metallic materials. This virtual analysis framework capable of providing insights into the performance and survivability will lead to significant advancements in the current state-of-art for materials modeling and can be extended to other structural materials.