Research Experiences for Undergraduates in Computational Methods with Applications in Materials Science
MSU Affiliation
High Performance Computing Collaboratory; Institute for Systems Engineering Research
Research Mentor
Eric Collins and Jacob Moore
Creation Date
7-25-2025
Abstract
The microstructure of a metal determines its properties and by understanding the grains that make up that structure, we can predict the behavior of that material. However, it can be difficult and costly to view the microstructure of a metal, especially since the microstructure is highly dependent on the manufacturing history of the part. By computer generating the microstructure of a material, we can better understand its properties. Exascale Cellular Automata (ExaCA) can generate a microstructure for a metal sample given its thermal history and Elasto-Visco Plastic Fast Fourier Transforms (EVPFFT) can model the response of the crystals in a grain structure to deformation. The focus of this investigation is the development of a work method to use both of these programs, compare their fidelity to physical reality and create one script to automatically run both programs in sequence. First, the structure needs to be generated in ExaCA, which for simplicity is a 128x128x128 voxelized cube directionally cooled in the Z direction. The output is converted into an input file for EVPFFT using a python conversion script. Then EVPFFT is used to model that microstructure generated by ExaCA under different conditions, in this case, tension in the Z direction. Running both of these programs from one script can increase the efficiency of the process. This modelling pipeline can be used to generate high fidelity data to increase the accuracy of predictions based on the grain structure of a material and train reduced order models.
Presentation Date
Summer 7-31-2025
Keywords
metals, grain structure
Recommended Citation
Beall, Lizzy; Collins, Eric; and Moore, Jacob, "Developing a Computational Pipeline for Microstructure-based Modelling with ExaCA and EVPFFT" (2025). Research Experiences for Undergraduates in Computational Methods with Applications in Materials Science. 9.
https://scholarsjunction.msstate.edu/ccs-reu/9
