Mesh Generation and AI-Enhanced Algorithms for Modeling Complex Materials Systems
Abstract
Key objectives of this proposal are threefold: (i) creating an automated computational framework relying customized image processing on CISAMR algorithms for creating high-fidelity FE models of polycrystalline microstructures; (ii) expanding CISAMR to enable modeling complex crack growth problems, involving crack kinking/merging; (iii) developing an AI-enhanced framework to not only significantly speedup the simulation of mechanical behavior of materials with complex microstructures but also enable modeling massive problems not feasible via DNS. These efforts aim to address some of the limitations of state-of-the-art algorithms available for the treatment of ICME and DNS problems by both automating and enhancing the modeling process, without sacrificing the accuracy. The proposed algorithms are highly transformative and can be used for modeling a variety of materials systems with complex microstructures, as well as applications beyond the realm of material research.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 21, 2022
- Source ID
- FA95502110245XX0
Entities
People
- Soheil Soghrati
Organizations
- Air Force Office of Scientific Research
- Ohio State University
- United States Air Force