Predictive modeling of dynamic fracture growth in brittle materials with machine learning

Journal Article · 21 February 2018 · Computational Materials Science

DOI: https://doi.org/10.1016/j.commatsci.2018.01.056 · OSTI ID:1425767

Moore, Bryan A. ^[1]; Rougier, Esteban ^[1]; O’Malley, Daniel ^[1]; Srinivasan, Gowri ^[1]; Hunter, Abigail ^[1]; Viswanathan, Hari ^[1]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

We use simulation data from a high delity Finite-Discrete Element Model to build an e cient Machine Learning (ML) approach to predict fracture growth and coalescence. Our goal is for the ML approach to be used as an emulator in place of the computationally intensive high delity models in an uncertainty quanti cation framework where thousands of forward runs are required. The failure of materials with various fracture con gurations (size, orientation and the number of initial cracks) are explored and used as data to train our ML model. This novel approach has shown promise in predicting spatial (path to failure) and temporal (time to failure) aspects of brittle material failure. Predictions of where dominant fracture paths formed within a material were ~85% accurate and the time of material failure deviated from the actual failure time by an average of ~16%. Additionally, the ML model achieves a reduction in computational cost by multiple orders of magnitude.

• Research Organization: Los Alamos National Laboratory (LANL)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC52-06NA25396
• OSTI ID: 1425767
• Alternate ID(s): OSTI ID: 1547147
• Report Number(s): LA-UR-17-30614
• Journal Information: Computational Materials Science, Journal Name: Computational Materials Science Journal Issue: C Vol. 148; ISSN 0927-0256
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English