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Meso-scale simulation of energetic materials. II. Establishing structure–property linkages using synthetic microstructures.
- Source :
- Journal of Applied Physics; 2/7/2022, Vol. 131 Issue 5, p1-19, 19p
- Publication Year :
- 2022
-
Abstract
- Meso-scale simulations of pressed energetic materials are performed using synthetic microstructures generated using deep feature representation, a deep convolutional neural network-based approach. Synthetic microstructures are shown to mimic real microstructures in the statistical representation of global and local features of micro-morphology for three different classes of pressed HMX with distinctive micro-structural characteristics. Direct numerical simulations of shock-loaded synthetic microstructures are performed to calculate the meso-scale reaction rates. For all three classes, the synthetic microstructures capture the effect of morphological uncertainties of real microstructures on the response to shock loading. The calculated reaction rates for different classes also compare well with those of the corresponding real microstructures. Thus, the article demonstrates that machine-generated ensembles of synthetic microstructures can be employed to derive structure–property–performance linkages of a wide class of real pressed energetic materials. The ability to manipulate the synthetic microstructures using deep learning-based approaches then provides an opportunity for material designers to develop and manufacture pressed energetic materials that can yield targeted performance. [ABSTRACT FROM AUTHOR]
- Subjects :
- MICROSTRUCTURE
COMPUTER simulation
Subjects
Details
- Language :
- English
- ISSN :
- 00218979
- Volume :
- 131
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Journal of Applied Physics
- Publication Type :
- Academic Journal
- Accession number :
- 155124919
- Full Text :
- https://doi.org/10.1063/5.0065298