Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
06 Aug 2020
06 Aug 2020
Historique:
received:
10
04
2020
accepted:
30
06
2020
entrez:
9
8
2020
pubmed:
9
8
2020
medline:
9
8
2020
Statut:
epublish
Résumé
The sensitivity of heterogeneous energetic (HE) materials (propellants, explosives, and pyrotechnics) is critically dependent on their microstructure. Initiation of chemical reactions occurs at hot spots due to energy localization at sites of porosities and other defects. Emerging multi-scale predictive models of HE response to loads account for the physics at the meso-scale, i.e. at the scale of statistically representative clusters of particles and other features in the microstructure. Meso-scale physics is infused in machine-learned closure models informed by resolved meso-scale simulations. Since microstructures are stochastic, ensembles of meso-scale simulations are required to quantify hot spot ignition and growth and to develop models for microstructure-dependent energy deposition rates. We propose utilizing generative adversarial networks (GAN) to spawn ensembles of synthetic heterogeneous energetic material microstructures. The method generates qualitatively and quantitatively realistic microstructures by learning from images of HE microstructures. We show that the proposed GAN method also permits the generation of new morphologies, where the porosity distribution can be controlled and spatially manipulated. Such control paves the way for the design of novel microstructures to engineer HE materials for targeted performance in a materials-by-design framework.
Identifiants
pubmed: 32764643
doi: 10.1038/s41598-020-70149-0
pii: 10.1038/s41598-020-70149-0
pmc: PMC7413342
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
13307Subventions
Organisme : Air Force Office of Scientific Research
ID : FA9550-19-1-0318
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