Journal article
Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials
Scientific reports, Vol.10(1), pp.13307-13307
12/01/2020
DOI: 10.1038/s41598-020-70149-0
PMCID: PMC7413342
PMID: 32764643
Abstract
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.
Details
- Title: Subtitle
- Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials
- Creators
- Sehyun Chun - Iowa City, IA 52242 USASidhartha Roy - Iowa City, IA 52242 USAYen Thi Nguyen - Iowa City, IA 52242 USAJoseph B Choi - Iowa City, IA 52242 USAH. S Udaykumar - Iowa City, IA 52242 USAStephen S Baek - Iowa City, IA 52242 USA
- Resource Type
- Journal article
- Publication Details
- Scientific reports, Vol.10(1), pp.13307-13307
- DOI
- 10.1038/s41598-020-70149-0
- PMID
- 32764643
- PMCID
- PMC7413342
- NLM abbreviation
- Sci Rep
- ISSN
- 2045-2322
- eISSN
- 2045-2322
- Publisher
- Nature Publishing Group UK
- Grant note
- FA9550-19-1-0318 / ;
- Language
- English
- Date published
- 12/01/2020
- Academic Unit
- IIHR--Hydroscience and Engineering; Industrial and Systems Engineering; Radiation Oncology; Injury Prevention Research Center; Mechanical Engineering
- Record Identifier
- 9984121866102771
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