Journal article
Heterogeneous energetic material damage simulator (HEDS): A deep learning approach to simulate damage–sensitivity linkages
APL machine learning, Vol.3(2), 026109
06/01/2025
DOI: 10.1063/5.0257683
Abstract
Damage in the microstructures of energetic materials (EMs), such as propellants and plastic bonded explosives (PBXs), can significantly alter their response to external loads. Both sensitization and desensitization can occur, causing concerns with safety and performance in the field; predictive models that connect damage and the sensitivity of EMs can enable design and provide confidence in their robustness and reliability. However, modeling of damage evolution is challenging for real microstructures of EMs; samples of damaged EMs are difficult to obtain, thereby hindering experiments and direct numerical simulations to determine the sensitivity of EMs at various stages of damage. Here, we develop an approach to generate synthetic, i.e., in silico produced, damaged microstructures for use in simulations to connect damage levels to sensitivity. The development of the present workflow to generate and impose varying levels of damage in microstructures, known as HEDS (Heterogeneous Energetic Material Damage Simulator), begins with a small set of images of damaged PBXs and combines a collection of deep neural network techniques to generate microstructures with varying levels of damage. By making the synthetic microstructures conform closely to those observed in available real, imaged microstructures, we develop an ensemble of damaged microstructures that can be used for in silico shock experiments. HEDS develops these microstructure ensembles as level set fields, which are directly employed in a sharp interface Eulerian hydrocode where shock simulations are performed to quantify the energy release rate from hotspot fields generated in the microstructure. These capabilities can be useful for the analysis and assessment of changes in the sensitivity of EMs and to design formulations that are less susceptible to damage-induced changes in sensitivity and performance.
Details
- Title: Subtitle
- Heterogeneous energetic material damage simulator (HEDS): A deep learning approach to simulate damage–sensitivity linkages
- Creators
- Irene Fang - University of IowaShobhan Roy - University of IowaPhong Nguyen - University of VirginiaStephen Baek - University of VirginiaH. S. Udaykumar - University of Iowa
- Resource Type
- Journal article
- Publication Details
- APL machine learning, Vol.3(2), 026109
- DOI
- 10.1063/5.0257683
- ISSN
- 2770-9019
- eISSN
- 2770-9019
- Publisher
- AIP Publishing
- Grant note
- AFOSR: FA9550-22-1-0239 Livermore National Laboratory LDRD: B663173
This work was supported by AFOSR, Grant No. FA9550-22-1-0239, program managers Dr. Martin J. Schmidt (2021-2022) and Dr. Derek Barbee (2022-present), and by the Livermore National Laboratory LDRD subcontract to the University of Iowa (Grant No. B663173), program manager Dr. Keo Springer, Lawrence Livermore National Laboratories.
- Language
- English
- Date published
- 06/01/2025
- Academic Unit
- Engineering Administration; IIHR--Hydroscience and Engineering; Injury Prevention Research Center; Mechanical Engineering
- Record Identifier
- 9984813289702771
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