Heterogeneous energetic material damage simulator (HEDS): A deep learning approach to simulate damage–sensitivity linkages
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 mode...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2025-06-01
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| Series: | APL Machine Learning |
| Online Access: | http://dx.doi.org/10.1063/5.0257683 |
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| author | Irene Fang Shobhan Roy Phong Nguyen Stephen Baek H. S. Udaykumar |
| author_facet | Irene Fang Shobhan Roy Phong Nguyen Stephen Baek H. S. Udaykumar |
| author_sort | Irene Fang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5c4c2fc1639b40fe9626ef91fff23744 |
| institution | DOAJ |
| issn | 2770-9019 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | APL Machine Learning |
| spelling | doaj-art-5c4c2fc1639b40fe9626ef91fff237442025-08-20T03:14:57ZengAIP Publishing LLCAPL Machine Learning2770-90192025-06-0132026109026109-1810.1063/5.0257683Heterogeneous energetic material damage simulator (HEDS): A deep learning approach to simulate damage–sensitivity linkagesIrene Fang0Shobhan Roy1Phong Nguyen2Stephen Baek3H. S. Udaykumar4Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, Iowa 52242, USADepartment of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, Iowa 52242, USASchool of Data Science, University of Virginia, Charlottesville, Virginia 22903, USASchool of Data Science, University of Virginia, Charlottesville, Virginia 22903, USADepartment of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, Iowa 52242, USADamage 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.http://dx.doi.org/10.1063/5.0257683 |
| spellingShingle | Irene Fang Shobhan Roy Phong Nguyen Stephen Baek H. S. Udaykumar Heterogeneous energetic material damage simulator (HEDS): A deep learning approach to simulate damage–sensitivity linkages APL Machine Learning |
| title | Heterogeneous energetic material damage simulator (HEDS): A deep learning approach to simulate damage–sensitivity linkages |
| title_full | Heterogeneous energetic material damage simulator (HEDS): A deep learning approach to simulate damage–sensitivity linkages |
| title_fullStr | Heterogeneous energetic material damage simulator (HEDS): A deep learning approach to simulate damage–sensitivity linkages |
| title_full_unstemmed | Heterogeneous energetic material damage simulator (HEDS): A deep learning approach to simulate damage–sensitivity linkages |
| title_short | Heterogeneous energetic material damage simulator (HEDS): A deep learning approach to simulate damage–sensitivity linkages |
| title_sort | heterogeneous energetic material damage simulator heds a deep learning approach to simulate damage sensitivity linkages |
| url | http://dx.doi.org/10.1063/5.0257683 |
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