Data driven prediction of fragment velocity distribution under explosive loading conditions
This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition. The fragment resultant velocities are correlated with key design parameters including casing dimensions and detonation positions. The paper de...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-01-01
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| Series: | Defence Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214914724001776 |
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| author | Donghwan Noh Piemaan Fazily Songwon Seo Jaekun Lee Seungjae Seo Hoon Huh Jeong Whan Yoon |
| author_facet | Donghwan Noh Piemaan Fazily Songwon Seo Jaekun Lee Seungjae Seo Hoon Huh Jeong Whan Yoon |
| author_sort | Donghwan Noh |
| collection | DOAJ |
| description | This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition. The fragment resultant velocities are correlated with key design parameters including casing dimensions and detonation positions. The paper details the finite element analysis for fragmentation, the characterizations of the dynamic hardening and fracture models, the generation of comprehensive datasets, and the training of the ANN model. The results show the influence of casing dimensions on fragment velocity distributions, with the tendencies indicating increased resultant velocity with reduced thickness, increased length and diameter. The model's predictive capability is demonstrated through the accurate predictions for both training and testing datasets, showing its potential for the real-time prediction of fragmentation performance. |
| format | Article |
| id | doaj-art-2840c5d96c9246afa0c834085767c1a2 |
| institution | Kabale University |
| issn | 2214-9147 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Defence Technology |
| spelling | doaj-art-2840c5d96c9246afa0c834085767c1a22025-01-23T05:26:47ZengKeAi Communications Co., Ltd.Defence Technology2214-91472025-01-0143109119Data driven prediction of fragment velocity distribution under explosive loading conditionsDonghwan Noh0Piemaan Fazily1Songwon Seo2Jaekun Lee3Seungjae Seo4Hoon Huh5Jeong Whan Yoon6Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of KoreaDepartment of Mechanical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of KoreaWarhead System Team, Precision Firepower Research Division, POONGSAN Defense R&D Institute, 2606-10 Hoguk-ro, Angang-Oup, Kyung Ju, Kyung-Buk, Republic of KoreaWarhead System Team, Precision Firepower Research Division, POONGSAN Defense R&D Institute, 2606-10 Hoguk-ro, Angang-Oup, Kyung Ju, Kyung-Buk, Republic of KoreaWarhead System Team, Precision Firepower Research Division, POONGSAN Defense R&D Institute, 2606-10 Hoguk-ro, Angang-Oup, Kyung Ju, Kyung-Buk, Republic of KoreaDepartment of Mechanical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of KoreaDepartment of Mechanical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; School of Engineering, Deakin University, Wauran Ponds, VIC 3216, Australia; Corresponding author.This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition. The fragment resultant velocities are correlated with key design parameters including casing dimensions and detonation positions. The paper details the finite element analysis for fragmentation, the characterizations of the dynamic hardening and fracture models, the generation of comprehensive datasets, and the training of the ANN model. The results show the influence of casing dimensions on fragment velocity distributions, with the tendencies indicating increased resultant velocity with reduced thickness, increased length and diameter. The model's predictive capability is demonstrated through the accurate predictions for both training and testing datasets, showing its potential for the real-time prediction of fragmentation performance.http://www.sciencedirect.com/science/article/pii/S2214914724001776Data driven predictionDynamic fracture modelDynamic hardening modelFragmentationFragment velocity distributionHigh strain rate |
| spellingShingle | Donghwan Noh Piemaan Fazily Songwon Seo Jaekun Lee Seungjae Seo Hoon Huh Jeong Whan Yoon Data driven prediction of fragment velocity distribution under explosive loading conditions Defence Technology Data driven prediction Dynamic fracture model Dynamic hardening model Fragmentation Fragment velocity distribution High strain rate |
| title | Data driven prediction of fragment velocity distribution under explosive loading conditions |
| title_full | Data driven prediction of fragment velocity distribution under explosive loading conditions |
| title_fullStr | Data driven prediction of fragment velocity distribution under explosive loading conditions |
| title_full_unstemmed | Data driven prediction of fragment velocity distribution under explosive loading conditions |
| title_short | Data driven prediction of fragment velocity distribution under explosive loading conditions |
| title_sort | data driven prediction of fragment velocity distribution under explosive loading conditions |
| topic | Data driven prediction Dynamic fracture model Dynamic hardening model Fragmentation Fragment velocity distribution High strain rate |
| url | http://www.sciencedirect.com/science/article/pii/S2214914724001776 |
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