Data driven prediction of fragment velocity distribution under explosive loading conditions

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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Bibliographic Details
Main Authors: Donghwan Noh, Piemaan Fazily, Songwon Seo, Jaekun Lee, Seungjae Seo, Hoon Huh, Jeong Whan Yoon
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2025-01-01
Series:Defence Technology
Subjects:
Data driven prediction
Dynamic fracture model
Dynamic hardening model
Fragmentation
Fragment velocity distribution
High strain rate
Online Access:http://www.sciencedirect.com/science/article/pii/S2214914724001776
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http://www.sciencedirect.com/science/article/pii/S2214914724001776

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