Data driven prediction of fragment velocity distribution under explosive loading conditions - Donghwan Noh; Piemaan Fazily; Songwon Seo; Jaekun Lee; Seungjae Seo; Hoon Huh; Jeong Whan Yoon (Defence Technology)
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.