Progress on Parameter Testing Technology for Fragment Fields of Blast-fragmentation Warheads

LIU Zhuo; LIU Tianyu; XU Yanli; GAO Xingyong; ZHENG Yingjie; SUN Peng; FAN Feigao; LUO Hao; LIU Yangshuo

doi:10.15892/j.cnki.djzdxb.2026.02.001

Abstract

Fragments,as the primary damage elements of blast-fragmentation warhead,have their damage lethality which is quantitatively evaluated through precise testing of parameters such as fragment velocity,spatial distribution and mass characteristics.This paper systematically reviews the latest advancements in the parameter testing technologies for the fragment fields of blast-fragmentation warheads,focusing on comparative analysis under two typical conditions of static and dynamic detonations.In the context of static detonation testing,the principles and features of the contact-type technologies such as net targets,the sectional optoelectronic technologies of light curtains and radar and the 3D reconstruction technologies like high-speed stereovision are compared in detail,and their technological improvements and development trends are elaborated.In the context of dynamic detonation testing,the research achievements in testing methods and simulation modeling at home and abroad are reviewed,and the unique challenges such as detonation point control and spatiotemporal synchronizationunder dynamic detonation conditions as well as the corresponding solutions are thoroughly analyzed.Furthermore,this paper also explores the applications and enabling potential of intelligent algorithms represented by machine learning (particularly deep learning) in the aspects fragment target recognition,trajectory tracking,data fusion,and dynamic explosion parameter prediction.Finally,it offers prospects for the future development trends in fragment field parameter testing technologies,and proposes the need to prioritize high-precision dynamic detonation fragment parameter testing technologies,enhance the 3D reconstruction capabilities and strengthen the integration of machine learning in testing methodologies,thereby supporting the optimized design and damage effectiveness evaluation of blast-fragmentation warheads.

Key words： blast-fragmentation warhead; fragment parameter; static detonation testing; dynamic detonation testing; machine learning

Cite this article

LIU Zhuo , LIU Tianyu , XU Yanli , GAO Xingyong , ZHENG Yingjie , SUN Peng , FAN Feigao , LUO Hao , LIU Yangshuo . Progress on Parameter Testing Technology for Fragment Fields of Blast-fragmentation Warheads[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2026 , 46(2) : 113 -130 . DOI: 10.15892/j.cnki.djzdxb.2026.02.001

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