Title
Predicting Metallic Armour Performance When Impacted by Fragment-Simulating Projectiles – Model Adjustments and Improvements
Abstract
In a previous study, a set of nine existing penetration models for blunt projectiles were identified and reviewed for their ability to predict the plugging-mode ballistic limit velocity of monolithic titanium alloy, aluminium alloy, and steel plates. Assessed with a database of more than 650 experimental ballistic limit measurements, often at conditions beyond that for which they had been originally derived or validated, it was found that all nine models could predict the ballistic limit to within +/- 50% for more than 50% of the database entries. In this paper, simple modifications to these nine models are proposed that introduce empirical adjustments, reformulations of the target strength dependency, or a combination of both with the pragmatic goal of identifying a model suitable for application across the wide range of monolithic metallic targets impacted by different calibre fragment-simulating projectiles across a full range of ordnance and sub-ordnance velocities in our database. The modifications were able to improve the performance of all models across this range, with the best performing models able to predict the experimental ballistic limit to within +/- 10% for 50% of the database entries and to within +/- 20% for 76% of the database entries.
Recommended Citation
W. P. Schonberg and S. Ryan, "Predicting Metallic Armour Performance When Impacted by Fragment-Simulating Projectiles – Model Adjustments and Improvements," International Journal of Impact Engineering, vol. 161, article no. 104090, Elsevier, Mar 2022.
The definitive version is available at https://doi.org/10.1016/j.ijimpeng.2021.104090
Department(s)
Civil, Architectural and Environmental Engineering
Keywords and Phrases
Armour penetration; Ballistic limit; Fragment simulating projectile; Plugging
International Standard Serial Number (ISSN)
0734-743X
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2023 Elsevier, All rights reserved.
Publication Date
01 Mar 2022