Defeat of High Velocity Projectiles by a Novel Spaced Armor System

Abstract

Development and testing of a novel armor system that defeats high-velocity projectiles and penetrators is described, from a phenomenological perspective. The process is covered where fundamental principles of shock mechanics and high fidelity computational physics (HFCP) simulations are used to create the armor system, from concept to proof testing. This armor development effort started with a “clean sheet,” which allowed for the basic ideas of penetration mechanics and advanced simulation to be brought to bear. Particular attention was paid to the nature of the two threats the armor was intended to stop: (1) three .50 caliber armor piercing (AP) projectiles at 850 meters per second, fired one at a time and grouped within a 5 centimeter diameter circle, and (2) an 18.6 gram mild steel projectile at 2,500 meters per second. The impact response of each threat is fundamentally different, and thus requires different mechanisms to defeat. Primary constraints on the armor system were minimal cost, no ceramic components, and minimal weight. There was freedom to make the system relatively thick, which allowed for the use of spaced components to progressively defeat each threat. Simulation was applied to determine geometries and materials that break up and yaw the AP threat, while maintaining multi-hit robustness. Simulation was also used to adapt the design to produce shattering shock pressures in the higher velocity mild steel, and then arrest of the resulting debris cloud. Simulations were performed using an in-house developed Lagrangian hydrocode having smooth particle hydrodynamic (SPH) capabilities. Outlined items include the material and failure models used, benefits of Lagrange-to-SPH conversion, and advantages of massive parallel processing capabilities which enabled the HFCP simulations. Also outlined is a design process that relies on the simulation capability to achieve minimal prototyping and testing. Results from proof tests are shown. Comparisons are made to show that simulations match well with the test data.