Abstract
We present a methodological framework for the evaluation and comparison of surface spray fragmentation patterns from a range of medium-calibre air bursting munitions. The methodology is underpinned by both analytic and probabilistic modelling techniques. In particular, we present a fly-out model for the calculation of the terminal speed of a fragment on the impact plane. The fly-out model constitutes a trade-off between the computational efficiency of analytic models and the accuracy of detailed numerical methods extant in the literature. The methodology has been developed with the ability to readily adapt to modifications in the gun and ammunition parameters, with applications to controlled and naturally fragmenting munitions, as well as shrapnel-based warheads. This is demonstrated by comparing the surface spray patterns of two different ammunition types.
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Notes
- 1.
- 2.
- 3.
The Improved Kinetic Energy—Electronically Timed advanced shotgun projectile.
- 4.
These models, which include detailed atmospheric data, usually require advanced numerical methods (e.g. fourth order Runge-Kutta) for their solution (see [5]).
- 5.
Also known as the injury criterion for anti-personnel applications [14].
- 6.
To verify that the polar representation describes the same conical surface, the components may be substituted into the known cartesian equation given in Eq. (2). This can be verified with a computational algebra software package such as MAPLE®.
- 7.
For example, the cross-sectional area of a spherical fragment is \(A = \pi d^2/4\), where d is the diameter.
- 8.
See Chap. 8 of Carlucci and Jacobson [3] for further details on the approximation of drag profiles by analytic functions.
- 9.
Produced in MATLAB®.
- 10.
For example, comparing the hyper-ballistic trajectories to standard trajectories.
- 11.
This is the so-called fragmentation blooming effect.
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Chircop, P.A. (2018). Analytic and Probabilistic Techniques for the Determination of Surface Spray Patterns from Air Bursting Munitions. In: Sarker, R., Abbass, H., Dunstall, S., Kilby, P., Davis, R., Young, L. (eds) Data and Decision Sciences in Action. Lecture Notes in Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-55914-8_9
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