Research Paper Title
Injury representation against ballistic threats using three novel numerical models.
Injury modelling of ballistic threats is a valuable tool for informing policy on personal protective equipment and other injury mitigation methods.
Currently, the Ministry of Defence (MoD) and Centre for Protection of National Infrastructure (CPNI) are focusing on the development of three interlinking numerical models, each of a different fidelity, to answer specific questions on current threats.
- High-fidelity models simulate the physical events most realistically, and will be used in the future to test the medical effectiveness of personal armour systems. They are however generally computationally intensive, slow running and much of the experimental data to base their algorithms on do not yet exist.
- Medium fidelity models, such as the personnel vulnerability simulation (PVS), generally use algorithms based on physical or engineering estimations of interaction. This enables a reasonable representation of reality and greatly speeds up runtime allowing full assessments of the entire body area to be undertaken.
- Low-fidelity models such as the human injury predictor (HIP) tool generally use simplistic algorithms to make injury predictions. Individual scenarios can be run very quickly and hence enable statistical casualty assessments of large groups, where significant uncertainty concerning the threat and affected population exist.
HIP is used to simulate the blast and penetrative fragmentation effects of a terrorist detonation of an improvised explosive device within crowds of people in metropolitan environments.
This paper describes the collaboration between MoD and CPNI using an example of all three fidelities of injury model and to highlight future areas of research that are required.
Breeze, J., Fryer, R., Pope, D. & Clasper, J. (2016) Injury Representation against Ballistic Threats using Three Novel Numerical Models. Journal of the Royal Army Medical Corps. 2016 Nov 3. pii: jramc-2016-000687. doi: 10.1136/jramc-2016-000687. [Epub ahead of print].