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Area of Science:

  • Ballistics and Injury Biomechanics
  • Computational Modelling and Simulation
  • Public Safety and Defence Technology

Background:

  • Injury modelling is crucial for informing policy on personal protective equipment (PPE) and injury mitigation strategies.
  • The Ministry of Defence (MoD) and Centre for Protection of National Infrastructure (CPNI) are developing three interlinking numerical models of varying fidelity to address current threats.
  • Existing models range from high-fidelity (realistic but data-intensive) to low-fidelity (fast, statistical assessments).

Purpose of the Study:

  • To describe the collaborative development and application of three distinct fidelity injury models (high, medium, and low).
  • To illustrate how these models address specific questions related to ballistic threats and improvised explosive device (IED) detonations.
  • To highlight future research directions in injury modelling for defence and security.

Main Methods:

  • Development and integration of high-fidelity, medium-fidelity (Personnel Vulnerability Simulation - PVS), and low-fidelity (Human Injury Predictor - HIP) numerical models.
  • Utilizing algorithms based on physical estimations for medium-fidelity models to balance realism and runtime.
  • Employing simplistic algorithms in low-fidelity models for rapid statistical casualty assessments in complex scenarios like IED blasts in crowds.

Main Results:

  • High-fidelity models offer realistic simulation for testing armour effectiveness but are computationally intensive and require extensive experimental data.
  • Medium-fidelity models (PVS) provide a balance of accuracy and speed, enabling full-body assessments.
  • Low-fidelity models (HIP) allow for quick statistical casualty predictions in scenarios with high uncertainty, such as terrorist IED detonations in urban environments.

Conclusions:

  • A tiered approach using multiple fidelity injury models provides a comprehensive framework for assessing ballistic threats and IED impacts.
  • Collaboration between MoD and CPNI is essential for advancing injury modelling capabilities.
  • Further research is needed to refine algorithms and gather experimental data, particularly for high-fidelity models, to enhance predictive accuracy and inform future safety policies.