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Projectile penetration into ballistic gelatin.
M V Swain1, D C Kieser, S Shah
1Sir John Walsh Research Institute, Faculty of Dentistry, The University of Otago, Dunedin, New Zealand; Biomaterials Laboratory, Faculty of Dentistry, The University of Sydney, NSW 2010, Australia.
Journal of the Mechanical Behavior of Biomedical Materials
|November 5, 2013
Summary
Ballistic gelatin penetration depth linearly correlates with projectile velocity above a threshold. This study refines predictive models for projectile impact on soft tissues using a modified elastic Froude number.
Area of Science:
- Biomechanics
- Materials Science
- Forensic Science
Background:
- Ballistic gelatin serves as a surrogate for soft biological tissues under impact.
- Understanding projectile penetration dynamics is crucial for forensic and safety applications.
Purpose of the Study:
- To investigate the penetration mechanics of spherical projectiles in ballistic gelatin.
- To develop and validate predictive models for gelatin's response to impact.
Main Methods:
- Testing penetration depth of steel spheres (7-11mm) in various gelatin formulations at velocities below 120m/s.
- Analyzing the relationship between penetration depth, projectile velocity, diameter, and gelatin properties.
- Comparing experimental data with existing predictive models and proposing a modified Froude number relationship.
Main Results:
- Penetration depth showed a linear relationship with impact velocity above a critical threshold.
- Normalized penetration depth (by projectile diameter) condensed data for different sphere sizes onto a single curve.
- A refined predictive model incorporating projectile diameter, threshold velocity, and estimated shear modulus was developed.
- Normalized penetration data best fit a modified elastic Froude number with a slope of 1/2.
Conclusions:
- The study provides a robust model for predicting ballistic gelatin penetration.
- The findings offer improved understanding of soft tissue response to projectile impact.
- The modified elastic Froude number offers a more accurate predictive tool within the tested parameters.

