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Material parameterization including damage for predicting diver lung underwater explosion (UNDEX) injury.

Srivatsa Bhat Kaudur1, Daniel C Hammerand2, Mohamed Jrad2

  • 1Kevin T. Crofton Department of Aerospace and Ocean Engineering, Virginia Polytechnic Institute and State University, United States of America.

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Summary

This study presents a computational model to predict lung injury from underwater explosions (UNDEX). The framework uses anatomical data and material properties to simulate blast effects and quantify damage, aiding safety assessments.

Keywords:
DamageHyperelasticityHyperviscoelasticityInjuryLungUnderwater explosion (UNDEX)

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Area of Science:

  • Computational mechanics
  • Biomedical engineering
  • Injury biomechanics

Background:

  • Underwater explosions (UNDEX) pose significant risks to human health and marine life.
  • Predicting lung injury from blast waves requires sophisticated modeling of complex biological tissues.
  • Existing models often lack detailed anatomical representation and accurate material property parameterization.

Purpose of the Study:

  • To develop a computational framework for predicting the mechanical response of the lung to UNDEX.
  • To create a parameterization methodology for hyperelastic and hyperviscoelastic lung tissue behavior, including damage.
  • To enable quantitative assessment of lung injury severity and spatial distribution.

Main Methods:

  • Integration of anatomically informed finite-element geometry with constitutive models.
  • Parameter identification using literature data, experimental dataset refitting, and plausible value adoption.
  • Quantification of injury via mappings from clinical severities to affected lung tissue percentages.
  • Incorporation of parameterized damage fields into blast simulations.

Main Results:

  • A validated computational framework capable of simulating lung response to UNDEX.
  • Methodology for parameterizing lung tissue properties and damage, circumventing direct biological testing.
  • Quantitative injury assessment integrated into blast simulations.

Conclusions:

  • The developed model provides a robust capability for predicting lung injury from underwater blast events.
  • This framework can inform operational protocols, risk assessments, and forensic analyses related to UNDEX.
  • The model has potential applications in evaluating environmental impacts, including marine mammal exposure to blast scenarios.