Computational Modeling of Human Blast Injuries in the Battle Fields

There is an urgent need within the United States (US) Defense Threat Reduction Agency (DTRA) and military community to understand and mitigate blast induced injuries to soldiers on the battlefield.  While continued development of high-fidelity blast-injury predictive tools is critical, there is also significant need for reduced order models for rapidly evaluating blast injury scenarios. These fast-running predictive tools are essential for providing the community with a means for performing large-scale trade studies and safety analyses that are used to inform the development of safety protocols, CONOPS, and mission planning. To address this need, Shearwater Technology, Inc. (Shearwater) will team with Corvid Technologies, Inc. (Corvid) to combine capabilities and develop a unique toolkit for rapidly evaluating primary and secondary blast injuries. The Phase I product will be based on Shearwater’s EngageThreat fast-running lethality simulation and will leverage a machine learning (ML) model that is trained on high-fidelity blast-injury simulations using Corvid’s in-house finite-element solver, Velodyne and CAVEMAN human model to predict blast injuries.