Given the challenge of achieving truly insensitive munitions, automation of sensing and condition reporting of energetic materials (EM) could be a transformative innovation for prevention/mitigation of explosive safety incidents. A multivariate system approach is needed based on knowledge of: EM behavior in elevated heat and mechanical environments, advanced sensor technologies and capabilities, and the expected impacts of passive or active avoidance/mitigation strategies. Development of a multivariate algorithm (MvA) is challenged by the uncertainty of a priori and real time information and the technology for automated decision-making. The a priori information will contain predicted responses of EM in variable locations under a variety of mechanical and thermal stress scenarios. The real time information includes sensor-derived data (e.g., temperature/heating rate, pressure, gas concentrations) and external inputs (e.g., damage observations, mitigation effectiveness). Predicting energetic material responses requires balancing a priori and real-time information. Compact heat transfer and temperature calculations will be derived to ensure the MvA achieves the balance of computational precision versus cycle time to make prediction uncertainty acceptable for automated condition reporting and decision making. Transition success demands an MvA architecture that facilitates integration between new MvA elements and existing shipboard sensors, processors and displays.
Keywords: Modeling And Simulation, Modeling And Simulation, Hazard Effects Mitigation, Insensitive Munitions, Ship Survivability, Automated Hazard Detection, Damage Control, Automated D
