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The future battlefield will include multiple dissimilar manned and unmanned aerial, ground, sea, and space vehicles working in concert with each other to support fires, logistics, maneuvers, communication, and coordination-based missions. Mission effectiveness and efficiency are often at odds, and due to the distributed and dissimilar energy flows inherent in Multi-Domain Operations (MDO) there is a need to understand, identify, and characterize the energy flows. The ability to analyze the energy flows and effectively maintain adequate energy reserves could provide strategic capabilities to the warfighters, permitting energy informed operations to maximize mission effectiveness and efficiency, while mitigating vulnerabilities. This research focuses on developing energy and exergy characterization through development of Artificial Intelligence (AI), Machine Learning (ML), and Artificial Neural Networks (ANNs) for assessing and analyzing performance of a platform.

A blast buck (Accelerative Loading Fixture, or ALF) was developed for studying underbody blast events in a laboratory-like setting. It was designed to provide a high-magnitude, high-rate, vertical loading environment for cadaver and dummy testing. It consists of a platform with a reinforcing cage that supports adjustable-height rigid seats for two crew positions. The platform has a heavy frame with a deformable floor insert. Fourteen tests were conducted using fourteen PMHS (post mortem human surrogates) and the Hybrid III ATD (Anthropomorphic Test Device). Tests were conducted at two charge levels: enhanced and mild. The surrogates were tested with and without PPE (Personal Protective Equipment), and in two different postures: nominal (knee angle of 90°) and obtuse (knee angle of 120°). The ALF reproduces damage in the PMHS commensurate with injuries experienced in theater, with the most common damage being to the pelvis and ankle.