Directed Energy Lethality Assessments of Hypersonic Threats

Hypersonic threats pose a significant danger to United States defenses due to their high speed and maneuvering capabilities. Low-altitude flight at hypersonic speeds makes tracking and engagement using kinetic weapons exceedingly difficult due to the short timelines and expensive, necessitating the use of directed energy defense measures that can provide speed-of-light engagement at a much lower cost. Pulsed high energy lasers (HELs) are a promising solution. However, the lethality assessment of pulsed HELs related to hypersonic vehicle vulnerabilities is far from complete. Furthermore, models used to assess pulsed laser lethality mechanisms in hypersonic conditions are extremely slow, burdened by the complex set of timescales and physical processes that must be accounted for. Neural Networks (NNs) trained using Machine Learning (ML) algorithms will allow these complex models to be reduced to simple, few-input programs providing fast and accurate defeat assessments across a broad range of engagement conditions. Spectral Energies will partner with the University of Notre Dame (ND) and Purdue University, with support from the Laboratory for Laser Energetics, to build and validate fast running NNs providing hypersonic laser lethality effects predictions. The modeling effort will be based on ND’s expertise in NN assessment of laser ablation, aero-optics, and hypersonic fluid dynamics. Model validation will be enabled through rapid experimentation at Purdue’s hypersonic wind tunnel and projectile facilities, driven by SE’s unique pulsed laser ablation capabilities and advanced hypersonic diagnostics. This includes burst mode HELs, which provide agile pulse duration and repetition rate combinations from a single source, and the only known demonstration of time-resolved 3D hypersonic flow and debris field imaging. The successful Phase I demonstration of initial hypersonic lethality models and NNs will be augmented during Phase II delivery of comprehensive hypersonic defeat models and rapid NN predictions of continuous wave and pulsed laser lethality. Approved for Public Release | 25-MDA-12220 (23 Sep 25)