Manufacturing optimized designs for high explosives

When materials are subjected to extreme environments, they face the risk of mixing together. This mixing may result in hydrodynamic instabilities, yielding undesirable side effects. Such instabilities present a grand challenge across multiple disciplines, especially in astrophysics, combustion, and shaped charges—a device used to focus the energy of a detonating explosive, thereby creating a high velocity jet that is capable of penetrating deep into metal, concrete, or other target materials. To address the challenges in controlling these instabilities, LLNL researchers are coupling computing capabilities and manufacturing methods to rapidly develop and experimentally validate modifications to a shaped charge. This work, published in the Journal of Applied Physics, is a part of Project DarkStar, which is aimed at controlling material deformation by investigating the scientific problems of complex hydrodynamics, shockwave physics, and energetic materials. Applying modern technologies to von Neumann’s computational theories, the team employed AI and ML to explore new, computationally optimized designs. The use of additive manufacturing made it possible for researchers to rapidly realize even the most radical AI-designed components that would otherwise be considered “impossible” to create using traditional manufacturing methods. Project DarkStar illuminates the potential of AI/ML to support a wide range of national security missions. Read more at LLNL News.