LLNL has developed a method that adds a polyamine based crosslinker and an acid receptor, based on MgO nanoparticles into a polymer bonded PBX, where the polymer binder is a fluoropolymer containing vinylidene difluoride functionality. Crosslinking kinetics can then be controlled by selecting an appropriate amine structure, pressing temperature and optionally the addition of a chemical…
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LLNL’s Distributed Implicit Neural Representation (DINR) is a novel approach to 4D time-space reconstruction of dynamic objects. DINR is the first technology to enable 4D imaging of dynamic objects at sufficiently high spatial and temporal resolutions that are necessary for real world medical and industrial applications.
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LLNL researchers uses Additive Manufacturing (AM) to create reinforcing scaffolds that can be integrated with High Explosives (HE) or solid rocket fuel with minimal volume fraction. Its main benefit is to create stability in harsh field conditions. Its secondary benefit is providing another method to finely tune blast performance or fuel burn. Creating complex shapes with structural…
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The LiDO code combines finite element analysis, design sensitivity analysis and nonlinear programming in a High-Performance Computing (HPC) environment that enables the solution of large-scale structural optimization problems in a computationally efficient manner. Currently, the code uses topology optimization strategies in which a given material is optimally distributed throughout the domain…