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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The approach is to build a high voltage insulator consisting of two materials: Poly-Ether-Ether-Ketone (“PEEK”) and Machinable Ceramic (“MACOR”). PEEK has a high stress tolerance but cannot withstand high temperatures, while MACOR has high heat tolerance but is difficult to machine and can be brittle. MACOR is used for the plasma-facing surface, while PEEK will handle the…
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LLNL’s approach is to use their patented Photoconductive Charge Trapping Apparatus (U.S. Patent No. 11,366,401) as the active switch needed to discharge voltage across a vacuum gap in a particle accelerator, like the one described in their other patent (U.S. Patent No.
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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…