LLNL researchers have developed additive manufactured fuel targets for IFE.  They have been successful in using TPL to fabricate low density (down to 60 mg/cm3) and low atomic number (CHO) polymeric foams for potential targets, and some have been tested at the OMEGA Laser Facility. With TPL, LLNL researchers have also been able to fabricate a full fuel capsule with diameter of ~ 5mm or…

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. 

This invention configures multiple spherical substrate targets to roll independently of one another.  The spheres’ rolling motion is deliberately randomized to promote uniform coating while eliminating the interaction (rubbing, sliding) of adjacent spheres that is present in conventional sphere coating designs. The devices’ novel structure features enable the collimation of depositing…

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…

LLNL has developed a new active memory data reorganization engine. In the simplest case, data can be reorganized within the memory system to present a new view of the data. The new view may be a subset or a rearrangement of the original data. As an example, an array of structures might be more efficiently accessed by a CPU as a structure of arrays. Active memory can assemble an alternative…