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 have developed a technology suite that includes several methods for detecting trace levels of illicit drugs even in mixtures. These methods can be used as a rapid screening test for incoming samples; for the samples that were determined to contain detectable amounts, they would undergo final verification using conventional laboratory analytical techniques.
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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…
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Dubbed the "LLNL Chemical Prism", the LLNL system has use wherever there is a need to separate components of a fluid. A few examples include:
- Chemical detection for known and previously unknown chemicals or substances
- Separation of biomolecules from a cellular extract
- Fractionation of a complex mixture of hydrocarbons
- Forensic analysis of…