Publications by authors named "J P Knauer"
This paper presents a simple physics-based model for the interpretation of key metrics in laser direct drive. The only input parameters required are target scale, in-flight aspect ratio, and beam-to-target radius, and the importance of each has been quantified with a tailored set of cryogenic implosion experiments. These analyses lead to compact and accurate predictions of the fusion yield and areal density as a function of hydrodynamic stability, and they suggest new ways to take advantage of direct drive.
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- The National Ignition Facility is exploring the use of Agfa D4 film to enhance spectral resolution for x-ray imaging in high-energy-density experiments.
- Characterization of the film is being conducted using a Manson x-ray source with six different anodes, building on previous research to improve analysis methods.
- Results from the Agfa D4 film characterization at both the Manson source and the Stanford Synchrotron Radiation Lightsource Beamline 16-2 are compared, highlighting improvements and reduced uncertainties in the characterization process.
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- The study explores the potential use of GPS data collected from devices like smartphones to assess depression, as current methods may benefit from this type of unobtrusive data.
- Researchers reviewed 19 studies with nearly 3,000 participants to analyze the relationship between GPS features (like distance traveled) and depressive symptoms.
- Significant correlations were found, indicating that less distance traveled (and other GPS metrics) is associated with higher levels of depression symptoms, suggesting GPS data could be a valuable tool in understanding mental health.
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- Deciduous trees often photosynthesize more efficiently than evergreens, allowing for faster growth over shorter seasons.
- The study compared 30 evergreen and 20 deciduous tree species, finding that photosynthetic differences were linked to nitrogen allocation rather than overall nutrient content.
- Key factors influencing photosynthesis traits included the leaf carbon-to-nitrogen ratio and nutrient constraints, highlighting the distinct functional strategies of these tree groups.
A deep-learning convolutional neural network (CNN) is used to infer, from x-ray images along multiple lines of sight, the low-mode shape of the hot-spot emission of deuterium-tritium (DT) laser-direct-drive cryogenic implosions on OMEGA. The motivation of this approach is to develop a physics-informed 3-D reconstruction technique that can be performed within minutes to facilitate the use of the results to inform changes to the initial target and laser conditions for the subsequent implosion. The CNN is trained on a 3D radiation-hydrodynamic simulation database to relate 2D x-ray images to 3D emissivity at stagnation.
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