Explosives Storage [Slides]
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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SURF is an acronym for Scaled-Unified-Reactive-Front, invented by R. Menikoff and S. Shaw. What follows is a brief synopsis of this model as used for the validation study discussed here. For more details see the xrage user manual and the references cited there.
GW170817 demonstrated the potential of GW+EM observations of NS mergers: most direct observation of r-process production to date. But what can we really learn from the observations. Getting from the observations to constraining properties of the merger ejecta requires multiple aspects of simulation (merger, disk calculations, and radiation flow) and physics (atomic, nuclear cross-section, dense nuclear matter, neutrino, …). Can we maximize what we can do with observations.
With a brilliant hot flash and a loud boom, the giant mushroom cloud with its fiery core filled the predawn sky above the New Mexico desert. The nuclear age had begun. It was 75 years ago on July 16, 1945, that Los Alamos scientists changed the world with the successful detonation of “the Gadget” – the device created to test the Fat Man implosion-type plutonium weapon before it was taken into combat. However, the road to Trinity, as the test was named by J. Robert Oppenheimer, who is thought to have been inspired by a line in a John Donne poem, was a difficult one.
Advanced reactive burn models enable phenomena to be modeled that aren’t possible with programmed burn. Programmed burn propagates a burn front at a prescribed speed from a prescribed initiation point. Reactive burn uses the hydro variables (pressure, temperature, etc.) to determine when, how rapidly, and to what extent a zone detonates. This enables a completely new set of problems to be simulated. Our goal in this milestone has been to improve the usability of reactive burn modes. Thus, the closure criteria are: 1. Perform code and solution verification of the reactive burn models using appropriate solutions and simplified HE setups. 2. Perform validation assessments using small-scale experiments. 3. Evaluate sensitivities and quantify uncertainties due to model form variations and mesh dependencies.
On June 30, 2020, a 0.87 gram PETN charge being pressed in the Rapid Prototyping Facility (RPF), unexpectedly initiated, resulting in destruction of the pressing fixture but no injuries or facility damage. In response, the Safety Review Board (SRB) met on Aug. 13, 2020 and Oct. 1, 2020 to review information collected following the incident, consider likely direct causes, and form recommendations.
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These lightning strikes can be more dangerous than “normal” lightning associated with a regular thunderstorm in that the “bolts from the blue” often occur well away from where the precipitation is occurring at the surface. “Bolts from the blue” occurred at a measured distance of 33 miles and an estimated 35 to 50 miles away from where the precipitation was occurring with this storm.
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Explore the source record for details and available documents.
Explore the source record for details and available documents.
In order to fit models to data, we need a way to simulate the data given a model. Our projects involved simulators for the Manganin gauge experiment and for cylinder tests, which we use to calibrate models to experiments. We call the parameters we seek to optimize the degrees of freedom (DOF). We use a Bayesian approach with its associated UQ benefits.
We present the design and performance of a proof-of-concept 32 channel material identification system. Our system is based on the energy-dependent attenuation of fast neutrons for four elements: hydrogen, carbon, nitrogen and oxygen. We describe a new approach to obtaining a broad range of neutron energies to probe a sample, as well as our technique for reconstructing the molar densities within a sample. The system's performance as a function of time-of-flight energy resolution is explored using a Geant4-based Monte Carlo. Our results indicate that, with the expected detector response of our system, we will be able to determine the molar density of all four elements to within a 20-30% accuracy in a two hour scan time. In many cases this error is systematically low, thus the ratio between elements is more accurate. This degree of accuracy is enough to distinguish, for example, a sample of water from a sample of pure hydrogen peroxide: the ratio of oxygen to hydrogen is reconstructed to within 8 0.5% of the true value. Finally, with future algorithm development that accounts for backgrounds caused by scattering within the sample itself, the accuracy of molar densities, not ratios, may improve to the 5-10% level for a two hour scan time. Experimental performance was evaluated with various thicknesses of polyethylene. The detector response in terms of energy, particle identification, and timing are presented as well.
The data sources tend to have the highest quality data and metadata, particularly for more recent data sets. Early data from sources such as IRIS tend to have some metadata issues.
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