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Sjue, Sky K.

Publications and source records attributed to Sjue, Sky K..

22 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Image quality indicator and dechromator setup for static LANSCE proton radiography experiment

This document describes a static geometry consisting of a sphere of gold and Ti-6Al-4V alloy, along with an energy-loss matching dechromator composed of polycarbonate, to field with the X3 electromagnetic magnifying lens at LANSCE during the next beam cycle. This static configuration will allow us to: (1) characterize the system’s resolution at the material interface between the gold and titanium alloy, and test the image quality for fine features associated with the screws used to fasten the titanium alloy hemispheres; (2) tune our models of proton scattering and imaging for these materials; and (3) test a procedure to align the polycarbonate dechromator and the sphere within the proton beamline. The data will also be suitable for validation of the software options available for proton radiography simulations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Thermoelasticity Model for Lithium Fluoride

Lithium fluoride (LiF) is a typical ionic solid, and at the same time a typical polymorphic (multi-phase) material. Its phase diagram contains two solid phases, B1 (rock salt) and B2 (CsCl), which are typical of ionic solids. The phase diagram of LiF was suggested by Smirnov. We have carried out extensive study of the properties of LiF using one of the most recent versions of VASP (Vienna Ab initio Simulation Package) which allows one to carry out calculations on ionic solids; more detail will be presented elsewhere.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Uncertainties in Density and Simulation Parameters for Radiographic Reconstructions Using Machine Learning

We will develop new and improved methods that will enable a fuller and more extensive use of experimental data, such as from radiographic imaging, towards better characterizing and reducing uncertainties in predictive modeling of weapons performance. We will achieve this by leveraging recent developments in the areas of computational imaging, statistical and machine learning, and reduced order modeling. Key new aspects of our approach include (a) a coupling of deep learning based density reconstruction with fast hydrodynamics simulators that will permit the development of certain guarantees on extrapolatability and bounds on certain other hydrodynamics-related para- metric uncertainties and (b) a dual approach towards better characterizing uncertainties in density reconstruction using deep learning based surrogates to accelerate Bayesian reconstructions on the one hand and using variational inference and Bayesian machine learning on the other hand.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗