A new benchmark of soft X-ray transition energies of $$\mathrm {Ne}$$, $$\mathrm {CO}_2$$, and $$\mathrm {SF}_6$$: paving a pathway towards ppm accuracy
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Abstract not provided.
Deposits in commercial uranium processing facilities of interest to nuclear safeguards are predominantly low enriched uranium (LEU). The shapes and sizes of holdup deposits can vary significantly, thus making it difficult to model the deposits accurately for calibration purposes. This presents a challenge to currently employed methods such as the Generalized Geometry Holdup (GGH) that rely on quantifying 235U mass by simplifying deposit shapes as a point, a line, or an area. In this work, gamma ray imaging using high energy resolution Germanium Gamma Imagers (GeGI™) is employed to determine the distribution of uranium inside the source containment. Two types of imaging methods are employed; coded-aperture imaging and Compton imaging. Gamma-ray emissions seen in different regions of the image are quantified using an inverse gamma-ray transport solver being developed at ORNL. Using measured data, the inverse method will be used to solve for unknown source parameters such as source matrix thickness, density, and attenuation due to container wall and shielding. The intrinsic efficiency of the detector is determined based on a library of built-in response functions and key dimensions of the detector (e.g., thickness and radius of the detector crystal). Measurements are performed in both imaging modalities using uranium sources of well-known masses and enrichments configured inside mocked-up holdup fixtures. Results from the two imaging modalities are intercompared. The paper presents preliminary results of the imaging measurements and the progress made in the computational methods to quantify uranium mass.
This PowerPoint presentation was presented virtually at the American Geophysical Union (AGU) Fall Meeting 2020.
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Quasi-Monte Carlo sampling was implemented in the OpenOA annual energy production analysis method. Preliminary results show that this method improves the order of the asymptotic convergence of OpenOA’s AEP estimates.