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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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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Panel Session 13: D and D Collaboration Between US DOE EM and NNSA (R6.3)

This panel focused on the interaction and collaboration between US Department of Energy's Office of Environmental Management (DOE EM) and National Nuclear Security Administration (NNSA) offices and sites. Panelists with presentations: NNSA and DOE EM Collaboration on Facility Disposition (Deborah Couchman-Griswold); DOE EM and NNSA D and D Collaboration at the Y-12 National Security Complex (Diane McDaniel); Working Together to Achieve Environmental Cleanup Success in Oak Ridge (Laura Wilkerson, James Daffron); DOE-EM - NNSA (Rick Demmer)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Artificial Intelligence Application to D and D - 20492

As aging facilities across the DOE complex await decommissioning, there is an ongoing need to understand any changes in the structural conditions. Many of these facilities were built over 50 years ago and, in some cases, these facilities have gone beyond the expected operational lifetime. Many facilities have been placed in a state of 'cold and dark,' sitting unused and awaiting decommissioning. Especially challenging are the aging facilities that provide unique operational/production capabilities to support critical DOE missions and cannot be shut down. In any of these scenarios, the structural integrity of these facilities may become compromised as time passes. It is critical that adequate inspections be performed on a continual basis and that the data collected undergoes sufficient analysis to support timely identification of any new or worsening structural issues as well as prompt needed maintenance and repairs to maintain the facilities in a safe condition. In recent days, Artificial Intelligence (AI) [1] and its application to various domains are growing at fast speed. FIU is performing research in this area and exploring the associated technologies to solve nuclear decommissioning problems. Artificial intelligence refers to the capability of a program to autonomously act, react and adapt to the working environment. AI enables the machine to behave like humans and perform the cognitive functions such as 'learning' and 'problem solving'. AI systems gradually moving from traditional approaches (algorithms and expert systems) towards more efficient and advanced technologies (machine learning [1] and deep learning [2] [3]). AI is the study of algorithms and statistical models that is being used by computers to perform specific tasks without using explicit instructions. FIU is working to develop a pilot-scale infrastructure to implement structural health monitoring using AI technologies with focus on machine learning, deep learning. This research is focused on Computer Vision/Image Classification area of AI applications. This can also be expanded to other areas of AI related to Object Recognition and Character Recognition in images. In addition to utilizing existing data sets, FIU will collect and investigate image and video data using FIU test-bed mockups to monitor structural health of the facility. Resulting data will be processed and analyzed using machine learning/deep learning technologies. The proposed pilot system is intended to serve as a starting point to engage the DOE field sites on related data sets and their decision making needs. It is anticipated that proposed machine learning/deep learning technologies can be effectively employed using anomaly detection to solve EM challenges in surveillance and maintenance of the D and D facilities. FIU will work with research stakeholders to identify applications at various sites and other DOE facilities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Searches for 25 rare and forbidden decays of D + and ${D}_s^{+}$ mesons

A search is performed for rare and forbidden charm decays of the form ${D}_{(s)}^{+}\to {h}^{\pm }{\mathrm{\ell}}^{+}{\mathrm{\ell}}^{\left(\prime \right)\mp } $, where h ± is a pion or kaon and ℓ (')± is an electron or muon. The measurements are performed using proton-proton collision data, corresponding to an integrated luminosity of 1.6 fb –1 , collected by the LHCb experiment in 2016. No evidence is observed for the 25 decay modes that are investigated and 90 % confidence level limits on the branching fractions are set between 1.4 × 10 –8 and 6.4 × 10 –6 . In most cases, these results represent an improvement on existing limits by one to two orders of magnitude.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Modeling a divertor with mid-leg pumping for high-power H-mode scenarios in DIII-D considering E x B drift flows

Edge-plasma simulations of a baffled, long-legged divertor in DIII-D, performed using the multi-fluid code UEDGE, indicate that the position of the detachment front is constrained to the location of the pump duct along the low-field side (LFS) baffle. Simulations including magnetic and E x B drifts were performed for 12.5 MW deuterium plasmas including intrinsic carbon and seeded neon to assess the optimal location of the LFS divertor pump to create a stable detachment front between the target and the X-point. The radiation front position in the simulations, taken to be indicative of the detachment front, can be controlled between the pump and X-point in the favorable magnetic field direction for H-mode access by moving the pump duct location upstream of the target along the LFS baffle. In the unfavorable magnetic field direction, the radial E θ x B drift flows are directed towards the pumping surface, efficiently removing the injected deuterium gas and limiting the sensitivity of the radiation front location to the gas injection rate. The role of pumping rate and drift direction on the pumping efficiency are also found to affect the divertor plasma conditions and detachment front location in UEDGE simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1989

Numerical simulations - Some results for the 2- and 3-D Hubbard models and a 2-D electron phonon model

Numerical simulations on the half-filled three-dimensional Hubbard model clearly show the onset of Neel order. Simulations of the two-dimensional electron-phonon Holstein model show the competition between the formation of a Peierls-CDW state and a superconducting state. However, the behavior of the partly filled two-dimensional Hubbard model is more difficult to determine. At half-filling, the antiferromagnetic correlations grow as T is reduced. Doping away from half-filling suppresses these correlations, and it is found that there is a weak attractive pairing interaction in the d-wave channel. However, the strength of the pair field susceptibility is weak at the temperatures and lattice sizes that have been simulated, and the nature of the low-temperature state of the nearly half-filled Hubbard model remains open.

Scalapino, D. J.↗