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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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Evolution of Network Enumeration Strategies in Emulated Computer Networks
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Solving a steady-state pde using spiking networks and neuromorphic hardware.
Abstract not provided.
Synergetic Use of the WSR-88D Radars, GOES-R Satellites, and Lightning Networks to Study Microphysical Characteristics of Hurricanes
Abstract This study analyzes the microphysics and precipitation pattern of Hurricanes Harvey (2017) and Florence (2018) in both the eyewall and outer rainband regions. From the retrievals by a satellite red–green–blue scheme, the outer rainbands show a strong convective structure while the inner eyewall has less convective vigor (i.e., weaker upper-level reflectivities and electrification), which may be related to stronger vertical wind shear that hinders fast vertical motions. The WSR-88D column-vertical profiles further confirm that the outer rainband clouds have strong vertical motion and large ice-phase hydrometeor formation aloft, which correlates well with 3D Lightning Mapping Array source counts in height and time. From the results from this study, it is determined that the inner eyewall region is dominated by warm rain, whereas the external rainband region contains intense mixed-phase precipitation. External rainbands are defined here as those that reside outside of the main hurricane circulation, associated with surface tropical storm wind speeds. The synergy of satellite and radar dual-polarization parameters is instrumental in distinguishing between the key microphysical features of intense convective rainbands and the warm-rain-dominated eyewall regions within the hurricanes. Substantial amounts of ice aloft and intense updrafts in the external rainbands are indicative of heavy surface precipitation, which can have important implications for severe weather warnings and quantitative precipitation forecasts. The novel part of this study is to combine ground-based radar measurement with satellite observations to study hurricane microphysical structure from surface to cloud top so as to fill in the gaps between the two observational techniques.
Autonomous Network and IT Resource Management for Geographically Distributed Data Centers
Not Available
Leveraging Mixed-Strategy Gaming to Realize Incentive-Driven VNF Service Chain Provisioning in Broker-Based Elastic Optical Inter-Datacenter Networks
Not Available
Flexible Network Architecture and Provisioning Strategy for Geographically Distributed Metro Data Centers
Not Available
Next-Generation Exascale Network Integrated Architecture for Global Science [Invited]
Not Available
Pipeline Defects Detection and Classification Based on Distributed Fiber Sensors and Neural Networks
An integrated approach is presented to detect pipeline defects using distributed acoustic sensors and machine learning. Over 80% accuracy for defected pipe identification was achieved with defect depth classifications with error less than 1-mm.
Air Monitoring Network at Tonopah Test Range: Network Description, Capabilities, and Analytical Results
During the period April to June 2008, at the behest of the Department of Energy (DOE), National Nuclear Security Administration, Nevada Site Office (NNSA/NSO); the Desert Research Institute (DRI) constructed and deployed two portable environmental monitoring stations at the Tonopah Test Range (TTR) as part of the Environmental Restoration Project Soils Activity. DRI has operated these stations since that time. A third station was deployed in the period May to September 2011. The TTR is located within the northwest corner of the Nevada Test and Training Range (NTTR), and covers an area of approximately 725.20 km2 (280 mi2). The primary objective of the monitoring stations is to evaluate whether and under what conditions there is wind transport of radiological contaminants from Soils Corrective Action Units (CAUs) associated with Operation Roller Coaster on TTR. Operation Roller Coaster was a series of tests, conducted in 1963, designed to examine the stability and dispersal of plutonium in storage and transportation accidents. These tests did not result in any nuclear explosive yield. However, the tests did result in the dispersal of plutonium and contamination of surface soils in the surrounding area.
Energy Sciences Network (ESnet): Nuclear Physics Network Requirements Review Report
In May 2019, ESnet and the Office of Nuclear Physics (NP) of the DOE Office of Science organized an ESnet requirements review of NP-supported activities. Preparation for this event included identification of key stakeholders to the process: program and facility management, research groups, technology providers, and a number of external observers. These individuals were asked to prepare formal case study documents about their relationship to the NP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward. A series of pre-planning meetings better prepared case study authors for this task and facilitated a smooth transition to the in-person review.
High Energy Physics Network Requirements Review (Final Report, July-October 2020)
Throughout 2020, ESnet and the Office of High Energy Physics (HEP) of the DOE SC organized an ESnet requirements review of HEP-supported activities. Preparation for this event included identification of key stakeholders: program and facility management, research groups, technology providers, and a number of external observers. These individuals were asked to prepare formal case study documents about their relationship to the HEP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward. A series of pre-planning meetings better prepared case study authors for this task, along with guidance on how the review would proceed in a virtual fashion.
Network Calculus and Mu2e event building Network
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Predicting Voltage Changes in Low-Voltage Secondary Networks using Deep Neural Networks
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