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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Communication Requirements and Concept of Operation for Sensor Networks

Today’s nuclear power plant (NPP) instrumentation uses current loops and voltage-based communications sometimes compromised by radio frequency interference (RFI), also called electromagnetic interference (EMI). Copper-based communications technology also relies on insulation that may degrade after decades of exposure to NPP environments and can be flammable. Wireless technologies offer the potential for greater expansion in instrumentation in a plant that could augment human performance, provide additional data on plant equipment and component status, and facilitate online assessment of the material condition of plants.

Nuclear Power↗
DOE OSTI2018

High Spatial Resolution Distributed Fiber-Optic Sensor Networks for Reactors and Fuel Cycle Systems

Objective of this NEET research program is to develop distributed fiber sensing solution to perform high spatial resolution measurements of physical and chemical parameters in radiation environments. These additional sensing capabilities will improve safety margins and efficient operation of nuclear energy system and various fuel cycle processes.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Revolutionizing observations and predictability of Arctic system dynamics through next-generation dense, heterogeneous and intelligent wireless sensor networks with embedded AI

The Arctic region is a complex and dynamic system, where changes in temperature and partitioning between water in solid, liquid and vapor phase are fundamentally reshaping hydro-biogeochemical fluxes from the bedrock to the atmosphere. The increasing temperature and frequency of extreme heat, precipitation and fire events (Wang et al., 2017; Meredith et al., 2019) have critical consequences for the Arctic ecosystems and people, as well as for the global climate system. Although many observational and remote sensing activities have been on-going, detailed ecosystem processes – particularly related to controls on and impacts of permafrost thaw – are difficult to detect or measure. To improve the understanding and prediction of these extreme events and their consequences, we need a revolution in the way we collect ground-based observations from hillslope to pan-Arctic scales and couple them with satellite imagery and models to deliver rich, actionable, and scale-appropriate data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Regressing Nuclear Reactor Power Level Using Low-Cost Sensor Network Data

Multisensor networks deployed at nuclear facilities can be leveraged to collect data used as inputs to machine learning models predicting nuclear safeguard relevant information. This work demonstrates an application of this idea by regressing nuclear reactor power levels, a key indicator for nuclear safeguard verification, at the McClellan Nuclear Research Center using data collected by five Merlyn multisensor platforms with LASSO and LSTM models. This work also demonstrates the use of Leave One Node Out to measure the importance of each multisensor for this regression problem providing insight into model explainability and allowing inferential hypotheses about the nuclear facility to be made. This work can be used as a starting point for future development of methods for regression on reactor power levels at nuclear facilities using multisensor network data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗