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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Development of the Science Data System for the International Space Station Cold Atom Lab

Cold Atom Laboratory (CAL) is a facility that will enable scientists to study ultra-cold quantum gases in a microgravity environment on the International Space Station (ISS) beginning in 2016. The primary science data for each experiment consists of two images taken in quick succession. The first image is of the trapped cold atoms and the second image is of the background. The two images are subtracted to obtain optical density. These raw Level 0 atom and background images are processed into the Level 1 optical density data product, and then into the Level 2 data products: atom number, Magneto-Optical Trap (MOT) lifetime, magnetic chip-trap atom lifetime, and condensate fraction. These products can also be used as diagnostics of the instrument health. With experiments being conducted for 8 hours every day, the amount of data being generated poses many technical challenges, such as downlinking and managing the required data volume. A parallel processing design is described, implemented, and benchmarked. In addition to optimizing the data pipeline, accuracy and speed in producing the Level 1 and 2 data products is key. Algorithms for feature recognition are explored, facilitating image cropping and accurate atom number calculations.

bose einstein condensate↗
DOE OSTI2023

Operation and Maintenance of PV Systems: Data Science, Analysis, and Standards

This effort improves the effectiveness and reduce uncertainty in O&M cost through four primary objectives/tasks: 1) institutionalize standards for reliability and availability reporting for large PV power plants; 2) bridge systemic O&M knowledge gaps around important topics affecting O&M; 3) characterize systemic failure modes and patterns and accelerate O&M experiential learning cycles using field data; and 4) establish a baseline understanding of UPVS O&M cost drivers. Key results of this effort include publication of IEC standards, published topical papers on O&M topics, training, and characterize field data for climate- and service-related patterns (additional details below). Integrating these results serves to reduce performance risk and facilitate improvement in the way solar projects are operated and maintained. Results are well received and two publications are among the most successful SETO publications at NREL ("Model of Operation and Maintenance Costs for Photovoltaic Systems with over 40,000 downloads and "Best Practices in Operation and Maintenance of PV Systems, 3rd Ed." with over 90,000 downloads).

14 SOLAR ENERGY↗
NASA NTRS2013

GRAIL Science Data System Orbit Determination : Approach, Strategy, and Performance

This paper details orbit determination techniques and strategies employed within each stage of the larger iterative process of preprocessing raw GRAIL data into the gravity science measurements used within gravity field solutions. Each orbit determination pass used different data, corrections to them, and/or estimation parameters. We compare performance metrics among these passes. For example, for the primary mission, the magnitude of residuals using our orbits progressed from approximately or equal to19.4 to 0.077 approximately or equal to m/s for inter-satellite range rate data and from approximately or equal to 0.4 to approximately or equal to 0.1 mm/s for Doppler data.

Gravity Recovery and Interior Laboratory (GRAIL)↗
DOE OSTI2022

An Overview in the Development of a Multi-Sensor Data Science System for Monitoring a Solvent Extraction Process

To enhance nuclear nonproliferation stewardship, Idaho National Laboratory is building the Beartooth nuclear fuel cycle processing test bed. The Beartooth test bed will allow researchers the opportunity to study nuclear fuel processing operations including the use of centrifugal contactors in solvent extraction processes. The test bed is designed to support data collection and machine learning to monitor process operations. As part of this project, researchers will study data collected from a host of sensors that have not been typically used to monitor solvent extraction processes such as vibration, acoustic, colormetric, and thermal measurement data. The goal of this research is to employ machine learning and data analytics to study the confluence of signals collected from both traditionally and non-traditionally used sensors to provide operator process awareness and discover process anomalies. An overview of planned sensors and experiments that will focus on signal discovery will be presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

An Overview in the Development of a Multi-Sensor Data Science System for Monitoring a Solvent Extraction Process

To enhance nuclear nonproliferation stewardship, Idaho National Laboratory is building the Beartooth nuclear fuel cycle processing test bed. The Beartooth test bed will allow researchers the opportunity to study nuclear fuel processing operations including the use of centrifugal contactors in solvent extraction processes. The test bed is designed to support data collection and machine learning to monitor process operations. As part of this project, researchers will study data collected from a host of sensors that have not been typically used to monitor solvent extraction processes such as vibration, acoustic, colormetric, and thermal measurement data. The goal of this research is to employ machine learning and data analytics to study the confluence of signals collected from both traditionally and non-traditionally used sensors to provide operator process awareness and discover process anomalies. An overview of planned sensors and experiments that will focus on signal discovery will be presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Development of a Multi Sensor Data Science System for Monitoring a Solvent Extraction Process

Idaho National Laboratory is designing and constructing a solvent extraction test bed, Beartooth, as part of a nuclear fuel cycle stewardship initiative. The test bed will provide facilities and equipment for testing novel extraction processes and give early career scientists opportunities to gain skills in performing separations chemistry. The test bed is being uniquely designed to enable machine learning capabilities for the characterization of chemical process operations. One of the intentions of incorporating machine learning methods is to provide process operators with enhanced situational awareness for the optimization of separations techniques and possible detection of a diversion event. As a result, the objective of this Laboratory Directed Research and Development project is to develop a multi-sensor data collections system and implement machine learning techniques on signals from an existing centrifugal contactor cascade to identify equipment usage and process events. This presentation will provide an overview of sensors used and experiments conducted to date.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Development of a Multi-Sensor Data Science System Used for Signature Development on Solvent Extraction Processes in support of safeguards- an overview

A new nuclear fuel cycle test bed is being built at Idaho National Laboratory to support the purification of special nuclear material recovered from used fuel. The test bed provides an opportunity to research process flow and the application of computational tools in solvent extraction processes. A deeper understanding of process and equipment behavior coupled with real time data collection can indicate whether a process failure is accidental or purposeful. The goal of this project is to develop a system that utilizes non-traditional measurement sources such as vibration, acoustics, current, light, flow, and temperature in conjunction with data-based, machine learning techniques that will allow for signal discovery. This multi-sensor data can support the development of safeguards by design and security by design measures for such a facility. Additionally, it can aid in early detection and identification of removed materials indicating diversion, which is essential for initiating material recovery and actor identification. This overview encompasses the current research and testing of sensors to develop a spectrum of process signatures. To be followed by planned experiments aimed to characterize said signatures and study potential feature extraction techniques to identify a fault in the system (i.e. flow diversion).

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Preliminary Results of a Multi-Sensor Data Science System for Monitoring a Solvent Extraction Process

Idaho National Laboratory is building a test bed to allow researchers the opportunity to study nuclear fuel processing operations. This includes studying the solvent extraction process and the use of centrifugal contactors. The goal of this project is to develop a system that utilizes non-traditional measurement sources such as vibration, acoustics, current, color, flow, and temperature in conjunction with data-based, machine learning techniques that will allow for signal discovery. This multi-sensor data supports the development of safeguards by design, provides operator process awareness, and aids in the discovery of process anomalies. This paper highlights some of the preliminary results from initial data collection campaigns and shares some of the lessons learned.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Data Challenges in Multi-Sensor Data Science System for Monitoring a Solvent Extraction Process

Idaho National Laboratory (INL) is maintaining and gaining knowledge into the nuclear fuel cycle by building a test bed to allow researchers the opportunity to study nuclear fuel processing operations. This includes studying solvent extraction processes that use centrifugal contactors. As part of INL’s mission, the goal of this project is to develop a system that utilizes non-traditional measurement sources such as vibration, acoustics, current, light, flow, and temperature in conjunction with data-based, machine learning techniques that will allow for signal discovery. This multisensory data can support the development of safeguards by design, provide operator process awareness, and discover process anomalies. This poster will highlight some of the data collection and analytics challenges for the multi-sensor system as well as the mitigation strategies to build a robust system. Additionally, some preliminary data from the first testing campaign will be shown to help illustrate the data needs of the system.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS1968

Science data systems

Digital generation of acceleration voltage for mass spectrometer, and development of low power, nondestructive readout, plated-wire memory

Source record↗
NASA NTRS1968

Science data systems

Shock tests on Mariner Venus 67 prototype data automation subsystems to evaluate multilayer laminate packaging

Source record↗
NASA NTRS1991

Scientific requirements for space science data systems

In the 1990's space plasma physics studies will increasingly involve correlative analysis of observations from multiple instruments and multiple spacecraft. The solar terrestrial physics missions in the 1990's will be designed around simultaneous observations from spacecraft monitoring the solar wind, the polar magnetosphere and the near and distant magnetotail. Within these regions, clusters of spacecraft flying in formation will increasingly involve comparative magnetospheric studies. No single lab will have the expertise to process and analyze all of the different types of data so the data repositories will be distributed. Catalog and browse systems will be required to help select events for study. Data compression techniques may be useful in designing the data bases used for selecting events for study. Data compression onboard the spacecraft will be necessary, since instrument data rates will be much larger than available telemetry rates. However, considerable care will be necessary to avoid losing valuable data when applying data compression algorithms.

Walker, Raymond J.↗