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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Neural Networks

Artificial intelligence (AI)-based techniques for intelligent contingency management (ICM) require that intelligent agents learn various aspects of system dynamics to create and execute contingencies. For high assurance contingency management, agents achieve the most compelling results through supervised or semi-supervised machine learning, for which agents require large datasets to learn the dynamics of the system. Unfortunately, data collection in aerospace applications can be costly, due to both time and resources. Presented work describes a framework for data augmentation of ICM databases containing training data for machine learning models. This framework populates the database with the outputs of generative adversarial network (GAN) models that were trained on flight data. Methods for evaluating the suitability of these models based on the equations of motion, as well as other physical constraints, are discussed. The paper demonstrates the utility of this database for training intelligent agents on the NASA T2 generic transport aircraft model and experimental vertical takeoff and landing (VTOL) simulation model.

Generative Machine Learning↗
NASA NTRSDate not supplied

Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Neural Networks

Artificial intelligence (AI)-based techniques for intelligent contingency management (ICM) require that intelligent agents learn various aspects of system dynamics to create and execute contingencies. For high assurance contingency management, agents achieve the most compelling results through supervised or semi-supervised machine learning, for which agents require large datasets to learn the dynamics of the system. Unfortunately, data collection in aerospace applications can be costly, due to both time and resources. Presented work describes a framework for data augmentation of ICM databases containing training data for machine learning models. This framework populates the database with the outputs of generative adversarial network (GAN) models that were trained on flight data. Methods for evaluating the suitability of these models based on the equations of motion, as well as other physical constraints, are discussed. The paper demonstrates the utility of this database for training intelligent agents on the NASA T2 generic transport aircraft model and experimental vertical takeoff and landing (VTOL) simulation model.

Generative Machine Learning↗
NASA NTRS1983

Development INTERDATA 8/32 computer system

The capabilities of the Interdata 8/32 minicomputer were examined regarding data and word processing, editing, retrieval, and budgeting as well as data management demands of the user groups in the network. Based on four projected needs: (1) a hands on (open shop) computer for data analysis with large core and disc capability; (2) the expected requirements of the NASA data networks; (3) the need for intermittent large core capacity for theoretical modeling; (4) the ability to access data rapidly either directly from tape or from core onto hard copy, the system proved useful and adequate for the planned requirements.

Sonett, C. P.↗
NASA NTRS1989

COLD-SAT: Cryogenic On-Orbit Liquid Depot-Storage, Acquisition and Transfer

NASA is entering an era of expanded space activity. Space-based transportation systems will carry cargo and humans from low earth orbit to geosynchronous orbit, to lunar bases, and to the Martian surface. Support of these future missions will require new, long lived, on-orbit systems using subcritical cryogens for propellants and life support systems. Such on-orbit systems present low gravity fluid management challenges of long term storage and efficient fluid transfer and supply techniques. Development of these cryogenic systems requires on-orbit experimentation to demonstrate the capability of performing these fluid management tasks and to obtain the engineering data base required to correlate analytical tools used for system design.

Source record↗
DOE OSTI2020

Reliable machine prognostic health management in the presence of missing data

Prognostics and health management enables the prediction of future degradation and remaining useful life (RUL) for in-service systems based on historical and contemporary data, showing promise for many practical applications. One major challenge for prognostics is the common occurrence of missing values in time-series data, often caused by disruptions in sensor communication or hardware/software failures. Another major concern is that the sufficient prior knowledge of critical component degradation with a clear failure threshold is often not readily available in practice. These issues can significantly hinder the application of advanced signal and data analysis methods and consequently degrade the health management performance. In this article, we propose a novel data-driven framework that is capable of providing accurate and reliable predictions of degradation and RUL. In this approach, one-hot health state indicators are appended to the historical time series so that the model learns end-of-life automatically. A modified gate recurrent unit based variational autoencoder is employed in generative adversarial networks to model the temporal irregularity of the incomplete time series. Furthermore, experiments on multivariate time-series datasets collected from real-world aeroengines verify that significant performance improvement can be achieved using the proposed model for robust long-term prognostics.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Recent Experience with the CMS Data Management System

The CMS[1] experiment manages a large-scale data infrastructure, currently handling over 200 PB of disk and 500 PB of tape storage and transferring more than 1 PB of data per day on average between various WLCG[2] sites. Utilizing Rucio[3] for high-level data management, FTS[4] for data transfers, and a variety of storage and network technologies at the sites, CMS confronts inevitable challenges due to the system’s growing scale and evolving nature. Key challenges include managing transfer and storage failures, optimizing data distribution across different storages based on production and analysis needs, implementing necessary technology upgrades and migrations, and efficiently handling user requests. The data management team has established comprehensive monitoring to supervise this system and has successfully addressed many of these challenges. The team’s efforts aim to ensure data availability and protection, minimize failures and manual interventions, maximize transfer throughput and resource utilization, and provide reliable user support. This paper details the operational experience of CMS with its data management system in recent years, focusing on the encountered challenges, the effective strategies employed to overcome them and the ongoing challenges as we prepare for future demands.

Öztürk, Hasan [CERN]↗
NASA NTRS1990

The COLD-SAT Experiment for Cryogenic Fluid Management Technology

Future national space transportation missions will depend on the use of cryogenic fluid management technology development needs for these missions. In-space testing will be conducted in order to show low gravity cryogenic fluid management concepts and to acquire a technical data base. Liquid H2 is the preferred test fluid due to its propellant use. The design of COLD-SAT (Cryogenic On-orbit Liquid Depot Storage, Acquisition, and Transfer Satellite), an Expendable Launch Vehicle (ELV) launched orbital spacecraft that will perform subcritical liquid H2 storage and transfer experiments under low gravity conditions is studied. An Atlas launch vehicle will place COLD-SAT into a circular orbit, and the 3-axis controlled spacecraft bus will provide electric power, experiment control, and data management, attitude control, and propulsive accelerations for the experiments. Low levels of acceleration will provide data on the effects that low gravity might have on the heat and mass transfer processes used. The experiment module will contain 3 liquid H2 tanks; fluid transfer, pressurization and venting equipment; and instrumentation.

Schuster, J. R.↗
NASA NTRS2019

Departure Approval Request Compliance Effects on Overhead Stream Insertion

Integrated Arrival-Departure-Surface (IADS) air traffic management relies on information sharing and integration between automation systems for efficiency, predictability, and throughput improvements throughout the National Airspace System (NAS). The NASA Airspace Technology Demonstration 2 (ATD-2) project supports this effort by integrating SWIM (System Wide Information Management) information and evaluating and refining potential TFDM (Terminal Flight Data Manager) functionality in a surface traffic management system fielded at Charlotte-Douglas International Airport (CLT). Among the IADS areas under study during ATD-2 is the use of electronically negotiated departure times for efficiently inserting flights into overhead traffic streams. Stream-insertion accuracy is important for reducing excessive vectoring that negatively impacts the NAS by increasing controller workload along with aircraft fuel burn and emissions. Departure-time approval requests (APREQs) provide center-approved departure times to allow for smooth stream insertion. In this presentation, TBFM (Time-Based Flow Management) and ATD-2 flight summary data are used to empirically show that APREQ compliance leads to improved stream insertion.

Overhead-Stream Insertion↗
NASA NTRSDate not supplied

Space-based Sensor for Extreme Fire Weather Events

Catastrophic bushfires are becoming increasingly prevalent as climate change advances. Impacts extend beyond national borders. Multinational efforts can inform new science and management practices. Space-based sensors and integrated data facilities will play an important role. This paper describes a collaborative project between a consortium of Australian universities and NASA Centers to develop and implement a small satellite platform comprising highly integrated thermal and lightning sensors coupled with AI-based edge computing to help predict, detect, and track bushfires, supporting mitigation activities. This will fill an important capability gap since Australia does not currently have any sovereign Earth observation satellites. This program is enabled by and builds on Australia-NASA collaboration and will also support fire science and management activities in the broader global context.

wildfire↗
NASA NTRSDate not supplied

Linear Regression Model for Predictive Service Provider Selection

The increasing number of satellites in orbit has led to a growing reliance on third-party service providers for data transfer between Earth and space. Traditional approaches to managing satellite communications require human intervention, which becomes more burdensome with the escalating number of satellites. This research addresses the need for an efficient and automated system to optimize service provider selection for NASA space communication. Previous research has utilized human-operated approaches for service provider management. Our study fills a gap by developing a cognitive algorithm that automates and optimizes the selection process based on various parameters, such as data volume, priority, quality of service and cost. This novel solution reduces user burden, facilitates service management, and contributes to the development of cognitive spaceflight missions, ultimately supporting NASA’s research into Cognitive Communications technology. The algorithm design consists of three major steps: modeling data, developing a Link Selection Algorithm (LSA) based on a grading system, and applying machine learning using linear regression. The LSA evaluates providers based on user-defined constraints, considering factors such as delivery time, cost, and quality of service. We define a suitability metric which allows our algorithm to make a recommendation to a user regarding which commercial service providers to select. The addition of Linear Regression predicts the future suitability value. Our main findings demonstrate that the resulting algorithm can autonomously manage connections between satellites and providers, maximizing communication channel efficiency. This research has significant implications, as it not only addresses a pressing issue in satellite communication management but also advances the field of cognitive spaceflight missions.

Linear regression↗
NASA NTRS1981

Appropriateness in using LANDSAT in development energy related data bases

The use of automated classification systems in the field of resource management and resource inventory is discussed. Applications of LANDSAT classification are outlined and include: energy load forecasting based upon land use inventories and change analysis, impact analysis of activities related to energy extraction, capability/suitability mapping in support of generation and substation location and transmission line routing, and assessment of solar energy potential in a highly urbanized setting where land values are high. It is found that the use of LANDSAT data is adequate for general inventories where few data categories are required, where resolution of data to around 150 acres minimum is required, and where no other complete imagery set can be obtained.

Harnden, E.↗
NASA NTRS1979

Integrated use of Landsat data for state resource management

The study deals with the integration of a practical information resource - data from the earth orbiting satellite Landsat - with alternate forms of data to structure a state resource data base. State use of Landsat data within a comprehensive resource management scheme is examined. The efforts of NASA in the transfer of this space technology to state resource management applications are outlined. The role two nongovernmental sectors, universities and private industry, play in assisting states to develop Landsat data analysis capability is described.

Schneider, W. G., Jr.↗
NASA NTRSDate not supplied

Data Fusion With Uncertainty Quantification for Sub-City-Scale Air Quality Assessment and Forecasting

Many information sources can support air quality assessment and forecasting, including atmospheric chemistry model outputs, satellite retrievals of column chemical and aerosol constituents, and surface-based air quality monitoring data from both regulatory and low-cost instruments. Systematic integration of these data sources provides a major opportunity to improve understanding and management of air quality, but also presents technical barriers, especially in resource- and data-constrained settings in the Global South. This presentation describes a data fusion system, currently under development using the Google Earth Engine platform, which aims to integrate the information sources listed above to support comprehensive sub-city-scale assessment and management of air quality. Furthermore, the data fusion framework includes provisions for the quantification of uncertainties in the resulting fused estimates based on the variability of and among the input data sources. These capabilities will allow air quality managers to better understand their local air quality situation, including relative confidence in the fused estimates for different constituents, locations, and times, leading to better informed air quality management decisions. This presentation covers the underlying methodology of the data fusion and uncertainty quantification approaches, provides an update on the status of its implementation, and presents early qualitative and quantitative results.

Carl Malings↗
NASA NTRS2008

2008 Year in Review

In February of 2008; NASA Stennis Space Center (SSC), NASA Kennedy Space Center (KSC), and The Applied Research Laboratory at Penn State University demonstrated a pilot implementation of an Integrated System Health Management (ISHM) capability at the Launch Complex 20 of KSC. The following significant accomplishments are associated with this development: (1) implementation of an architecture for ground operations ISHM, based on networked intelligent elements; (2) Use of standards for management of data, information, and knowledge (DIaK) leading to modular ISHM implementation with interoperable elements communicating according to standards (three standards were used: IEEE 1451 family of standards for smart sensors and actuators, Open Systems Architecture for Condition Based Maintenance (OSA-CBM) standard for communicating DIaK describing the condition of elements of a system, and the OPC standard for communicating data); (3) ISHM implementation using interoperable modules addressing health management of subsystems; and (4) use of a physical intelligent sensor node (smart network element or SNE capable of providing data and health) along with classic sensors originally installed in the facility. An operational demonstration included detection of anomalies (sensor failures, leaks, etc.), determination of causes and effects, communication among health nodes, and user interfaces.

Figueroa, Jorge Fernando↗
NASA NTRS2001

PRMS Data Warehousing Prototype

Project and Resource Management System (PRMS) is a web-based, mid-level management tool developed at KSC to provide a unified enterprise framework for Project and Mission management. The addition of a data warehouse as a strategic component to the PRMS is investigated through the analysis design and implementation processes of a data warehouse prototype. As a proof of concept, a demonstration of the prototype with its OLAP's technology for multidimensional data analysis is made. The results of the data analysis and the design constraints are discussed. The prototype can be used to motivate interest and support for an operational data warehouse.

Guruvadoo, Eranna K.↗
NASA NTRS2002

PRMS Data Warehousing Prototype

Project and Resource Management System (PRMS) is a web-based, mid-level management tool developed at KSC to provide a unified enterprise framework for Project and Mission management. The addition of a data warehouse as a strategic component to the PRMS is investigated through the analysis, design and implementation processes of a data warehouse prototype. As a proof of concept, a demonstration of the prototype with its OLAP's technology for multidimensional data analysis is made. The results of the data analysis and the design constraints are discussed. The prototype can be used to motivate interest and support for an operational data warehouse.

Guruvadoo, Eranna K.↗
NASA NTRS2000

Enhancement of the NMSU Channel Error Simulator to Provide User-Selectable Link Delays

This is the third in a continuing series of reports describing the development of the Space-to-Ground Link Simulator (SGLS) to be used for testing data transfers under simulated space channel conditions. The SGLS is based upon Virtual Instrument (VI) software techniques for managing the error generation, link data rate configuration, and, now, selection of the link delay value. In this report we detail the changes that needed to be made to the SGLS VI configuration to permit link delays to be added to the basic error generation and link data rate control capabilities. This was accomplished by modifying the rate-splitting VIs to include a buffer the hold the incoming data for the duration selected by the user to emulate the channel link delay. In sample tests of this configuration, the TCP/IP(sub ftp) service and the SCPS(sub fp) service were used to transmit 10-KB data files using both symmetric (both forward and return links set to 115200 bps) and unsymmetric (forward link set at 2400 bps and a return link set at 115200 bps) link configurations. Transmission times were recorded at bit error rates of 0 through 10(exp -5) to give an indication of the link performance. In these tests. we noted separate timings for the protocol setup time to initiate the file transfer and the variation in the actual file transfer time caused by channel errors. Both protocols showed similar performance to that seen earlier for the symmetric and unsymmetric channels. This time, the delays in establishing the file protocol also showed that these delays could double the transmission time and need to be accounted for in mission planning. Both protocols also showed a difficulty in transmitting large data files over large link delays. In these tests, there was no clear favorite between the TCP/IP(sub ftp) and the SCPS(sub fp). Based upon these tests, further testing is recommended to extend the results to different file transfer configurations.

Horan, Stephen↗