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Results for “operational data”

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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Global Change Data Center: Mission, Organization, Major Activities, and 2003 Highlights

Rapid, efficient access to Earth sciences data from satellites and ground validation stations is fundamental to the nation's efforts to understand the effects of global environmental changes and their implications for public policy. It becomes a bigger challenge in the future when data volumes increase from current levels to terabytes per day. Demands on data storage, data access, network throughput, processing power, and database and information management are increased by orders of magnitude, while budgets remain constant and even shrink.The Global Change Data Center's (GCDC) mission is to develop and operate data systems, generate science products, and provide archival and distribution services for Earth science data in support of the U.S. Global Change Program and NASA's Earth Sciences Enterprise. The ultimate product of the GCDC activities is access to data to support research, education, and public policy.

FROM↗
DOE OSTI2024

A Refined Method to Translate Solar Data Quality Assessment Flags to Estimated Measurement Uncertainty

Integrating solar resource uncertainties due to radiometer measurement performance and operational data quality assessment can provide improved estimates of economic bankability, system design performance, and compliance of solar energy conversion systems. Estimating radiometer measurement uncertainty is an established procedure consistent with recognized best practices and international guidelines. SERI QC is a robust solar data quality assessment software tool that has been in continuous use for more than three decades. This report, the fourth of six for the Data Quality and Uncertainty Integration Project, presents a refined algorithm description for software to translate solar resource data quality assessment results into estimated uncertainty values in a Solar Resource Operational Uncertainty Integrator (SROUI) application. This algorithm requires three-component solar irradiance measurements - global horizontal irradiance, direct normal irradiance, and diffuse horizontal irradiance - collected at 1- to 60-minute intervals, as described in the previous deliverables. The development of this report as Deliverable 6.4 was an iterative process that included reviews and feedback from the project team on initial drafts designed to refine how the new software could best support determining solar resource data uncertainty. The results of this effort will contribute to the final software system development by National Renewable Energy Laboratory staff.

14 SOLAR ENERGY↗
NASA NTRS1973

AE-C attitude determination and control prelaunch analysis and operations plan

A description of attitude control support being supplied by the Mission and Data Operations Directorate is presented. Included are descriptions of the computer programs being used to support the missions for attitude determination, prediction, and control. In addition, descriptions of the operating procedures which will be used to accomplish mission objectives are provided.

Werking, R. D.↗
DOE OSTI2023

Form EIA-923 Data Reformatted

Form EIA-923 data collection provides a centralized and comprehensive source for monthly operating data about the high-voltage bulk electric power grid in the Lower 48 states.

17 WIND ENERGY↗
DOE OSTI2023

Form EIA-930 Data Reformatted

Form EIA-930 data collection provides a centralized and comprehensive source for hourly operating data about the high-voltage bulk electric power grid in the Lower 48 states.

17 WIND ENERGY↗
NASA NTRS1975

Analysis of fourth sounding rocket heat pipe experiment, summary report

An analysis was made of the cryogenic axial groove methane and axial groove nitrogen heat pipes. Data cover the establishment of reliable start-up and operational data for the cryogenic pipe and its reference control pipes, and requirements for and the applicability of cryogenic heat pipes to NASA coolers.

Brennan, P. J.↗
NASA NTRS1977

Remote sensing data processing - Two years ago, today, and two years from today

Certain technical problems arising in the recent past (1975) in the field of the processing of remote sensing data are reviewed including approaches to the analysis of Landsat MSS data and technical difficulties which must be overcome to achieve operational data processing. The current status of remote sensing data processing is then examined with emphasis on such current technical issues as training selection and labeling, sampling schemes and classification and mensuration. Hardware projections are made for the near future (1979) relative to the development of remote sensing data processing.

Holmes, Q. A.↗
NASA NTRS1992

A new approach for data acquisition at the JPL space simulators

In 1990, a personal computer based data acquisition system was put into service for the Space Simulators and Environmental Test Laboratory at the Jet Propulsion Laboratory (JPL) in Pasadena, California. The new system replaced an outdated minicomputer system which had been in use since 1980. This new data acquisition system was designed and built by JPL for the specific task of acquiring thermal test data in support of space simulation and thermal vacuum testing at JPL. The data acquisition system was designed using powerful personal computers and local-area-network (LAN) technology. Reliability, expandability, and maintainability were some of the most important criteria in the design of the data system and in the selection of hardware and software components. The data acquisition system is used to record both test chamber operational data and thermal data from the unit under test. Tests are conducted in numerous small thermal vacuum chambers and in the large solar simulator and range in size from individual components using only 2 or 3 thermocouples to entire planetary spacecraft requiring in excess of 1200 channels of test data. The system supports several of these tests running concurrently. The previous data system is described along with reasons for its replacement, the types of data acquired, the new data system, and the benefits obtained from the new system including information on tests performed to date.

Fisher, Terry C.↗
NASA NTRS2005

Validation Of The Airspace Concept Evaluation System Using Real World Data

This paper discusses the process of performing a validation of the Airspace Concept Evaluation System (ACES) using real world historical flight operational data. ACES inputs are generated from select real world data and processed to create a realistic reproduction of a single day of operations within the National Airspace System (NAS). ACES outputs are then compared to real world operational metrics and delay statistics for the reproduced day. Preliminary results indicate that ACES produces delays and airport operational metrics similar to the real world with minor variations of delay by phase of flight. ACES is a nation-wide fast-time simulation tool developed at NASA Ames Research Center. ACES models and simulates the NAS using interacting agents representing center control, terminal flow management, airports, individual flights, and other NAS elements. These agents pass messages between one another similar to real world communications. This distributed agent based system is designed to emulate the highly unpredictable nature of the NAS, making it a suitable tool to evaluate current and envisioned airspace concepts. To ensure that ACES produces the most realistic results, the system must be validated. There is no way to validate future concepts scenarios using real world historical data, but current day scenario validations increase confidence in the validity of future scenario results. Each operational day has unique weather and traffic demand schedules. The more a simulation utilizes the unique characteristic of a specific day, the more realistic the results should be. ACES is able to simulate the full scale demand traffic necessary to perform a validation using real world data. Through direct comparison with the real world, models may continuee to be improved and unusual trends and biases may be filtered out of the system or used to normalize the results of future concept simulations.

Zelinski, Shannon↗
NASA NTRS1971

SSS-A attitude control prelaunch analysis and operations plan

A description of the attitude control support being supplied by the Mission and Data Operations Directorate is presented. Descriptions of the computer programs being used to support the mission for attitude determination, prediction, control, and definitive attitude processing are included. In addition, descriptions of the operating procedures which will be used to accomplish mission objectives are provided.

Werking, R. D.↗
NASA NTRS1988

Microgravity and Materials Processing Facility study (MMPF): Requirements and Analyses of Commercial Operations (RACO) preliminary data release

This requirements and analyses of commercial operations (RACO) study data release reflects the current status of research activities of the Microgravity and Materials Processing Facility under Modification No. 21 to NASA/MSFC Contract NAS8-36122. Section 1 includes 65 commercial space processing projects suitable for deployment aboard the Space Station. Section 2 contains reports of the R:BASE (TM) electronic data base being used in the study, synopses of the experiments, and a summary of data on the experimental facilities. Section 3 is a discussion of video and data compression techniques used as well as a mission timeline analysis.

Source record↗
DOE OSTI2024

Peridynamic neural operators: A data-driven nonlocal constitutive model for complex material responses

Neural operators, which can act as implicit solution operators of hidden governing equations, have recently become popular tools for learning the responses of complex real-world physical systems. Nevertheless, most neural operator applications have thus far been data-driven and neglect the intrinsic preservation of fundamental physical laws in data. Here, in this work, we introduce a novel integral neural operator architecture called the Peridynamic Neural Operator (PNO) that learns a nonlocal constitutive law from data. This neural operator provides a forward model in the form of state-based peridynamics, with objectivity and momentum balance laws automatically guaranteed. As applications, we demonstrate the expressivity and efficacy of our model in learning complex material behaviors from both synthetic and experimental data sets. We also compare the performances with baseline models that use predefined constitutive laws. We show that, owing to its ability to capture complex responses, our learned neural operator achieves improved accuracy and efficiency. Moreover, by preserving the essential physical laws within the neural network architecture, the PNO is robust in treating noisy data. The method shows generalizability to different domain configurations, external loadings, and discretizations.

42 ENGINEERING↗
NASA NTRS2016

Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation

A major focus of the commercial aviation community is discovery of unknown safety events in flight operations data. Data-driven unsupervised anomaly detection methods are better at capturing unknown safety events compared to rule-based methods which only look for known violations. However, not all statistical anomalies that are discovered by these unsupervised anomaly detection methods are operationally significant (e.g., represent a safety concern). Subject Matter Experts (SMEs) have to spend significant time reviewing these statistical anomalies individually to identify a few operationally significant ones. In this paper we propose an active learning algorithm that incorporates SME feedback in the form of rationales to build a classifier that can distinguish between uninteresting and operationally significant anomalies. Experimental evaluation on real aviation data shows that our approach improves detection of operationally significant events by as much as 75% compared to the state-of-the-art. The learnt classifier also generalizes well to additional validation data sets.

anomaly detection↗
NASA NTRS2021

CIF Report - Information Fusion and Data Analytics for Human Lunar Exploration

This project leverages the Concept Exploration Laboratory (CEL) to collect, warehouse, and augment data relevant to human lunar exploration as a platform for NA (S&MA) to develop operational data integration techniques. The project capitalizes on 16+ years of CEL experience applied to NASA, DoD, the City of Houston, the State of Texas, and private industry. The integrated data will be utilized in the two scenarios described in a definition of concept for development of a full scale data analysis suite and storage solution, useful to all JSC organizations engaged in real time operations and safety tasks, and may be useful as pathfinders for the Digital Transformation Program.

information fusion↗
DOE OSTI2021

A.I. for nuclear physics

Nuclear science is concerned with the understanding of the nature of matter, its basic constituents and their interaction to form the elements and the properties we observe. This includes the forms of matter we see around us and also exotic forms such as those that existed in the first moments after the Big Bang and that exist today inside neutron stars. The techniques, tools, and expertise needed for nuclear physics (NP) research are therefore diverse in nature. State-of-the art accelerators are being developed to illuminate the dynamical basis of the core of the atom in terms of the fundamental constituents called quarks and gluons and to increase the number of isotopes with known properties. This scientific infrastructure is reaching scales and complexities that require computational methods for tasks such as anomaly detection in operational data. New methodologies are needed to detect anomalies and to optimize operating parameters, predict failures as well as to discover new optimization algorithms.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

BEAM (Battery Ensemble ANN Modeler) [SWR-19-69]

Dynamic losses of batteries can be a nonlinear function of the operational state of the battery. This package trains an Ensemble of Artificial Neural Networks on battery operational data to model the dynamic losses as a function of the operational state of charge and the active power set point. The package can also be used to return the dynamic battery losses which will be incurred as the battery operates if fed some operational profile.

Bryce, Richard↗
DOE OSTI2022

Deep Lynx Data Historian

This software is intended to facilitate the ingestion of data from some data historian into Deep Lynx. A data historian in this instance is any location where sensor and operational data from some live asset is gathered. The data can be either manual retrieved by this software or the data historian source can push to a listening endpoint provided by this software.

Browning, JerenM↗