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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
NASA NTRS2018

Initial Approach to Collect Small Unmanned Aircraft System Off-Nominal Operational Situations Data

NASA is developing the Unmanned Aircraft System Traffic Management research platform to safely integrate small unmanned aircraft operations in large-scale at low-altitudes. As a part of this effort, small unmanned aircraft system off-nominal operational situations data collection process has been developed to take lessons learned and to reinforce operational compliance. In this paper, descriptions of variables used for digital data collection and an online report form for collection of observational data from the operators (contextual data) are provided. They are used to collect off-nominal data from the Unmanned Aircraft System Traffic Management National Campaign in 2017. The digital data show that 2 out of 118 campaign operations (1.7%) encountered loss of navigation. Since the campaign aircraft used Global Positioning System for navigation, it is likely that unobstructed view of the sky at the campaign locations contributed to this small number. Also, 4 out of 47 operations (8.5%) encountered loss of communications. A relatively short distance between ground control system and aircraft, ranging from 2300 feet to 4200 feet, likely contributed to this small number. There was no data to identify the loss of communications condition, aircraft received signal strength, for the remaining 71 operations suggesting that some operators may not be monitoring unmanned aircraft communications system performance or monitoring it with different parameters. For the contextual data, due to the low number of total reports during the campaign, no significant trends emerged. This is an initial attempt to collect contextual data from small unmanned aircraft operators about off-nominal situations, and changes will be made to the future data collection to improve the amount and quality of the information.

Jung, Jaewoo↗
NASA NTRS1974

Spaceflight tracking and data network operational reliability assessment for Skylab

Data on the spaceflight communications equipment status during the Skylab mission were subjected to an operational reliability assessment. Reliability models were revised to reflect pertinent equipment changes accomplished prior to the beginning of the Skylab missions. Appropriate adjustments were made to fit the data to the models. The availabilities are based on the failure events resulting in the stations inability to support a function of functions and the MTBF's are based on all events including 'can support' and 'cannot support'. Data were received from eleven land-based stations and one ship.

Seneca, V. I.↗
NASA NTRS2000

NASA Goddard Space Flight Center

The NASA SLR Operational Center is responsible for: 1) NASA SLR network control, sustaining engineering, and logistics; 2) ILRS mission operations; and 3) ILRS and NASA SLR data operations. NASA SLR network control and sustaining engineering tasks include technical support, daily system performance monitoring, system scheduling, operator training, station status reporting, system relocation, logistics and support of the ILRS Networks and Engineering Working Group. These activities ensure the NASA SLR systems are meeting ILRS and NASA mission support requirements. ILRS mission operations tasks include mission planning, mission analysis, mission coordination, development of mission support plans, and support of the ILRS Missions Working Group. These activities ensure than new mission and campaign requirements are coordinated with the ILRS. Global Normal Points (NP) data, NASA SLR FullRate (FR) data, and satellite predictions are managed as part of data operations. Part of this operation includes supporting the ILRS Data Formats and Procedures Working Group. Global NP data operations consist of receipt, format and data integrity verification, archiving and merging. This activity culminates in the daily electronic transmission of NP files to the CDDIS. Currently of all these functions are automated. However, to ensure the timely and accurate flow of data, regular monitoring and maintenance of the operational software systems, computer systems and computer networking are performed. Tracking statistics between the stations and the data centers are compared periodically to eliminate lost data. Future activities in this area include sub-daily (i.e., hourly) NP data management, more stringent data integrity tests, and automatic station notification of format and data integrity issues.

Carter, David↗
NASA NTRS1985

Systematic analysis of EOS data system for operations

A data management analysis methodology is being proposed. The objective of the methodology is to assist mission managers by identifying a series of ordered activities to be systematically followed in order to arrive at an effective ground system design. Existing system engineering tools and concepts have been assembled into a structured framework to facilitate the work of a mission planner. It is intended that this methodology can be gainfully applied (with probable modifications and/or changes) to the EOS payloads and their associated data systems.

Moe, K. L.↗
DOE OSTI2021

Wind Plant Performance Prediction Benchmark Phase 1 (Technical Report)

Financial risk resulting from the uncertainty associated with developing, owning, and operating wind power plants remains a barrier to reducing the levelized cost of energy (LCOE). On average, modern wind power plants in the U.S. underperform their expected annual energy output by 3.5-4.5% , with many underperforming by over 10%. To compensate for this uncertainty, investors require a larger return on investment (ROI) and apply "knock-down" factors that mask much of the underlying sources of uncertainty. Wind energy projects thus have reduced access to low-cost capital. Furthermore, operating wind plants often take a simple approach to estimating operations & maintenance (O&M) costs (e.g. straight-line estimates based on similar plants), which can eat into profits. To overcome these issues, the wind industry must improve the models they use for estimating wind plant performance and operations. An industry consortium (IC) requested that the National Renewable Energy Laboratory (NREL) lead a Department of Energy (DOE) working group to benchmark the accuracy of wind power plant energy predictions against real operational data. The IC was also motivated by DOE and NREL's potential to characterize systematic energy underperformance, identify sources of uncertainty, and explore root causes. The Wind Plant Performance Prediction (WP3) project was created out of this request, and this report represents the successful completion of Phase 1 of the WP3 project. During the project, wind plant owners provided both pre-construction and operational data to NREL. The pre-construction data was provided to wind resource assessment (WRA) consultants so they could conduct energy yield assessments (EYA). NREL took all of the completed EYAs, along with the operational data, and conducted an operational assessment to benchmark the EYA results against actual operational data. Given the large amounts of sensitive data required for this effort, as well as historical opposition to sharing data within industry, successful completion of Phase 1 represents an unprecedented milestone for industry data sharing. To improve the accuracy and confidence of pre-construction EYAs, wind plant owners and investors need better, more certain, energy yield predictions. The WP3 Benchmark Project is an industry-driven response to this reality. For the first time, industry has taken the important step of working together at scale, sharing valuable operational data with DOE and NREL in order to investigate the sources of bias and uncertainty in these energy estimates. This IC provides wind plant preconstruction and operational data to NREL in an organized and documented fashion and provides guidance and feedback as needed. The IC also provides introspection of the design of experiment, key metrics of success, data challenges, analysis best practices, and quality of results.

17 WIND ENERGY↗
DOE OSTI2024

Learning Nonlinear Reduced Models from Data with Operator Inference

This review discusses Operator Inference, a nonintrusive reduced modeling approach that incorporates physical governing equations by defining a structured polynomial form for the reduced model, and then learns the corresponding reduced operators from simulated training data. The polynomial model form of Operator Inference is sufficiently expressive to cover a wide range of nonlinear dynamics found in fluid mechanics and other fields of science and engineering, while still providing efficient reduced model computations. The learning steps of Operator Inference are rooted in classical projection-based model reduction; thus, some of the rich theory of model reduction can be applied to models learned with Operator Inference. This connection to projection-based model reduction theory offers a pathway toward deriving error estimates and gaining insights to improve predictions. Furthermore, through formulations of Operator Inference that preserve Hamiltonian and other structures, important physical properties such as energy conservation can be guaranteed in the predictions of the reduced model beyond the training horizon. This review illustrates key computational steps of Operator Inference through a large-scale combustion example.

Mechanics↗
DOE OSTI2022

Natural Language Processing-Enhanced Nuclear Industry Operating Experience Data Analysis to Support Risk Model Parameter Estimations

This set of slides has been prepared for a talk at the INL AI/ML Symposium held on September 8, 2022. Presentation outline: Background - Nuclear power plant operating experience data sources • Research focus and motivation - Analyzing free-text operating experience data: present and future • Research method - Input - Methodological steps - Output • Conclusions and next steps

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Cyber Secure Sensor Network for Fossil Fuel Power Generation Assets Monitoring

The energy sector is undergoing digital transformation which is to say more and more power generation assets have become automated and connected to the internet. The connected sensors can tap into plants to monitor the health of assets and manage fleets remotely. These are just some of the benefits digitalization is bringing to the power industry. Within a power plant, control systems are no longer concerned with one system or one piece of equipment, but rather whole fleet of assets inter-connected with smart sensors which have the function of continuously monitoring and transmitting real-time operational data to operators. These connected systems will form a part of the industrial internet of things (IIoT). The big data scenarios provide benefits of performing system prognostics and optimization which is a key selling point of power plant digitalization.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS1991

NASA Customer Data and Operations System

In addition to the currently provided NASA services such as Communications and Tracking and Data Relay Satellite System services, the NASA's Customer Data and Operations System (CDOS) will provide the following services to the user: Data Delivery Service, Data Archive Service, and CDOS Operations Management Service. This paper describes these services in detail and presents respective block diagrams. The CDOS services will support a variety of multipurpose missions simultaneously with centralized and common hardware and software data-driven systems.

Butler, Madeline J.↗
DOE OSTI2022

Data-Driven Operator Theoretic Methods for Phase Space Learning and Analysis

This paper uses data-driven operator theoretic approaches to explore the global phase space of a dynamical system. In this work, we defined conditions for discovering new invariant subspaces in the state space of a dynamical system starting from an invariant subspace based on the spectral properties of the Koopman operator. When the system evolution is known locally in several invariant subspaces in the state space of a dynamical system, a phase space stitching result is derived that yields the global Koopman operator. Additionally, in the case of equivariant systems, a phase space stitching result is developed to identify the global Koopman operator using the symmetry properties between the invariant subspaces of the dynamical system and time-series data from any one of the invariant subspaces. Finally, these results are extended to topologically conjugate dynamical systems; in particular, the relation between the Koopman tuple of topologically conjugate systems is established. The proposed results are demonstrated on several second-order nonlinear dynamical systems including a bistable toggle switch. Our method elucidates a strategy for designing discovery experiments: experiment execution can be done in many steps, and models from different invariant subspaces can be combined to approximate the global Koopman operator.

42 ENGINEERING↗
NASA NTRS1976

Experiment S-213 selenocentric geodetic reference system

Development and implementation of a photogrammetric system was undertaken to provide accurate selenodetic positions and topographic mapping of all areas overflown by orbital spacecraft. The system was installed in the scientific instrument module (SIM) bay of the Apollo command service module (CSM). In theory, this system provided everything a photogrammetrist could want: the position of each exposure station would be obtained from Earth-based tracking; the orientation of each photograph could be computed from the synchronized stellar exposure and the lock-angles determined by preflight calibration; and the scale of each stereomodel would be obtained directly from the altimeter data. Operationally, the data acquisition was adequate, but less than optimum. Systematic errors are believed to be the result of the primitive orbit determination procedures in use at the time of the Apollo 15 mission, inadequate models of the lunar gravity field, and spacecraft oscillations induced by uncoupled thrusting and various activities of the astronauts.

Doyle, F. J.↗
NASA NTRS1998

Impact of Tropospheric Aerosol Absorption on Ozone Retrieval from buv Measurements

The impact of tropospheric aerosols on the retrieval of column ozone amounts using spaceborne measurements of backscattered ultraviolet radiation is examined. Using radiative transfer calculations, we show that uv-absorbing desert dust may introduce errors as large as 10% in ozone column amount, depending on the aerosol layer height and optical depth. Smaller errors are produced by carbonaceous aerosols that result from biomass burning. Though the error is produced by complex interactions between ozone absorption (both stratospheric and tropospheric), aerosol scattering, and aerosol absorption, a surprisingly simple correction procedure reduces the error to about 1%, for a variety of aerosols and for a wide range of aerosol loading. Comparison of the corrected TOMS data with operational data indicates that though the zonal mean total ozone derived from TOMS are not significantly affected by these errors, localized affects in the tropics can be large enough to seriously affect the studies of tropospheric ozone that are currently undergoing using the TOMS data.

Torres, O.↗
NASA NTRS2004

Global Aerosol Profiling by Orbital Lidar, GLAS Results and Validation

The Geoscience Laser Altimeter System (GLAS) launched in 2003 is the first polar orbiting satellite lidar. The instrument was designed for high performance observations of the distribution and optical scattering cross sections of clouds and aerosol. GLAS is approaching six months of on orbit data operation. These data from thousands of orbits illustrate the ability of space lidar to accurately and dramatically measure the height distribution of global aerosol to an unprecedented degree. There were many intended science applications of the GLAS data and significant results have already been realized, profiling is a fundamentally new measurement from space with multiple applications. A most important aerosol application is providing input to global aerosol generation transport models. Another is improved measurement of aerosol optical depth. A main approach to verify the aerosol optical depth retrieval is comparison to surface measurements by Aeronet. A special feature of the GLAS satellite bus is to rapidly point the lidar instrument at off nadir targets with less than 100 m accuracy. About a dozen selected Aeronet sites were pointed at whenever the GLAS lidar came within 5 degrees of zenith. These plus a more general comparison to nearby sites support the GLAS data product values. In addition the GLAS data can be used to add vertical distribution information to Aeronet aerosol measurements. As an EOS project instrument, GLAS data products are openly available to the science community. First year results from GLAS are summarized.

Spinhirne, J. D.↗
NASA NTRS2004

Cloud and Aerosol Measurements from the GLAS Polar Orbiting Lidar: First Year Results

The Geoscience Laser Altimeter System (GLAS) launched in 2003 is the first polar orbiting satellite lidar. The instrument was designed for high performance observations of the distribution and optical scattering cross sections of clouds and aerosol. GLAS is approaching six months of on orbit data operation. These data from thousands of orbits illustrate the ability of space lidar to accurately and dramatically measure the height distribution of global cloud and aerosol to an unprecedented degree. There were many intended science applications of the GLAS data and significant results have already been realized. One application is the accurate height distribution and coverage of global cloud cover with one goal of defining the limitation and inaccuracies of passive retrievals. Comparison to MODIS cloud retrievals shows notable discrepancies. Initial comparisons to NOAA 14&15 satellite cloud retrievals show basic similarity in overall cloud coverage, but important differences in height distribution. Because of the especially poor performance of passive cloud retrievals in polar regions, and partly because of high orbit track densities, the GLAS measurements are by far the most accurate measurement of Arctic and Antarctica cloud cover from space to date. Global aerosol height profiling is a fundamentally new measurement from space with multiple applications. A most important aerosol application is providing input to global aerosol generation and transport models. Another is improved measurement of aerosol optical depth. Oceanic surface energy flux derivation from PBL and LCL height measurements is another application of GLAS data that is being pursued. A special area of work for GLAS data is the correction and application of multiple scattering effects. Stretching of surface return pulses in excess of 40 m from cloud propagation effects and other interesting multiple scattering phenomena have been observed. As an EOS project instrument, GLAS data products are openly available to the science community. First year results from GLAS are summarized.

Spinhirne, J. D.↗
NASA NTRS2004

Global Aerosol Profiling by Orbital Lidar, GLAS Results and Validation

The Geoscience Laser Altimeter System (GLAS) launched in 2003 is the first polar orbiting satellite lidar. The instrument was designed for high performance observations of the distribution and optical scattering cross sections of clouds and aerosol. GLAS is approaching six months of on orbit data operation. These data from thousands of orbits illustrate the ability of space lidar to accurately and dramatically measure the height distribution of global aerosol to an unprecedented degree. There were many intended science applications of the GLAS data and significant results have already been realized. profiling is a fundamentally new measurement from space with multiple applications. A most important aerosol application is providing input to global aerosol generation and transport models. Another is improved measurement of aerosol optical depth. A main approach to verify the aerosol optical depth retrieval is comparison to surface measurements by Aeronet. A special feature of the GLAS satellite bus is to rapidly point the lidar instrument at off nadir targets with less than 100 m accuracy. About a dozen selected Aeronet sites were pointed at whenever the G U S lidar came within 5 degrees of zenith. These plus a more general comparison to nearby sites support the G U S data product values. In addition the GUS data can be used to add vertical distribution information to Aeronet aerosol measurements.. As an EOS project instrument, GLAS data products are openly available to the science community. First year results from G U S are summarized.

Spinhirne, J. D.↗
NASA NTRS2016

Pitfalls and Precautions When Using Predicted Failure Data for Quantitative Analysis of Safety Risk for Human Rated Launch Vehicles

Launch vehicle reliability analysis is largely dependent upon using predicted failure rates from data sources such as MIL-HDBK-217F. Reliability prediction methodologies based on component data do not take into account system integration risks such as those attributable to manufacturing and assembly. These sources often dominate component level risk. While consequence of failure is often understood, using predicted values in a risk model to estimate the probability of occurrence may underestimate the actual risk. Managers and decision makers use the probability of occurrence to influence the determination whether to accept the risk or require a design modification. The actual risk threshold for acceptance may not be fully understood due to the absence of system level test data or operational data. This paper will establish a method and approach to identify the pitfalls and precautions of accepting risk based solely upon predicted failure data. This approach will provide a set of guidelines that may be useful to arrive at a more realistic quantification of risk prior to acceptance by a program.

Hatfield, Glen S.↗
DOE OSTI2026

Operating Experience Data Analysis for Digital Instrumentation and Control System Reliability and Risk Assessment in Nuclear Power Plants

The implementation of advanced digital instrumentation and control (DI&C) systems in U.S. nuclear power plants (NPPs) can bring significant advancements in reliability, monitoring, and control capabilities. However, these systems also introduce new challenges, particularly in assessing risks such as common-cause failures (CCFs) and establishing robust reliability estimates for DI&C components. Addressing these challenges is critical for ensuring the safe and efficient operation of NPPs. Recently, Idaho National Laboratory was tasked by the U.S. Nuclear Regulatory Commission (NRC) to conduct a DI&C reliability study using operating experience data from the nuclear industry. The two operating experience data sources for the study are the Institute of Nuclear Power Operations’ Industry Reporting and Information System (IRIS) and the NRC’s Licensee Event Report database which is hosted at Idaho National Laboratory at https://lersearch.inl.gov/LERSearchCriteria.aspx. This report provides a comprehensive examination of DI&C systems, including their architecture, operational advantages, and associated challenges. It reviews existing industry DI&C studies and failure mode taxonomies, along with reliability data from various industries. Through a detailed analysis of these databases, the study provides insights into DI&C system performance. Considerations should be given to incorporate DI&C failure data into the NRC's Integrated Data Collection and Coding System and updating the Reliability and Availability Data System to support ongoing DI&C reliability studies. Recommendations are also provided for modeling DI&C reliability and CCF in probabilistic risk assessment, thereby supporting risk-informed decision-making and enhancing the reliability and safety of NPPs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗