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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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The probability of Ni-Cd battery survival was determined by researching survival data on cells fabricated from 1964-1977. A log of cell failure times were plotted against cumulative failure percentage and mean and standard deviation were determined.
The design of a real-time magnetic tape I/O system using automatic error recovery and data spooling is discussed. Real time requirements (data acquired at one rate and recorded at another by queing) must be met and data integrity must be provided regardless of tape failures or I/O errors. Automatic blocking services should be used for multiple local records storage. Avoidance of operational delays and loss of data is essential, noting the problems of the tape drives of the NASA Langley cryogenic wind tunnel. A series of block diagrames and flow charts are included.
As dynamic line ratings (DLR) are used to support different use cases, the preferred data source varies according to the forecast horizon. The below guidance is one possibility to utilize weather forecasting. Note that the time-periods of forecasts and spatial resolution may be subject to change as National Oceanic and Atmospheric Administration (NOAA)/National Weather Service (NWS) periodically upgrades its forecast models and data servers. Moreover, Persistence/ML Observations advances in analytics may justify combining data sources for more reliable DLR forecasts. The list below gives a time period (t) followed by possible guidance for data in that interval.
During the development of data base software for a multi-sensor tracking system, reliability was measured. The failure ratio and failure rate were found to be consistent measures. Trend lines were established from these measurements that provided good visualization of the progress on the job as a whole as well as on individual modules. Over one-half of the observed failures were due to factors associated with the individual run submission rather than with the code proper. Possible application of these findings for line management, project managers, functional management, and regulatory agencies is discussed. Steps for simplifying the measurement process and for use of these data in predicting operational software reliability are outlined.
Hydroelectric generation and water storage have long been components of the clean energy mix, providing both reliable steady output and operational flexibility. As variable renewable energy sources such as wind and solar increasingly replace traditional generation, the role of all flexible resources—including hydropower—in balancing supply and demand continues to evolve. This study examined the contribution of the Lower Snake River (LSR) Dam plants to Bonneville Power Administration grid services in maintaining power system reliability within the Western Interconnection. By analyzing publicly available data, the study evaluated various reliability services through performance metrics including energy capacity, balancing and ramping, voltage and reactive power support, frequency response, and transmission impact. Results indicated that the LSR plants deliver services as expected based on their size, contributing to the balancing process, ramping capabilities, and operational reserves, particularly during peak load conditions and weather events, while also providing measurable frequency and voltage support to the grid.
Clustering algorithms can identify groups in large data sets, such as star catalogs and hyperspectral images. In general, clustering methods cannot analyze items that have missing data values. Common solutions either fill in the missing values (imputation) or ignore the missing data (marginalization). Imputed values are treated as just as reliable as the truly observed data, but they are only as good as the assumptions used to create them. In contrast, we present a method for encoding partially observed features as a set of supplemental soft constraints and introduce the KSC algorithm, which incorporates constraints into the clustering process. In experiments on artificial data and data from the Sloan Digital Sky Survey, we show that soft constraints are an effective way to enable clustering with missing values.
Orbiting Geophysical Observatory 5 (OGO 5) magnetospheric ion-composition data (H+, He+ and O+) from an ion spectrometer (Sharp, 1969) have been retrieved from old magnetic tapes archived at the National Space Science Data Center (NSSDC). The highly compressed binary format was converted into a user-friendly ASCII format and these data have been made available online. We have inspected reliability and consistency of this data set in state of the art current knowledge. Comparing with the climatological model IRI-2012 and the mathematical model FLIP a shift of absolute and relative ion densities with time was revealed. We have suggested a correction procedure of individual H+, He+ and O+ ion densities. Using the corrected data set, we investigated plasmapause locations based on density gradient in H+, and He+. Correlation coefficient of both locations was determined as approx. 0.886 and the typical difference (Delta)L approx. 0.1. The electron density at the He+ plasmapause location for all cases is >100/cu cm.
Investigations of unsteady pressure loadings on the blades of fans operating near the stall flutter boundary are carried out under simulated conditions in the NASA Transonic Flutter Cascade facility (TFC). It has been observed that for inlet Mach numbers of about 0.8, the cascade flowfield exhibits intense low-frequency pressure oscillations. The origins of these oscillations were not clear. It was speculated that this behavior was either caused by instabilities in the blade separated flow zone or that it was a tunnel resonance phenomenon. It has now been determined that the strong low-frequency oscillations, observed in the TFC facility, are not a cascade phenomenon contributing to blade flutter, but that they are solely caused by the tunnel resonance characteristics. Most likely, the self-induced oscillations originate in the system of exit duct resonators. For sure, the self-induced oscillations can be significantly suppressed for a narrow range of inlet Mach numbers by tuning one of the resonators. A considerable amount of flutter simulation data has been acquired in this facility to date, and therefore it is of interest to know how much this tunnel self-induced flow oscillation influences the experimental data at high subsonic Mach numbers since this facility is being used to simulate flutter in transonic fans. In short, can this body of experimental data still be used reliably to verify computer codes for blade flutter and blade life predictions? To answer this question a study on resonance effects in the NASA TFC facility was carried out. The results, based on spectral and ensemble averaging analysis of the cascade data, showed that the interaction between self-induced oscillations and forced blade motion oscillations is very weak and can generally be neglected. The forced motion data acquired with the mistuned tunnel, when strong self-induced oscillations were present, can be used as reliable forced pressure fluctuations provided that they are extracted from raw data sets by an ensemble averaging procedure.
The rapid growth of hyperscale AI data centers is bringing renewed attention to the reliability risk that sustained forced oscillations pose to bulk power systems, with cyclic computational workloads emerging as a new forcing source. Unlike the broadband, stochastic disturbances from traditional industrial loads such as arc furnaces, AI training and inference facilities can inject large active power swings concentrated at specific frequencies over extended durations - characteristics that existing grid planning practices do not account for. While the North American Electric Reliability Corporation (NERC) has recognized this gap and called for system-level studies of large load interconnections, no standardized methodology exists to screen, simulate, and quantify these risks at the planning stage. This report presents the Risk Assessment Tool for Large Load-induced Events (RATLLE), a Python-based, publicly available script suite developed at the Pacific Northwest National Laboratory to evaluate bulk power system reliability risks from data center-induced oscillations. RATLLE implements a three-module workflow: a screening module that identifies vulnerable interconnection locations and excitable system modes; a simulation module that models cyclic data center load behavior using a commercial positive sequence simulation platform; and an analysis module that computes risk metrics and generates interactive visualization dashboards. The risk metrics, formulated around simulation observables, map oscillation impacts to a three-stage severity scale spanning latent equipment fatigue through imminent cascading failure. The methodology is demonstrated on two Western Electricity Coordinating Council (WECC) system models: a publicly available 240-bus reduced representation and a detailed 2031 Heavy Winter planning case. Case studies illustrate that even modest 50 MW forced oscillations at resonant frequencies can produce wide-area power swings, N-1 security constraint violations, and cascading generator trips through protection actions - outcomes that would not occur under normal operating conditions without oscillations present. The results underscore the need for standardized oscillation impact assessment in large load interconnection studies and provide a reproducible, extensible framework for utilities to adopt or customize within their existing planning workflows.
With continued deployment of solar across the United States, assessing the interactions of solar with the power system is an increasingly important complement to studies tracking the cost and performance of solar plants. This project focuses on the historical contribution to reliability, trends in market value, and impacts on the bulk power system of solar deployed in the U.S. through the end of 2019.
With continued deployment of solar across the United States, assessing the interactions of solar with the power system is an increasingly important complement to studies tracking the cost and performance of solar plants. This project focuses on the historical contribution to reliability, trends in market value, and impacts on the bulk power system of solar deployed in the U.S. through the end of 2020. The scope of this analysis includes the seven organized U.S. wholesale power markets and is based on historical hourly solar generation profiles for each individual plant larger than 1 MW or county-level aggregate profiles for smaller solar. In addition, we present a limited set of results for ten utilities that are outside of the independent system operator (ISO)/regional transmission organization (RTO) markets.
We provide an update on the PV Fleet Performance Data Initiative at the 2026 PV Reliability Workshop. Our latest runs incorporate additional data sources and an integrated analysis pipeline run on our Kestrel HPC cluster. Initial degradation findings suggest that single-axis tracked PV systems exhibit higher performance loss rates than fixed-tilt systems, an increase of 0.5 %/yr, almost double. We discuss multiple methods for identifying stuck tracker rows, which are suspected to be a contributor to the enhanced degradation. Through satellite image detection and data-driven approaches we address the topic of identifying when stuck trackers are occuring and to what extent the problem exists. Preliminary results suggest that the increased performance loss detected for the tracked systems would be consistent with stuck tracker rows affecting on the order of 5% - 10% of the system.
Machine learning allows for efficient extraction of physical properties from stellar spectra that have been obtained by large surveys. The viability of machine-learning approaches has been demonstrated for spectra covering a variety of wavelengths and spectral resolutions, but most often for main-sequence (MS) or evolved stars, where reliable synthetic spectra provide labels and data for training. Spectral models of young stellar objects (YSOs) and low-mass MS stars are less well-matched to their empirical counterparts, however, posing barriers to previous approaches to classify spectra of such stars. In this work, we generate labels for YSOs and low-mass MS stars through their photometry. We then use these labels to train a deep convolutional neural network to predict logg, T {sub eff}, and Fe/H for stars with Apache Point Observatory Galactic Evolution Experiment (APOGEE) spectra in the DR14 data set. This “APOGEE Net” has produced reliable predictions of logg for YSOs, with uncertainties of within 0.1 dex and a good agreement with the structure indicated by pre-MS evolutionary tracks, and it correlates well with independently derived stellar radii. These values will be useful for studying pre-MS stellar populations to accurately diagnose membership and ages.
The author has identified the following significant results. Bands 4, 5, and 7 and 5, 6, and 7 were best for distinguishing among crop and soil categories in ERTS-1 SCENES 1182-16322 (1-21-73) and 1308-16323 (5-21-73) respectively. Chlorotic sorghum areas 2.8 acres or larger in size were identified on a computer printout of band 5 data. Reflectance of crop residues was more often different from bare soil in band 4 than in bands 5, 6, and 7. Simultaneously acquired aircraft and spacecraft MSS data indicated that spacecraft surveys are as reliable as aircraft surveys. ERTS-1 data were successfully used to estimate acreage of citrus, cotton, and sorghum as well as idle crop land.
The main engines for the Future Shuttle will focus on improved safety and operability. Performance enhancements may also be required for vehicle safety purposes to achieve more desirable abort scenarios. This paper discusses the potential improvements that will be considered for implementation into the Future Shuttle. Integrated engine and vehicle health management systems will achieve additional system-level reliability improvements over those currently in development. Advanced instrumentation for detecting leaks, analyzing component wear and degradation, and providing sophisticated operational data will be used for reliable engine control and scheduling maintenance operations. A new nozzle and main combustion chamber (MCC) will reduce failure probability by 50% and allow for higher thrust capability without requiring the entire engine to be redesigned. Turbopump improvements may range from minor component improvements to using 3rd-generation pumps built on the advanced concepts demonstrated by the Integrated Powerhead Development (IPD) program and the Space Launch Initiative (SLI) prototype engines.The main engines for the Future Shuttle will focus on improved safety and operability. Performance enhancements may also be required for vehicle safety purposes to achieve more desirable abort scenarios. This paper discusses the potential improvements that will be considered for implementation into the Future Shuttle. Integrated engine and vehicle health management systems will achieve additional system-level reliability improvements over those currently in development. Advanced instrumentation for detecting leaks, analyzing component wear and degradation, and providing sophisticated operational data will be used for reliable engine control and scheduling maintenance operations. A new nozzle and main combustion chamber (MCC) will reduce failure probability by 50% and allow for higher thrust capability without requiring the entire engine to be redesigned. Turbopump improvements may range from minor component improvements to using 3rd-generation pumps built on the advanced concepts demonstrated by the Integrated Powerhead Development (IPD) program and the Space Launch Initiative (SLI) prototype engines.
An extensive effort was undertaken to edit Mariner 10 radiometric tracking data. Interactive computer graphics were used for the first time by an interplanetary mission to detect blunder points and spurious signatures in the data. Interactive graphics improved the former process time by a factor of 10 to 20, while increasing reliability. S/X dual Doppler data was used for the first time to calibrate charged particles in the tracking medium. Application of the charged particle calibrations decreased the orbit determination error for a short data arc following the 16 March 1974 maneuver by about 80%. A new model was developed to calibrate the earth's troposphere with seasonal adjustments. The new model has a 2% accuracy and is 5 times better than the old model.
The processing stage in which the restored values of the physical parameters are received is described. The following main steps are discussed: estimation of the state of the telemetry data, processing of the calibration data, and determination of the errors in the data; data decommutation and analysis of the structure of measurement cycles for each instrument; decoding, estimates of the reliability of the restored data, and their agreement with the models adopted for the measurement process; and analysis of errors due to deterministic and random factors. A block diagram of the method is presented.
The present invention provides a fail-over file transfer process to handle data file transfer when the transfer is unsuccessful in order to avoid unnecessary network congestion and enhance reliability in an automated data file transfer system. If a file cannot be delivered after attempting to send the file to a receiver up to a preset number of times, and the receiver has indicated the availability of other backup receiving locations, then the file delivery is automatically attempted to one of the backup receiving locations up to the preset number of times. Failure of the file transfer to one of the backup receiving locations results in a failure notification being sent to the receiver, and the receiver may retrieve the file from the location indicated in the failure notification when ready.