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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 NTRS1994

En route noise levels from propfan test assessment airplane

The en route noise test was designed to characterize propagation of propfan noise from cruise altitudes to the ground. In-flight measurements of propfan source levels and directional patterns were made by a chase plane flying in formation with the propfan test assessment (PTA) airplane. Ground noise measurements were taken during repeated flights over a distributed microphone array. The microphone array on the ground was used to provide ensemble-averaged estimates of mean flyover noise levels, establish confidence limits for those means, and measure propagation-induced noise variability. Even for identical nominal cruise conditions, peak sound levels for individual overflights varied substantially about the average, particularly when overflights were performed on different days. Large day-to-day variations in peak level measurements appeared to be caused by large day-to-day differences in propagation conditions and tended to obscure small variations arising from operating conditions. A parametric evaluation of the sensitivity of this prediction method to weather measurement and source level uncertainties was also performed. In general, predictions showed good agreement with measurements. However, the method was unable to predict short-term variability of ensemble-averaged data within individual overflights. Although variations in absorption appear to be the dominant factor in variations of peak sound levels recorded on the ground, accurate predictions of those levels require that a complete description of operational conditions be taken into account. The comprehensive and integrated methods presented in this paper have adequately predicted ground-measured sound levels. On average, peak sound levels were predicted within 3 dB for each of the three different cruise conditions.

Garber, Donald P.↗
NASA NTRS2022

Can Machine Learning Reveal Precursors of Reversals of the Geomagnetic Axial Dipole Field?

It is well known that the axial dipole part of Earth’s magnetic field reverses polarity, so that the magnetic North Pole becomes the South Pole and vice versa. The timing of reversals is well documented for the past 160 Myr, but the conditions that lead to a reversal are still not well understood. It is not known if there are reliable ‘precursors’ of reversals (events that indicate that a reversal is upcoming) or what they might be. We investigate if machine learning (ML) techniques can reliably identify precursors of reversals based on time-series of the axial magnetic dipole field. The basic idea is to train a classifier using segments of time-series of the axial magnetic dipole. This training step requires modification of standard ML techniques to account for the fact that we are interested in rare events—a reversal is unusual, while a non-reversing field is the norm. Without our tweak, the ML classifiers lead to useless predictions. Perhaps even more importantly, the usable observational record is limited to 0–2 Ma and contains only five reversals, necessitating that we determine if the data are even sufficient to reliably train and validate an ML algorithm. To answer these questions we use several ML classifiers (linear/non-linear support vector machines and long short-term memory networks), invoke a hierarchy of numerical models (from simplified models to 3-D geodynamo simulations), and two palaeomagnetic reconstructions (PADM2M and Sint-2000). The performance of the ML classifiers varies across the models and the observational record and we provide evidence that this is not an artefact of the numerics, but rather reflects how ‘predictable’ a model or observational record is. Studying models of Earth’s magnetic field via ML classifiers thus can help with identifying shortcomings or advantages of the various models. For Earth’s magnetic field, we conclude that the ability of ML to identify precursors of reversals is limited, largely due to the small amount and low frequency resolution of data, which makes training and subsequent validation nearly impossible. Put simply: the ML techniques we tried are not currently capable of reliably identifying an axial dipole moment (ADM) precursor for geomagnetic reversals. This does not necessarily imply that such a precursor does not exist, and improvements in temporal resolution and length of ADM records may well offer better prospects in the future.

Kyle Gwirtz↗
NASA NTRS1977

Model predictions of wind and turbulence profiles associated with an ensemble of aircraft accidents

The feasibility of predicting conditions under which wind/turbulence environments hazardous to aviation operations exist is studied by examining a number of different accidents in detail. A model of turbulent flow in the atmospheric boundary layer is used to reconstruct wind and turbulence profiles which may have existed at low altitudes at the time of the accidents. The predictions are consistent with available flight recorder data, but neither the input boundary conditions nor the flight recorder observations are sufficiently precise for these studies to be interpreted as verification tests of the model predictions.

Williamson, G. G.↗
NASA NTRS1986

Predicting the magnetospheric plasma of weather

The prediction of the plasma environment in time, the plasma weather, is discussed. It is important to be able to predict when large magnetic storms will produce auroras, which will affect the space station operating in low orbit, and what precautions to take both for personnel and sensitive control (computer) equipment onboard. It is also important to start to establish a set of plasma weather records and a record of the ability to predict this weather. A successful forecasting system requires a set of satellite weather stations to provide data from which predictions can be made and a set of plasma weather codes capable of accurately forecasting the status of the Earth's magnetosphere. A numerical magnetohydrodynamic fluid model which is used to model the flow in the magnetosphere, the currents flowing into and out of the auroral regions, the magnetopause, the bow shock location and the magnetotail of the Earth is discussed.

Dawson, John M.↗
DOE OSTI2025

Hyper Spectral Anomaly Detection

The HSA is a statistics based anomaly detection model. The model performs unsupervised anomaly detection, based on a datapoint's density and similarity within a dataset. Density and similarity data are encoded into an affinity matrix. The affinity matrix is evolved to summarize the data's structure on greater topographical scales within the data's function space. The set of evolved affinity matrices and an anomaly score vector are passed to a user defined penalized objective function. The penalized objective function of anomaly scores is then minimized. Data points where the absolute value of the z-scores of anomaly scores greater than a specified threshold are predicted as anomalies. A novel multi-filter feature has also been implemented. To reduce false positive rates, the multi-filter records the indexes of the HSA predictions. A new dataset and data loader are instantiated consisting of all the initial HSA predictions and non-anomalous data points in a 10% and 90% split respectively. The HSA is then run through this data set and a count of number of times a data point is predicted is kept. In this way the initial predictions may be compared with data spanning the entire dataset. After the multi-filter is complete, all datapoints will have an associated anomaly score, as well as a multi-filter prediction count to further filter the anomalous predictions.

Rogers, DempseyD [Idaho National Laboratory (INL),↗
NASA NTRS2005

Trends from the Long-term Data Record and Models: What Do They Tell Us About our Ability to Predict Ozone Recovery?

Our industrial society has performed an experiment on the stratospheric ozone layer over the last several decades. The initial part of this experiment was the rapidly increasing release of halogen-containing compounds that carry chlorine and bromine to the stratosphere where they can cause a loss of ozone. The present part of this experiment is the implementation of the Montreal Protocol, which has led to a leveling off of these halogen compounds and the beginning of their slow removal from the atmosphere. The observation and attribution of ozone response to the halogens has been a particularly important and difficult task because of the impact of solar cycle uv variation, two major volcanic eruptions (El Chichon and Pinatubo), and interannual dynamic variability of the stratosphere. We have run 3 different simulations of the chemistry and transport of ozone and the minor constituents that affect ozone to help evaluate our understanding of the causes of ozone change and to assess our ability to predict ozone recovery with the removal of halogens from the stratosphere. One simulation, using the Goddard chemical transport model (CTM), had interannual variability in the dynamics for the entire 50 years of simulation, which included the past 3 decades (1974-2004) and the next 2 decades to 2022. The other two simulations used the Global Modeling Initiative (GMI) CTM with no dynamical variability: one used a the winds and temperatures from a repeating warm Arctic winter and the other used a repeating cold Arctic winter. All simulations included the effects of aerosol surfaces from volcanic eruptions on chemical reactions as well as the variation in UV over the 11-year solar cycle.

Strahan, Susan↗
DOE OSTI2026

PopGNN: Graph Neural Network-Based Flexible Future Population Forecasting Model

Accurate population forecasts is important to plan critical infrastructure and services, from housing and education to healthcare and transport. However, traditional population prediction studies have only employed traditional machine learning models limited to capture complex spatial interdependencies and patterns. Althogh recently computer vision-based framework was introduced with with promising accuracy, it has critical limitations for real-world planning applications: it function only at fixed spatial resolutions, restricting their use in diverse boundaries such as census tracts, neighborhoods, or administrative zones. Therefore, this study suggests a Graph Neural Network (GNN)-based population prediction framework, called PopGNN. This model recorded remarkable performance compared with state-of-the-art models and traditional baseline models in the grid and administrative boundaries. Furthermore, our framework achieved comparable predictive accuracy to a computer vision-based model in both the South Korea and Tennessee case studies. Consequently, this study is valuable in that a single model can provide accurate population forecasts that address diverse planning demands, ranging from granular grid-level estimates for precise service allocation and facility location planning to aggregate administrative-level forecasts for macro-scale regional policy and resource distribution.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1992

Long-term particle flux variability indicated by comparison of Interplanetary Dust Experiment (IDE) timed impacts for LDEF's first year in orbit with impact data for the entire 5.75-year orbital lifetime

The electronic sensors of the Interplanetary Dust Experiment (IDE) recorded precise impact times and approximate directions for submicron to approximately 100-micron size particles on all six primary sides of the spacecraft for the first 346 days of the Long Duration Exposure Facility (LDEF) orbital mission. Previously-reported analyses of the timed impact data have established their spatio-temporal features, including the demonstration that a preponderance of the particles in this regime are orbital debris and that a large fraction of the debris particles are encountered as megameter-size clouds, some of which persist for long times. Short-term fluxes within such clouds can rise several orders of magnitude above the long-term average. These finding are consistent with the results of the first catastrophic hypervelocity laboratory impacts on a real satellite, recently reported in the press. Analysis continues on the geometric and evolutionary characteristics of these clouds, as well as on the isolation and characterization of the natural micrometeoroid component in the IDE data, but the unexpectedly large short-term variations in debris flux raises the question of how representative an indication of the multi-year average flux is given by the nearly one year of timed data. It has, therefore, always been one of the goals of IDE to conduct an optical survey of the craters on the IDE detectors, to obtain full-mission fluxes for comparisons with the timed data. This work is underway, and the results presently in hand are significant. Optical scanning of the ram and wake (East and West) panels is complete, and it is clear that the first year was in some respects not representative of the subsequent years. The 5.75-year average flux on East panel was 90 percent of the value predicted by the average flux recorded during the first year, while it was only 34 percent on West panel. This suggests that western hemisphere spacecraft launches are a major contributor to the long-term flux and that their contribution is primarily in the smaller end of the size distribution. This conclusion follows from the fact that a closely-spaced series of launch failures (Titan, Delta, Ariane, and Challenger) caused a virtual hiatus in launch activity during a large part of the later years of the LDEF mission. We hope to provide a quantification of the particle size distribution function in this case.

Mulholland, J. Derral↗
DOE OSTI2024

Data‐Driven Predictions of Peak Warming Under Rapid Decarbonization

Abstract The severe impacts associated with recent record‐setting annual global temperatures elevate the need to accurately predict the hottest conditions that could occur even if the most ambitious decarbonization goals are achieved. We use convolutional neural networks (CNNs) to predict peak global warming from recent observed temperature maps and future cumulative CO 2 emissions. For the SSP1‐1.9 decarbonization scenario there is >99% probability that mean global warming exceeds 1.5°C, approximately even odds that it reaches 2°C, and ∼90% probability that the hottest year globally exceeds 2023 by at least 0.5°C. Further, for the SSP2‐4.5 decarbonization scenario, there is >90% probability that the hottest annual global temperature anomaly is twice the 2023 anomaly. That our framework makes highly accurate out‐of‐sample predictions of the hottest historical year provides confidence in the predicted future probabilities, suggesting substantial risks from the extreme local conditions that are likely to result from globally hot years during rapid decarbonization.

Diffenbaugh, Noah S. [Doerr School of Sustainabili↗
DOE OSTI2025

Long-term calibration and validation of stability of the Auger Engineering Radio Array using the diffuse Galactic radio emission

The Auger Engineering Radio Array (AERA) measures radio emission from high-energy extensive air showers. Consisting of 153 autonomous radio-detector stations spread over 17 km^2, it detects radio waves in the frequency range of 30 to 80 MHz. Accurate characterization of the detector response is crucial for proper interpretation of the collected data. Previously, this was achieved through laboratory measurements of the analog chain and simulations and measurements of the antenna's directional response. In this paper, we perform an absolute calibration using the continuously monitored sidereal modulation of the diffuse Galactic radio emission. Calibration is done by comparing the average frequency spectra recorded by the stations with predictions from seven different models of the full radio sky, accounting for the system response, which includes the antenna, filters, and amplifiers. The analysis of the calibration constants over a period of seven years shows no relevant and no significant ageing effect in the AERA antennas. This result confirms the long-term stability of the detector stations and demonstrates the possibility for a radio detector to effectively monitor ageing effects of other detectors operating over extended periods.

Antennas↗
DOE OSTI2025

Videos, photos, and AI-derived grain size data associated with “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization” under review. This data package includes five data types: 1) raw photos and videos from drone survey and walking smartphone surveys; 2) images derived from raw videos; 3) manual labeling of reference scales; 4) metadata for all images and photo resolution derived from artificial intelligence (AI) models or manual labels, 5) grain size data obtained from AI models for all photos, 6) metadata and grain size data after quality control, 7) summaries of sample efficiency for all data, and 8) computational fluid dynamics (CFD) data used to support hydro-biogeochemical (HBGC) parameter estimation. Such data is used to 1) demonstrate significant improvements in accuracy, efficiency, and quality control for grain size data collection with the help of AI models, 2) study the spatial heterogeneity of grain size and observation reproducibility based on tens of thousands of data points generated by the AI models, and 3) evaluate the impacts of grain size heterogeneity on key HBGC parameters across sediment-to-reach and hourly-to-yearly scales. In particular, the data package contains 116 folders and 179696 files. The files include 41 videos in .mov format, 64047 photos in .jpg format, 13541 video-derived photos in .png format, 12747 segmentation mask data in .tif format, 12747 segmentation data in .json format, 24771 .csv files that with metadata and grain size for each individual photo as well as water depth and velocity data from CFD and observation, 51791 .txt files of raw AI predicted labels, and 11 flight record data in .srt format. The summary for all metadata and grain size statistics information is included in “Scales_V3_NG.csv” and “Statistics_V3_NG.csv”. The summary for data that pass data quality control (QC) level 0-2 is included in “QCStatistics_V3_NG.csv”. The QC level 0 represents photos whose photo resolution is positive, excluding photos that miss reference scale. The QC level 1 means reference scale circularity uncertainty is less than 5% for smartphone images while representing photo resolution is larger than 0.44 mm/pixel for drone images. The QC level 2 means excluding photos whose grain number is less than 100, a minimum number of grains recommended by classic literature. The summary for each video’s name, length, frame rates, survey area, grain number, survey efficiency, etc. can be found in “QCSummary_V3_NG.csv”. The summary for site name, GPS coordinates, and number of images at each site can be found in “SitesSummary_V3_*.csv” files. Overall computational efficiency summary is reported in Table 4 of accompanying manuscript. Additionally, the nitrate concentration data used in this work was downloaded from an existing dataset published on ESS-DIVE (Boat-Dragged Sensor Hanford Reach.csv; Conner A. et al., 2020). We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Port of Benton, and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the data were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate data collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1984

Comments on the BLAG model - The carbonate-silicate geochemical cycle and its effect on atmospheric carbon dioxide over the past 100 million years

A self-consistent method of determining initial conditions for the model presented by Berner, Lasaga, and Garrels (1983) (henceforth, the BLAG model) is derived, based on the assumption that the CO2 geochemical cycle was in steady state at t = -100 m.y. (million years). This initialization procedure leads to a dissolved magnesium concentration higher than that calculated by Berner, Lasaga, and Garrels and to a low ratio of dissolved calcium to bicarbonate prior to 60 m.y. ago. The latter prediction conflicts with the geologic record of evaporite deposits, which requires that this ratio remain greater than 0.5. The contradiction is probably caused by oversimplifications in the BLAG model, such as the neglect of the cycles of organic carbon and sulfur.

Kasting, J. F.↗
NASA NTRS1986

Regional-scale estimates of surface moisture availability and thermal inertia using remote thermal measurements

A review is presented of numerical models which were developed to interpret thermal IR data and to identify the governing parameters and surface energy fluxes recorded in the images. Analytic, predictive, diagnostic and empirical models are described. The limitations of each type of modeling approach are explored in terms of the error sources and inherent constraints due to theoretical or measurement limitations. Sample results of regional-scale soil moisture or evaporation patterns derived from the Heat Capacity Mapping Mission and GOES satellite data through application of the predictive model devised by Carlson (1981) are discussed. The analysis indicates that pattern recognition will probably be highest when data are collected over flat, arid, sparsely vegetated terrain. The soil moisture data then obtained may be accurate to within 10-20 percent.

Carlson, T. N.↗
NASA NTRS1997

Three-Wave Gas Journal Bearing Behavior With Shaft Runout

Experimental orbits of a free-mounted, three-wave gas journal bearing housing were recorded and compared to transient predicted orbits. The shaft was mounted eccentric with a fixed runout. Experimental observations for both the absolute bearing housing center orbits and the relative bearing housing center to shaft center orbits are in good agreement with the predictions. The sub-synchronous whirl motion generated by the fluid film was found experimentally and predicted theoretically for certain speeds. A three-wave journal bearing can run stably under dynamic loads with orbits well inside the bearing clearance. Moreover, the orbits are almost circular free of the influence of bearing wave shape.

Dimofte, Florin↗
NASA NTRS2002

Vestibular convergence patterns in vestibular nuclei neurons of alert primates

Sensory signal convergence is a fundamental and important aspect of brain function. Such convergence may often involve complex multidimensional interactions as those proposed for the processing of otolith and semicircular canal (SCC) information for the detection of translational head movements and the effective discrimination from physically congruent gravity signals. In the present study, we have examined the responses of primate rostral vestibular nuclei (VN) neurons that do not exhibit any eye movement-related activity using 0.5-Hz translational and three-dimensional (3D) rotational motion. Three distinct neural populations were identified. Approximately one-fourth of the cells exclusively encoded rotational movements (canal-only neurons) and were unresponsive to translation. The canal-only central neurons encoded head rotation in SCC coordinates, exhibited little orthogonal canal convergence, and were characterized with significantly higher sensitivities to rotation as compared to primary SCC afferents. Another fourth of the neurons modulated their firing rates during translation (otolith-only cells). During rotations, these neurons only responded when the axis of rotation was earth-horizontal and the head was changing orientation relative to gravity. The remaining one-half of VN neurons were sensitive to both rotations and translations (otolith + canal neurons). Unlike primary otolith afferents, however, central neurons often exhibited significant spatiotemporal (noncosine) tuning properties and a wide variety of response dynamics to translation. To characterize the pattern of SCC inputs to otolith + canal neurons, their rotational maximum sensitivity vectors were computed using exclusively responses during earth-vertical axis rotations (EVA). Maximum sensitivity vectors were distributed throughout the 3D space, suggesting strong convergence from multiple SCCs. These neurons were also tested with earth-horizontal axis rotations (EHA), which would activate both vertical canals and otolith organs. However, the recorded responses could not be predicted from a linear combination of EVA rotational and translational responses. In contrast, one-third of the neurons responded similarly during EVA and EHA rotations, although a significant response modulation was present during translation. Thus this subpopulation of otolith + canal cells, which included neurons with either high- or low-pass dynamics to translation, appear to selectively ignore the component of otolith-selective activation that is due to changes in the orientation of the head relative to gravity. Thus contrary to primary otolith afferents and otolith-only central neurons that respond equivalently to tilts relative to gravity and translational movements, approximately one-third of the otolith + canal cells seem to encode a true estimate of the translational component of the imposed passive head and body movement.

Non-NASA Center↗
NASA NTRS1984

Comments on the BLAG model: the carbonate-silicate geochemical cycle and its effect on atmospheric carbon dioxide over the past 100 million years

A self-consistent method of determining initial conditions for the model presented by Berner, Lasaga, and Garrels (1983) (henceforth, the BLAG model) is derived, based on the assumption that the CO2 geochemical cycle was in steady state at t = -100 my (million years). This initialization procedure leads to a dissolved magnesium concentration higher than that calculated by Berner, Lasaga, and Garrels and to a low ratio of dissolved calcium to bicarbonate prior to 60 my ago. The latter prediction conflicts with the geologic record of evaporite deposits, which requires that this ratio remain greater than 0.5. The contradiction is probably caused by oversimplifications in the BLAG model, such as the neglect of the cycles of organic carbon and sulfur.

Evolution, Planetary↗
NASA NTRS2015

Physics Simulation Software for Autonomous Propellant Loading and Gas House Autonomous System Monitoring

1. Kennedy Space Center (KSC) is developing a mobile launching system with autonomous propellant loading capabilities for liquid-fueled rockets. An autonomous system will be responsible for monitoring and controlling the storage, loading and transferring of cryogenic propellants. The Physics Simulation Software will reproduce the sensor data seen during the delivery of cryogenic fluids including valve positions, pressures, temperatures and flow rates. The simulator will provide insight into the functionality of the propellant systems and demonstrate the effects of potential faults. This will provide verification of the communications protocols and the autonomous system control. 2. The High Pressure Gas Facility (HPGF) stores and distributes hydrogen, nitrogen, helium and high pressure air. The hydrogen and nitrogen are stored in cryogenic liquid state. The cryogenic fluids pose several hazards to operators and the storage and transfer equipment. Constant monitoring of pressures, temperatures and flow rates are required in order to maintain the safety of personnel and equipment during the handling and storage of these commodities. The Gas House Autonomous System Monitoring software will be responsible for constantly observing and recording sensor data, identifying and predicting faults and relaying hazard and operational information to the operators.

Regalado Reyes, Bjorn Constant↗
NASA NTRS2016

Impact of Spatial Sampling on Continuity of MODIS-VIIRS Land Surface Reflectance Products: A Simulation Approach

With the increasing need to construct long-term climate-quality data records to understand, monitor, and predict climate variability and change, it is vital to continue systematic satellite measurements along with the development of new technology for more quantitative and accurate observations. The Suomi National Polar-orbiting Partnership mission provides continuity in monitoring the Earths surface and its atmosphere in a similar fashion as the heritage MODIS instruments onboard the National Aeronautics and Space Administrations Terra and Aqua satellites. In this paper, we aim at quantifying the consistency of Aqua MODIS and Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Reflectance (LSR) and NDVI products as related to their inherent spatial sampling characteristics. To avoid interferences from sources of measurement and/or processing errors other than spatial sampling, including calibration, atmospheric correction, and the effects of the bidirectional reflectance distribution function, the MODIS and VIIRSLSR products were simulated using the Landsat-8s Operational Land Imager (OLI) LSR products. The simulations were performed using the instruments point spread functions on a daily basis for various OLI scenes over a 16-day orbit cycle. It was found that the daily mean differences due to discrepancies in spatial sampling remain below 0.0015 (1) in absolute surface reflectance at subgranule scale (i.e., OLI scene size).We also found that the MODISVIIRS product intercomparisons appear to be minimally impacted when differences in the corresponding view zenith angles (VZAs) are within the range of -15deg to -35deg (VZA(sub v) - VZA(sub m)), where VIIRS and MODIS footprints resemble in size. In general, depending on the spatial heterogeneity of the OLI scene contents, per-grid-cell differences can reach up to 20.Further spatial analysis of the simulated NDVI and LSR products revealed that, depending on the user accuracy requirements for product intercomparisons, spatial aggregations may be used. It was found that if per-grid-cell differences on the order of 10(in LSR or NDVI) are tolerated, the product intercomparisons are expected to be immune from differences in spatial sampling.

VIIRS↗