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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Detecting Changes in Terrain Using Unmanned Aerial Vehicles

In recent years, small unmanned aerial vehicles (UAVs) have been used for more than the thrill they bring to model airplane enthusiasts. Their flexibility and low cost have made them a viable option for low-altitude reconnaissance. In a recent effort, we acquired video data from a small UAV during several passes over the same flight path. The objective of the exercise was to determine if objects had been added to the terrain along the flight path between flight passes. Several issues accrue to this simple-sounding problem: (1) lighting variations may cause false detection of objects because of changes in shadow orientation and strength between passes; (2) variations in the flight path due to wind-speed, and heading change may cause misalignment of gross features making the task of detecting changes between the frames very difficult; and (3) changes in the aircraft orientation and altitude lead to a change in size of the features from frame-to-frame making a comparison difficult. In this paper, we discuss our efforts to perform this change detection, and the lessons that we learned from this exercise.

Rahman, Zia-ur↗
DOE OSTI2024

Luminosity determination using Z boson production at the CMS experiment

The measurement of Z boson production is presented as a method to determine the integrated luminosity of CMS data sets. The analysis uses proton–proton collision data, recorded by the CMS experiment at the CERN LHC in 2017 at a center-of-mass energy of 13 TeV. Events with Z bosons decaying into a pair of muons are selected. The total number of Z bosons produced in a fiducial volume is determined, together with the identification efficiencies and correlations from the same data set, in small intervals of 20 pb –1 of integrated luminosity, thus facilitating the efficiency and rate measurement as a function of time and instantaneous luminosity. Using the ratio of the efficiency-corrected numbers of Z bosons, the precisely measured integrated luminosity of one data set is used to determine the luminosity of another. For the first time, a full quantitative uncertainty analysis of the use of Z bosons for the integrated luminosity measurement is performed. The uncertainty in the extrapolation between two data sets, recorded in 2017 at low and high instantaneous luminosity, is less than 0.5%. We show that the Z boson rate measurement constitutes a precise method, complementary to traditional methods, with the potential to improve the measurement of the integrated luminosity.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1997

Analysis of Wind Tunnel Longitudinal Static and Oscillatory Data of the F-16XL Aircraft

Static and oscillatory wind tunnel data are presented for a 10-percent-scale model of an F-16XL aircraft. Static data include the effect of angle of attack, sideslip angle, and control surface deflections on aerodynamic coefficients. Dynamic data from small-amplitude oscillatory tests are presented at nominal values of angle of attack between 20 and 60 degrees. Model oscillations were performed at five frequencies from 0.6 to 2.9 Hz and one amplitude of 5 degrees. A simple harmonic analysis of the oscillatory data provided Fourier coefficients associated with the in-phase and out-of-phase components of the aerodynamic coefficients. A strong dependence of the oscillatory data on frequency led to the development of models with unsteady terms in the form of indicial functions. Two models expressing the variation of the in-phase and out-of-phase components with angle of attack and frequency were proposed and their parameters estimated from measured data.

Klein, Vladislav↗
NASA NTRS1982

Satellite switched FDMA advanced communication technology satellite program

The satellite switched frequency division multiple access system provided a detailed system architecture that supports a point to point communication system for long haul voice, video and data traffic between small Earth terminals at Ka band frequencies at 30/20 GHz. A detailed system design is presented for the space segment, small terminal/trunking segment at network control segment for domestic traffic model A or B, each totaling 3.8 Gb/s of small terminal traffic and 6.2 Gb/s trunk traffic. The small terminal traffic (3.8 Gb/s) is emphasized, for the satellite router portion of the system design, which is a composite of thousands of Earth stations with digital traffic ranging from a single 32 Kb/s CVSD voice channel to thousands of channels containing voice, video and data with a data rate as high as 33 Mb/s. The system design concept presented, effectively optimizes a unique frequency and channelization plan for both traffic models A and B with minimum reorganization of the satellite payload transponder subsystem hardware design. The unique zoning concept allows multiple beam antennas while maximizing multiple carrier frequency reuse. Detailed hardware design estimates for an FDMA router (part of the satellite transponder subsystem) indicate a weight and dc power budget of 353 lbs, 195 watts for traffic model A and 498 lbs, 244 watts for traffic model B.

Atwood, S.↗
NASA NTRS2021

A Machine Learning Approach to Predict Aircraft Landing Times using Mediated Predictions from Existing Systems

We developed a novel approach for predicting the landing time of airborne flights in real-time operations. The first step predicts a landing time by using mediation rules to select from among physics-based predictions (relying on the expected flight trajectory) already available in real time in the Federal Aviation Administration System Wide Information Management system data feeds. The second step uses a machine learning model built upon the mediated predictions. The model is trained to predict the error in the mediated prediction, using features describing the current state of an airborne flight. These features are calculated in real time from a relatively small number of data elements that are readily available for airborne flights. Initial results based on five months of data at six large airports demonstrate that incorporating a machine learning model on top of the mediated physics-based prediction can lead to substantial additional improvements in prediction quality.

Machine learning↗
NASA NTRS2021

A Machine Learning Approach to Predict Aircraft Landing Times using Mediated Predictions from Existing Systems

We developed a novel approach for predicting the landing time of airborne flights in real-time operations. The first step predicts a landing time by using mediation rules to select from among physics-based predictions (relying on the expected flight trajectory) already available in real time in the Federal Aviation Administration System Wide Information Management system data feeds. The second step uses a machine learning model built upon the mediated predictions. The model is trained to predict the error in the mediated prediction, using features describing the current state of an airborne flight. These features are calculated in real time from a relatively small number of data elements that are readily available for airborne flights. Initial results based on five months of data at six large airports demonstrate that incorporating a machine learning model on top of the mediated physics-based prediction can lead to substantial additional improvements in prediction quality.

Machine learning↗
DOE OSTI2024

Learning together: Towards foundation models for machine learning interatomic potentials with meta-learning

Abstract The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material systems. However, traditional training methods for machine learning models are unable to leverage the plethora of data available as they require that each dataset be generated using the same QM method. Taking machine learning interatomic potentials (MLIPs) as an example, we show that meta-learning techniques, a recent advancement from the machine learning community, can be used to fit multiple levels of QM theory in the same training process. Meta-learning changes the training procedure to learn a representation that can be easily re-trained to new tasks with small amounts of data. We then demonstrate that meta-learning enables simultaneously training to multiple large organic molecule datasets. As a proof of concept, we examine the performance of a MLIP refit to a small drug-like molecule and show that pre-training potentials to multiple levels of theory with meta-learning improves performance. This difference in performance can be seen both in the reduced error and in the improved smoothness of the potential energy surface produced. We therefore show that meta-learning can utilize existing datasets with inconsistent QM levels of theory to produce models that are better at specializing to new datasets. This opens new routes for creating pre-trained, foundation models for interatomic potentials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2012

Data Assimilation of Photosynthetic Light-use Efficiency using Multi-angular Satellite Data: II Model Implementation and Validation

Spatially explicit and temporally continuous estimates of photosynthesis will be of great importance for increasing our understanding of and ultimately closing the terrestrial carbon cycle. Current capabilities to model photosynthesis, however, are limited by accurate enough representations of the complexity of the underlying biochemical processes and the numerous environmental constraints imposed upon plant primary production. A potentially powerful alternative to model photosynthesis through these indirect observations is the use of multi-angular satellite data to infer light-use efficiency (ε) directly from spectral reflectance properties in connection with canopy shadow fractions. Hall et al. (this issue) introduced a new approach for predicting gross ecosystem production that would allow the use of such observations in a data assimilation mode to obtain spatially explicit variations in ε from infrequent polar-orbiting satellite observations, while meteorological data are used to account for the more dynamic responses of ε to variations in environmental conditions caused by changes in weather and illumination. In this second part of the study we implement and validate the approach of Hall et al. (this issue) across an ecologically diverse array of eight flux-tower sites in North America using data acquired from the Compact High Resolution Imaging Spectroradiometer (CHRIS) and eddy-flux observations. Our results show significantly enhanced estimates of ε and therefore cumulative gross ecosystem production (GEP) over the course of one year at all examined sites. We also demonstrate that ε is greatly heterogeneous even across small study areas. Data assimilation and direct inference of GEP from space using a new, proposed sensor could therefore be a significant step towards closing the terrestrial carbon cycle.

Hilker, Thomas↗
NASA NTRSDate not supplied

Solar Flare Catalog for SPICE Instrument on the Solar Orbiter

Studying the solar corona, the outermost layer of solar atmosphere, is a pivotal part of understanding the dynamic relations between solar activity and the solar wind, which can disrupt the near-Earth environment. Solar flares emit electromagnetic radiation in the solar corona, capable of releasing large amounts of energy in a matter of minutes. Flares can also be associated with Coronal Mass Ejections (CMEs) and affect Earth’s ionosphere. One instrument that can be used to study flares is the Spectral Imaging of the Coronal Environment (SPICE) instrumentaboard the Solar Orbiter (SolO). SPICE is a high-resolution extreme ultraviolet stigmatic slit spectrometer that covers emission lines formed from the solar chromosphere to corona. Since SPICE is a stigmatic slit spectrometer, the instrument can only take in data from a small spatial area on the Sun at a time. Due to the fast and unpredictable nature of flare events, it can be difficult to determine if and when SPICE has observed a flare. For this reason, we have created a catalog of flares observed by SPICE. This catalog of observational data was assembledby cross referencing data between different solar missions, including data from SolO’s E xtreme Ultraviolet Imager (EUI) and Spectrometer Telescope for Imaging X-rays (STIX), Solar Dynamics Observatory’s Atmospheric Imaging Assembly (SDO/AIA) instrument, and the Geostationary Operational Environmental Satellite (GOES-R). Supplemental analysis of the SPICE solar flare data includes Gaussian line fitting for flares of particular interest. The catalog can be utilized to locate and study coronal loop structures and flare ribbons. This SPICE solar flare catalog and additional supplemental analysis allows for the ease of identification of useful SPICE spectral data and multi-instrument analysis in order to study solar flare activity. It will be open for use by the Solar Orbiter and broader Heliophysics communities.

Anneliese L. Schmidt↗
NASA NTRS1980

Forward- and reverse-bias tunneling effects in n/+/p silicon solar cells

Excess currents due to field-assisted tunneling in both forward and reverse bias directions have been observed in n(+)-p silicon solar cells. These currents arise from the effect of conducting paths produced in the depletion layer by n(+) diffusion and cell processing. Forward-bias data indicate a small potential barrier with height of 0.04 eV at the n(+) end of conducting paths. Under reverse bias, excess tunneling currents involve a potential barrier at the p end of the conducting paths, the longer paths being associated with smaller barrier heights and dominating at the lower temperatures. Low-reverse-bias data give energy levels of 0.11 eV for lower temperatures (253-293 K) and 0.35 eV for higher temperatures (293-380 K). A model is suggested to explain the results.

Garlick, G. F. J.↗
DOE OSTI2025

Small radius inclusive jet production at the LHC through NNLO+NNLL

The study of hadronic jets and their substructure at hadronic colliders is crucial for improving our understanding of QCD, and searching for new physics. As such, there has been a significant effort to improve their theoretical description. In the small radius limit, inclusive jet production exhibits a universal factorization, enabling the resummation of logarithms which greatly stabilizes theoretical predictions. In this paper, we show how to combine a recently introduced framework for small-R resummation with the Stripper subtraction formalism for fragmentation, enabling next-to-next-to-leading order calculations of small-R inclusive jet production for a wide variety of processes at the LHC. We extract the two-loop constants for the jet functions, enabling for the first time next-to-next-to-leading logarithmic resummation matched to next-to-next-to-leading order perturbative calculation. We compare with CMS data for small-R jet production, and find that our results greatly improve the accuracy of the predictions at small-R, and stabilize the perturbative convergence and error estimates at larger R. Our approach is applicable to a wide class of jet substructure observables exhibiting similar factorization theorems, opening the door to an NNLO jet substructure program at the LHC.

Effective Field Theories↗
NASA NTRS2013

Inferring Soil Moisture Memory from Streamflow Observations Using a Simple Water Balance Model

Soil moisture is known for its integrative behavior and resulting memory characteristics. Soil moisture anomalies can persist for weeks or even months into the future, making initial soil moisture a potentially important contributor to skill in weather forecasting. A major difficulty when investigating soil moisture and its memory using observations is the sparse availability of long-term measurements and their limited spatial representativeness. In contrast, there is an abundance of long-term streamflow measurements for catchments of various sizes across the world. We investigate in this study whether such streamflow measurements can be used to infer and characterize soil moisture memory in respective catchments. Our approach uses a simple water balance model in which evapotranspiration and runoff ratios are expressed as simple functions of soil moisture; optimized functions for the model are determined using streamflow observations, and the optimized model in turn provides information on soil moisture memory on the catchment scale. The validity of the approach is demonstrated with data from three heavily monitored catchments. The approach is then applied to streamflow data in several small catchments across Switzerland to obtain a spatially distributed description of soil moisture memory and to show how memory varies, for example, with altitude and topography.

Soil Moisture↗
NASA NTRSDate not supplied

High-Throughput Strategies that Encompass Experiments and Machine Learning to Predict the Mechanical Properties of Additive Manufactured Aerospace Alloys

Small Punch Test (SPT) uses a thin disk of material to predict mechanical properties. While SPT has existed for decades, it has been used largely as a qualitative evaluator of mechanical properties. Recent advances in computational modeling have enabled the extraction of uniaxial stress-strain response from the measured SPT load-displacement data. Due to small sample volumes and unidirectional testing, SPT is conducive to high-throughput automation and ideally suited to extract properties from high-cost materials. Aerospace alloys have been of recent interest to the Additive Manufacturing (AM) community due to AM’s unique ability to fabricate complex designs not possible, or extremely arduous, with conventional manufacturing. In this research, SPT, coupled with Materials Informatics and computational modeling, is used to develop relevant Process-Structure-Property relationships to decrease the cost and time of process optimization for AM aerospace alloys, namely Inconel 718, Inconel 625, and Niobium C103.

High-throughput Testing↗
NASA NTRS1995

Features of solar wind acceleration according to radio occultation data

In addressing one of the fundamental problems in solar physics establishing the mechanism(s) responsible for the solar wind acceleration and the corona heating - it is essential to have a reliable knowledge of the heliocentric radial dependence of the solar wind properties. Adequate data are available for small solar distances R less than 4 R(solar mass) from coronal white light and EUV observations and at distances R greater than 60 R(solar mass) from in situ measurements. One of the few methods available to fill in the gap between these boundaries is the radio scintillation technique. Taking the example of the solar wind velocity, the most reliable such measurements are obtained when phase fluctuation observations of scattered radio waves, which are not susceptible to saturation effects, are recorded at two or more widely-spaced ground stations. Two extensive observation campaigns of this type were carried out with the Venus-orbiting satellites Venera 10 in 1976 and Venera 15/16 in 1984. The observations were performed over the course of three months near superior conjunction at solar offset distances R approximately 6-80 R(solar mass). The main results from the subsequent analysis of these data are: (1) velocities vary between 250 and 380 km s(exp -1) for R greater than 20 R(solar mass), agreeing with similar measurements using natural sources (IPS); (2) velocities derived from two-station phase fluctuation observations varv between 70 and 120 km s(exp -1) for R less than 12 R(solar mass), i.e. values substantially lower than those derived from conventional IPS data; and (3) it is suggested that the different velocity profiles derived from the two data sets at small R may be due to the effects of magnetosonic and Alfvenic waves on radio wave scattering. Further analysis of additional radio sounding data should help resolve the apparent discrepancy.

Efimov, A. I.↗
NASA NTRS2011

High-Performance Data Analysis Tools for Sun-Earth Connection Missions

The data analysis tool of choice for many Sun-Earth Connection missions is the Interactive Data Language (IDL) by ITT VIS. The increasing amount of data produced by these missions and the increasing complexity of image processing algorithms requires access to higher computing power. Parallel computing is a cost-effective way to increase the speed of computation, but algorithms oftentimes have to be modified to take advantage of parallel systems. Enhancing IDL to work on clusters gives scientists access to increased performance in a familiar programming environment. The goal of this project was to enable IDL applications to benefit from both computing clusters as well as graphics processing units (GPUs) for accelerating data analysis tasks. The tool suite developed in this project enables scientists now to solve demanding data analysis problems in IDL that previously required specialized software, and it allows them to be solved orders of magnitude faster than on conventional PCs. The tool suite consists of three components: (1) TaskDL, a software tool that simplifies the creation and management of task farms, collections of tasks that can be processed independently and require only small amounts of data communication; (2) mpiDL, a tool that allows IDL developers to use the Message Passing Interface (MPI) inside IDL for problems that require large amounts of data to be exchanged among multiple processors; and (3) GPULib, a tool that simplifies the use of GPUs as mathematical coprocessors from within IDL. mpiDL is unique in its support for the full MPI standard and its support of a broad range of MPI implementations. GPULib is unique in enabling users to take advantage of an inexpensive piece of hardware, possibly already installed in their computer, and achieve orders of magnitude faster execution time for numerically complex algorithms. TaskDL enables the simple setup and management of task farms on compute clusters. The products developed in this project have the potential to interact, so one can build a cluster of PCs, each equipped with a GPU, and use mpiDL to communicate between the nodes and GPULib to accelerate the computations on each node.

Messmer, Peter↗
NASA NTRS1992

Analysis of lightning flash videos from the Space Shuttle using blob and morphological techniques

Flash rates measured from the Space Shuttle range from 27.8 flashes per minute to 77 flashes per minute. The cloud is an optically thick medium which effectively scatters the energy from a lightning discharge and thereby broadens the risetime and duration of each lightning pulse. Because of the small size, spacecraft sensors with resolutions of 1 km or more are unlikely to detect the individual lightning channels. Instead, the energy from the lightning channel is scattered within the cloud, thereby broadening the apparent area. All of these measurements of lightning flash area and flash rate have involved manual manipulation and analysis of the video or film data. Only a small percentage of the Space Shuttle lightning video has been analyzed. An attempt is made to combine the use of real-time digital disk system and an automated analysis routine in order to overcome this limitation and make processing of a sequence of video frames a much less labor-intensive task.

Pitts, David E.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-32652-DND

Space weather is an important driver of the exposure of aircrew and passengers to cosmic rays at flight altitudes. The assessment of the corresponding radiation doses can be realized by measurements or model calculations that cover the whole range of the radiation field in terms of geomagnetic shielding, atmospheric shielding, and the effects of space weather. Since the radiation field at aviation altitudes is very complex in terms of particle composition and energy distribution, the accurate experimental determination of doses at aviation altitudes is still a challenging task. Accordingly, the amount of data with comparatively small uncertainties is scarce. The Community Coordinated Modeling Center invited the Federal Aviation Administration, the German Aerospace Center, and the National Aeronautics and Space Administration to make their radiation models for aviation CARI-7A, PANDOCA, and NAIRAS available for interested users via the Community Coordinated Modeling Center web site. A concomitant comparison of model calculations with measuring data provided information on the predicting capabilities and the uncertainties of the current versions of these models under quiet space weather conditions.

Matthias M Meier↗
DOE OSTI2022

Size and Distribution of Parr Produced from Natural‐ and Hatchery‐Origin Steelhead Spawning Naturally in a Small Pacific Northwest Coastal Stream

Abstract Recent studies suggest that steelhead Oncorhynchus mykiss produced from local hatchery‐origin (HOR) adults have lower lifetime fitness than their natural‐origin (NOR) counterparts. To increase our understanding of this pattern, we compared age‐1 parr size and distribution produced by a local integrated population of HOR and NOR adults spawning naturally across a range of environmental conditions. Across 8 years, we used genetic parentage assignments and field measurements of parr in conjunction with creek temperature and flow data to find small, nonbiologically significant differences in size between parr of different parent origins. This suggests that parr produced by HOR adults are acquiring enough resources to grow at a rate similar to that of parr produced by NOR adults. In contrast to origin, we found strong positive associations between mean size of parr and annual mean water temperature and summer flow. Additionally, the distribution of parr was similar and HOR‐produced parr were not skewed relative to the location of the hatchery. Parr occupying the full extent of accessible and suitable habitat might be a positive outcome for hatchery programs seeking to supplement an existing population or replace an extirpated population. However, these results also imply that HOR fish could be competing with NOR fish for food and space, which might be less desirable if the goal of the hatchery program is harvest and not supplementation. Our results highlight the importance of clearly articulated goals for the hatchery program, as the observed pattern could be deemed a benefit or a risk depending on the hatchery's purpose. Lastly, projections for higher water temperatures and reduced summer flows, when considered in the context of the correlations we observed for size of parr, imply that coastal steelhead populations could start experiencing negative climate impacts with respect to steelhead parr growth metrics during the summer in the next few decades.

Kennedy, Benjamen M.↗