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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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This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.
This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.
This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.
This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.
This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.
This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.
This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.
This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.
This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.
Focal Area: (2) Predictive modeling through the use of AI techniques and AI-derived model components. (3) Insight gleaned from complex data using AI, big data analytics, and other advanced methods. We propose an AI-enabled model-experiment (MODEX) framework to improve the predictability of subsurface water storage (SWS) from local to conus scales in a changing environment by taking advantage of DOE’s observation and simulation capabilities, as well as to inform the model and the observation development.
This project developed methodologies to measure the in-situ principal stress in the deep subsurface through use of multiple independent, but complementary, seismic methods, laboratory verification, and development of theoretical frameworks. By leveraging existing regional and local datasets we developed, tested, and refined a set of diagnostic tools for determining the in-situ stress state with reduced uncertainty at and below reservoir depths (1.5-6 km). A set of novel tools was produced that are scale independent, such that their utility is equivalent on regional, field scale, and near borehole monitoring of principal stresses in reservoir underburden for carbon storage projects. During a 4-year Department of Energy (DOE) and Southern Company funded project, carried out by the Electric Power Research Institute (EPRI), Lawrence Livermore National Laboratory (LLNL), the University of Oklahoma (OU), and the U.S. Geological Survey (USGS), the project team developed methodologies to measure the far-field in-situ principal stress in the deep subsurface, leveraging induced seismicity data from waste-water disposal projects. These methodologies consisted in the use of well-established and technically advanced seismic processing methods, such as virtual seismometer method-moment tensor (VSM-MT) and shear wave splitting (SWS), that are adept at recovering the stress orientation and certain components of the stress tensor. These methods were applied to robust seismicity catalogs created with matched filter techniques near sites of active fluid disposal—a proxy for carbon storage sites where such datasets are more limited. Estimates of the stress orientation made with seismic processing tools were considered along with laboratory acoustic emission experiments conducted on rock samples from the region of interest. Stress orientations in the studied region do not vary significantly across distances of ~100 km, nor are they found to rotate through time as a consequence of local wastewater disposal, as previously speculated. Finally, the project team investigated the trade-offs among the different seismic methods and evaluated the range of uncertainty that is generated with these methodologies, which led to a practical use and refinement of the VSM-MT technique when it is applied to field datasets. Understanding the trade-offs between these different methods highlighted the potential benefits of improved quantification of uncertainties on stress field estimations.
Downward shortwave radiation (SW) and photosynthetically active radiation (PAR) play crucial roles in Earth system dynamics. Spaceborne remote sensing techniques provide a unique means for mapping accurate spatiotemporally continuous SW–PAR, globally. However, any individual polar-orbiting or geostationary satellite cannot satisfy the desired high temporal resolution (sub-daily) and global coverage simultaneously, while integrating and fusing multisource data from complementary satellites/sensors is challenging because of co-registration, intercalibration, near real-time data delivery and the effects of discrepancies in orbital geometry. The Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR), launched in February 2015, offers an unprecedented possibility to bridge the gap between high temporal resolution and global coverage and characterize the diurnal cycles of SW–PAR globally. In this study, we adopted a suite of well-validated data-driven machine-learning models to generate the first global land products of SW–PAR, from June 2015 to June 2019, based on DSCOVR/EPIC data. The derived products have high temporal resolution (hourly) and medium spatial resolution (0.1°×0.1°), and they include estimates of the direct and diffuse components of SW–PAR. We used independently widely distributed ground station data from the Baseline Surface Radiation Network (BSRN), the Surface Radiation Budget Network (SURFRAD), NOAA's Global Monitoring Division and the U.S. Department of Energy's Atmospheric System Research (ASR) program to evaluate the performance of our products, and we further analyzed and compared the spatiotemporal characteristics of the derived products with the benchmarking Clouds and the Earth's Radiant Energy System Synoptic (CERES) data. We found both the hourly and daily products to be consistent with ground-based observations (e.g., hourly and daily total SWs have low biases of -3.96 and -0.71 W m -2 and root-mean-square errors (RMSEs) of 103.50 and 35.40 W m -2 , respectively). The developed products capture the complex spatiotemporal patterns well and accurately track substantial diurnal, monthly, and seasonal variations in SW–PAR when compared to CERES data. They provide a reliable and valuable alternative for solar photovoltaic applications worldwide and can be used to improve our understanding of the diurnal and seasonal variabilities of the terrestrial water, carbon and energy fluxes at various spatial scales.
With the deployment of the Apollo 15 lunar surface experiments package, two identical solar-wind spectrometers (SWS), separated by approximately 1100 km, are now on the lunar surface. The spectrometers provide the first opportunity to measure the properties of the solar plasma simultaneously at two locations a fixed distance apart. It is hoped that these simultaneous observations will yield new information about the plasma and its interaction with the Moon and the geomagnetic field. At the time of preparation of this report, magnetic tapes of only 20 hours of simultaneous data had been received. These data are discussed.
The Apollo 15 mission was the fourth manned lunar landing and the northernmost location yet visited. The landing site on the southeastern edge of the Imbrium Basin afforded the opportunity of studying several unique lunar features, the Apennine Mountains, Hadley Rille, and the Imbrium Basin fill. Detailed geological study of the data returned from the mission provided new insight into the structure and history of the Basin. A third Apollo Lunar Surface Experiment Package was deployed during the mission containing seven experiments: Passive Seismometer (PSE), Heat Flow (HFE), Surface Magnetometer (LSM), Suprathermal Ion Detector (SIDE), Cold Cathode Gauge (CCGE), Solar Wind Spectrometer (SWS), and a Dust Detector. We have been able to triangulate on the sources of moonquakes and to observe simultaneous nighttime and daytime changes in the moon's magnetic field, solar wind flux, and the neutral and ionized components of the moon's tenuous atmosphere.
A preflight assessment of the Skylab VHF ranging coverage for the rendezvous portion of the nominal SL-1/SL-3 mission is reported, assuming a 27 July 1973 SL-3 launch. Data are based on a nominal attitude trajectory, which has the Saturn workshop in a solar inertial attitude throughout the rendezvous; the CSM terminal phase initiation maneuver is nominal. An addendum to this report is being prepared, which considers the effects of early and late TPI maneuvers. Curves are presented which show the variation in received power levels on both spacecraft-to-spacecraft links from about 600 n.mi. range to CSM and SWS station keeping. Appropriate threshold levels are shown on these received power curves to indicate zero circuit margins for the ranging function.
Papers are presented on such topics as the development of the Imaging Spectrometer for Shuttle and space platform applications; the in-flight calibration of pushbroom remote sensing instruments for the SPOT program; buttable detector arrays for 1.55-1.7 micron imaging; the design of the Improved Stratospheric and Mesospheric Sounder on the Upper Atmosphere Research Satellite; and SAGE II design and in-orbit performance. Consideration is also given to the Shuttle Imaging Radar-B/C instruments; the Venus Radar Mapper multimode radar system design; various ISO instruments (ISOCAM, ISOPHOT, and SWS and LWS); and instrumentation for the Space Infrared Telescope Facility.
An algorithm was developed for calculating simultaneously SSTs, SWs, and TAUs on a global basis using only the 6.6 and 18 GHz channels of the SMMR. Samples of the retrievals were calculated in each of eight of the SMMR years and found to produce independent results, consistent with weather charts and climatic records, even in the presence of high winds. Another new algorithm for calculating high-latitude scalar winds from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) data was devised and tuned with surface observations from a number of documented Arctic Polar Low events. The algorithm utilizes the horizontally and vertically polarized radiances from the 0.8 and 1.7 cm wavelength channels of the ten-channel SMMR to retrieve near surface oceanic scalar winds and cloud water in the column, and takes advantage of the relatively small fluctuations in atmospheric water vapor at high latitudes. An advantage of this algorithm for high latitude winds from SMMR over the global algorithm is an inherently better spatial resolution as a result of the shorter wavelengths used.
Developing a software product involves estimating various project parameters. This is typically done in the planning stages of the project when there is much uncertainty and very little information. Coming up with accurate estimates of effort, cost, schedule, and reliability is a critical problem faced by all software project managers. The use of estimation models and commercially available tools in conjunction with the best bottom-up estimates of software-development experts enhances the ability of a product development group to derive reasonable estimates of important project parameters. This paper describes the experience of the IBM Software Solutions (SWS) Toronto Laboratory in selecting software estimation models and tools and deploying their use to the laboratory's product development groups. It introduces the SLIM and COSTAR products, the software estimation tools selected for deployment to the product areas, and discusses the rationale for their selection. The paper also describes the mechanisms used for technology injection and tool deployment, and concludes with a discussion of important lessons learned in the technology and tool insertion process.