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NASA NTRS1998

The Development of the Joint NASA GSFC and the National Imagery and Mapping Agency (NIMA) Geopotential Model EGM96

The NASA Goddard Space Flight Center (GSFC), the National Imagery and Mapping Agency (NIMA), and The Ohio State University (OSU) have collaborated to develop an improved spherical harmonic model of the Earth's gravitational potential to degree 360. The new model, Earth Gravitational Model 1996 (EGM96), incorporates improved surface gravity data, altimeter-derived gravity anomalies from ERS-1 and from the GEOSAT Geodetic Mission (GM), extensive satellite tracking data-including new data from Satellite Laser Ranging (SLR), the Global Postioning System (GPS), NASA's Tracking and Data Relay Satellite System (TDRSS), the French DORIS system, and the US Navy TRANET Doppler tracking system-as well as direct altimeter ranges from TOPEX/POSEIDON (T/P), ERS-1, and GEOSAT. The final solution blends a low-degree combination model to degree 70, a block-diagonal solution from degree 71 to 359, and a quadrature solution at degree 360. The model was used to compute geoid undulations accurate to better than one meter (with the exception of areas void of dense and accurate surface gravity data) and realize WGS84 as a true three-dimensional reference system. Additional results from the EGM96 solution include models of the dynamic ocean topography to degree 20 from T/P and ERS-1 together, and GEOSAT separately, and improved orbit determination for Earth-orbiting satellites.

Lemoine, F. G.↗

NASA NTRS2022

Dynamic Radioisotope Power System (DRPS) Design Reference Mission (DRM) Lunar Rover

The Radioisotope Power Systems (RPS) Program tasked the Compass Team to evaluate use of Dynamic Radioisotope Power Systems (DRPS) for lunar science rovers. The object was to identify their advantages and challenges as well as to influence the technology developments with flight-type requirements. This was easily done by using the promising Volatiles Investigating Polar Exploration Rover (VIPER) solar- powered rover mission as a platform to ‘swap in’ a DRPS. The ‘pickup truck bed’ approach allowed both simplified installation and operation of the DRPS while keeping the forward lunar surface ‘blocked’ from the DRPS waste heat which could sublimate the icy surface. It was found that with the Stirling DRPS option the mass is within the planned VIPER lander capability and is very close to VIPER mass and size (the DRPS replaces large battery pack/solar arrays). The Stirling DRPS option produced ~300 Watts electrical (We) using six general purpose heat source (GPHS) bricks and eight Stirling convertors. Replacing the solar/battery power with radioisotope power allows a continuous presence (instead of 6 hours) in a permanently shadowed region (PSR) and over 18 months of operations with minimal science impact (rearward surface heating). It was also found that use of a dynamic system (instead of a thermoelectric system) reduces the heat impact on the science environment two-to-three times. The DRPS, along with a relay link (like Gateway), can provide continuous access to PSRs. The system was also found to be capable of roving for 8 hours per day with a range of over 500 km in 18 months. Preliminary cost estimates fit into a Class D mission but only assuming VIPER heritage and launch, lander, operations, nuclear specific costs [National Environmental Policy Act (NEPA), fueling, transport, Launch Services Program (LSP), etc.] and DRPS are not included.

DRPS↗

NASA NTRS1996

Geocoding of AIRSAR/TOPSAR SAR Data

It has been demonstrated and recognized that radar interferometry is a promising method for the determination of digital elevation information and terrain slope from Synthetic Aperture Radar (SAR) data. An important application of Interferometric SAR (InSAR) data in areas with topographic variations is that the derived elevation and slope can be directly used for the absolute radiometric calibration of the amplitude SAR data as well as for scattering mechanisms analysis. On the other hand polarimetric SAR data has long been recognized as permitting a more complete inference of natural surfaces than a single channel radar system. In fact, imaging polarimetry provides the measurement of the amplitude and relative phase of all transmit and receive polarizations. On board the NASA DC-8 aircraft, NASA/JPL operates the multifrequency (P, L and C bands) multipolarimetric radar AIRSAR. The TOPSAR, a special mode of the AIRSAR system, is able to collect single-pass interferometric C- and/or L-band VV polarized data. A possible configuration of the AIRSAR/TOPSAR system is to acquire single-pass interferometric data at C-band VV polarization and polarimetric radar data at the two other lower frequencies. The advantage of this system configuration is to get digital topography information at the same time the radar data is collected. The digital elevation information can therefore be used to correctly calibrate the SAR data. This step is directly included in the new AIRSAR Integrated Processor. This processor uses a modification of the full motion compensation algorithm described by Madsen et al. (1993). However, the Digital Elevation Model (DEM) with the additional products such as local incidence angle map, and the SAR data are in a geometry which is not convenient, since especially DEMs must be referred to a specific cartographic reference system. Furthermore, geocoding of SAR data is important for multisensor and/or multitemporal purposes. In this paper, a procedure to geocode the new AIRSAR/TOPSAR data is presented. As an example an AIRSAR/TOPSAR image acquired in 1994 is geocoded and evaluated in terms of geometric accuracy.

Holecz, Francesco↗

DOE OSTI2025

IM3 Open Source Data Center Atlas

IM3 Open Source Data Center Atlas Description This dataset contains locations of existing data center facilities in the United States. Data center locations were derived from OpenStreetMap (OSM), a crowd-sourced database. Data points from OSM are processed in various ways to determine additional variables provided in the data including: facility area (square feet), associated US county, and US state. This dataset can be used to identify areas of concentrated data center development and inform government and private sector planning strategies for future buildout of data centers and the infrastructure necessary to support it. Usage Notes Validation of OSM-derived data center locations is an ongoing development under the IM3 project, and the database will be updated as new information becomes available. In some instances, both the data center area (e.g., campus) and individual data center buildings are included as overlapping areas in the database. Both values are retained. Data center points, buildings, and campus areas are provided as separate layers in the downloadable data package. Note that data items are not necessarily complete across layers. That is, a specific data center may only be present as a single point geometry in the "point" layer while other data centers are represented in both the campus and building layers. In some cases, data center campuses and/or buildings straddle a county boundary line. Mappings to both counties are retained in the database as separate rows. These data rows will have the same data center id information, but each will have different county information. Crowd-sourced data, by nature, relies on individuals and communities to provide information. As a result, some data may be missing where it has not yet been reported. As we collect information on additional data center locations and as OSM receives additional contributions, the database will be updated to capture additional data points not yet shown. Technical Information Data is available for download under the following formats: GeoPackage (GPKG) CSV Geospatial data is provided in the WGS84 (EPSG:4326) coordinate reference system. The GeoPackage download contains the following layers. See usage notes for more information. "point" "building" "campus" The "point" layer includes all data from OSM that had POINT geometry type (i.e., individual coordinates). The "building" layer includes all OSM data that did not have POINT geometry and where the building tag in the OSM export was neither equal to "no" or null. Data that did not meet the "point" or "building" qualification was assumed to be a facility campus and included in the "campus" layer. The dataset contains the following parameters. Variables provided by OSM are labeled with (OSM-provided). id - unique identification number (OSM-provided with prefix of "node/", "relation/" and similar attributes removed) state - name of US state state_abb - two letter US state abbreviation state_id - state ID number county - name of US county county_id - county ID number ref - reference numbers or codes (OSM-provided) operator - the name of the company, corporation, or person in charge facility (OSM-provided) name - name of facility (OSM-provided) sqft - surface area of facility polygon, measured in square feet. Only available for "building" and "campus" layers lat - latitude of data centroid point lon - longitude of data centroid point type – represented spatial information. One of "point", "building", or "campus". geometry – POLYGON geometry of area footprint (in "campus" and "building" layers) or POINT geometry of locations (in "point" layer). This parameter is not included in the csv download. Attribution Data center locations were derived from OpenStreetMap, which is made available at openstreetmap.org under the Open Database License (ODbL). US state and county boundary information was collected from the US Census Bureau for the year 2024, which is made publicly available at https://www.census.gov/geographies/mapping-files.html Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License The IM3 Open Source Data Center Atlas is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall [Pacific Northwest National Labor↗

DOE OSTI2026

IM3 Open Source Data Center Atlas

IM3 Open Source Data Center Atlas Description This dataset contains locations of existing data center facilities in the United States. Data center locations were derived from OpenStreetMap (OSM), a crowd-sourced database. Data points from OSM are processed in various ways to determine additional variables provided in the data including: facility area (square feet), associated US county, and US state. This dataset can be used to identify areas of concentrated data center development and inform government and private sector planning strategies for future buildout of data centers and the infrastructure necessary to support it. Usage Notes Validation of OSM-derived data center locations is an ongoing development under the IM3 project, and the database will be updated as new information becomes available. In some instances, both the data center area (e.g., campus) and individual data center buildings are included as overlapping areas in the database. Both values are retained. Data center points, buildings, and campus areas are provided as separate layers in the downloadable data package. Note that data items are not necessarily complete across layers. That is, a specific data center may only be present as a single point geometry in the "point" layer while other data centers are represented in both the campus and building layers. In some cases, data center campuses and/or buildings straddle a county boundary line. Mappings to both counties are retained in the database as separate rows. These data rows will have the same data center id information, but each will have different county information. Crowd-sourced data, by nature, relies on individuals and communities to provide information. As a result, some data may be missing where it has not yet been reported. As we collect information on additional data center locations and as OSM receives additional contributions, the database will be updated to capture additional data points not yet shown. Data items will occasionally be removed from OSM if they are misidentified, if they no longer exist, if they are duplicates of another item, or similar. For that reason, updated versions of this database may not contain all data center locations included in previous versions. Technical Information Data is available for download under the following formats: GeoPackage (GPKG) CSV Geospatial data is provided in the WGS84 (EPSG:4326) coordinate reference system. The GeoPackage download contains the following layers. See usage notes for more information. "point" "building" "campus" The "point" layer includes all data from OSM that had POINT geometry type (i.e., individual coordinates). The "building" layer includes all OSM data that did not have POINT geometry and where the building tag in the OSM export was neither equal to "no" or null. Data that did not meet the "point" or "building" qualification was assumed to be a facility campus and included in the "campus" layer. The dataset contains the following parameters. Variables provided by OSM are labeled with (OSM-provided). id - unique identification number (OSM-provided with prefix of "node/", "relation/" and similar attributes removed) state - name of US state state_abb - two letter US state abbreviation state_id - state ID number county - name of US county county_id - county ID number ref - reference numbers or codes (OSM-provided) operator - the name of the company, corporation, or person in charge facility (OSM-provided) name - name of facility (OSM-provided) sqft - surface area of facility polygon, measured in square feet. Only available for "building" and "campus" layers lat - latitude of data centroid point lon - longitude of data centroid point type – represented spatial information. One of "point", "building", or "campus". geometry – POLYGON geometry of area footprint (in "campus" and "building" layers) or POINT geometry of locations (in "point" layer). This parameter is not included in the csv download. Attribution Data center locations were derived from OpenStreetMap, which is made available at openstreetmap.org under the Open Database License (ODbL). US state and county boundary information was collected from the US Census Bureau for the year 2024, which is made publicly available at https://www.census.gov/geographies/mapping-files.html Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License The IM3 Open Source Data Center Atlas is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall [Pacific Northwest National Labor↗

DOE OSTI2022

Wind and Solar Hybrid Power Plants for Energy Resilience

Wind-solar-storage hybrid power plants represent a significant and growing share of new proposed projects in the United States (U.S.). Their uptake is supported by increasing renewable energy market share, technical abilities for dispatch and control, and decreasing wind, solar, and battery storage costs. Simultaneously, generation and storage resources are increasingly used in distributed power systems. While concerns around the reliability of the aging, transforming U.S. electric grid are growing, diversifying energy resources through hybridization or spatial distribution provides an opportunity to enhance power system resilience compared to single-source generation. Understanding where to build hybrids for resilience value, rather than bulk power supply, has not been fully explored in previous studies. Therefore, in this study, we complete a national complementarity analysis to identify areas in the U.S. that are particularly suited for wind-solar hybrid power plant development. We show the importance of seasonal and diurnal patterns in assessing complementarity, and identify that regions in the Great Plains, Midwest, and Southeast are particularly suited for hybrid power plants. We demonstrate the resilience value of hybridization for a reference system based near Memphis, Tennessee, and show optimal sizing of wind, solar, and storage assets given 1.0 and 0.9 critical load factors. Results indicate that pairing wind and solar assets better meet constant load demand and reduce storage requirements compared to solar alone. These results enable future work integrating complementarity metrics in resilience frameworks and indicate a need for more finer resolution of local resource, demand, and hazard data.

14 SOLAR ENERGY↗

NASA NTRS1972

Coordinate transformation by minimizing correlations between parameters

This investigation was to determine the transformation parameters (three rotations, three translations and a scale factor) between two Cartesian coordinate systems from sets of coordinates given in both systems. The objective was the determination of well separated transformation parameters with reduced correlations between each other, a problem especially relevant when the sets of coordinates are not well distributed. The above objective is achieved by preliminarily determining the three rotational parameters and the scale factor from the respective direction cosines and chord distances (these being independent of the translation parameters) between the common points, and then computing all the seven parameters from a solution in which the rotations and the scale factor are entered as weighted constraints according to their variances and covariances obtained in the preliminary solutions. Numerical tests involving two geodetic reference systems were performed to evaluate the effectiveness of this approach.

Kumar, M.↗

NASA NTRS2000

Lidar Technology at the Goddard Laser and Electro-Optics Branch

The Laser and Electro-Optics Branch at Goddard Space flight Center was established about three years ago to provide a focused center of engineering support and technology development in these disciplines with an emphasis on spaced based instruments for Earth and Space Science. The Branch has approximately 15 engineers and technicians with backgrounds in physics, optics, and electrical engineering. Members of the Branch are currently supporting a number of space based lidar efforts as well as several technology efforts aimed at enabling future missions. The largest effort within the Branch is support of the Ice, Cloud, and land Elevation Satellite (ICESAT) carrying the Geoscience Laser Altimeter System (GLAS) instrument. The ICESAT/GLAS primary science objectives are: 1) To determine the mass balance of the polar ice sheets and their contributions to global sea level change; and 2) To obtain essential data for prediction of future changes in ice volume and sea-level. The secondary science objectives are: 1) To measure cloud heights and the vertical structure of clouds and aerosols in the atmosphere; 2) To map the topography of land surfaces; and 3) To measure roughness, reflectivity, vegetation heights, snow-cover, and sea-ice surface characteristics. Our efforts have concentrated on the GLAS receiver component development, the Laser Reference Sensor for the Stellar Reference System, the GLAS fiber optics subsystems, and the prelaunch calibration facilities. We will report on our efforts in the development of the space qualified interference filter [Allan], etalon filter, photon counting detectors, etalor/laser tracking system, and instrument fiber optics, as well as specification and selection of the star tracker and development of the calibration test bed. We are also engaged in development work on lidar sounders for chemical species. We are developing new lidar technology to enable a new class of miniature lidar instruments that are compatible with small Discovery-class orbiters now in the NASA planetary program. The purpose of the lidar is to continuously profile the water vapor and dust in the Mars atmosphere from orbit in order to quantify its dynamics, their relationship in the diurnal cycles, and to infer water vapor exchange with the Mars surface. To remotely measure the water-vapor height profiles, we will use the differential absorption lidar (DIAL) technique. We are also developing a laser sensor for measuring the total column content of CO2 in the atmosphere of the earth. CO2 is the principal greenhouse gas and has increased by roughly 80 ppm in the last century and a half. We will report our efforts in the development of the laser transmitter and photon counting detector components for a Mars Orbiting DIAL system and for the CO2 sounder.

Heaps, William S.↗

DOE OSTI2025

IM3 Projected US Data Center Locations

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory Protected Areas Database of the United States (PAD-US) areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall (ORCID:0000000328077088)↗

DOE OSTI2026

IM3 Projected US Data Center Locations

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway or within an airport area boundary Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory USGS Protected Areas Database of the United States (PAD-US) GAP status 1, 2, or 3 areas US National Parks Wetlands USFWS critical habitats BIA land areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall (ORCID:0000000328077088)↗

NASA NTRS1973

Inertial reference unit

The inertial reference unit is a high performance gyro attitude reference system for use on the OAO spacecraft. The IRU is a three axis system, which provides both rate and attitude information for spacecraft control. The purpose of the IRU is to reduce the dependency on gimballed startrackers and, in turn, simplify the OAO ground operations by eliminating the need for the continual programming of gimballed startracker assignments in accordance with computed occultation schedules. During normal operations, it is used to control the pitch and yaw axes during experiment occultations and during spacecraft reorientations. The roll axis is continuously under control of the IRU except during brief periods for attitude update. To provide for these capabilities the IRU must be able to perform two basic functions. One is to maintain an inertially fixed reference for spacecraft control and the second is to accurately reorient the reference upon command.

Kull, F. J.↗

NASA NTRS2010

Employing Geodatabases for Planetary Mapping Conduct - Requirements, Concepts and Solutions

Planetary geologic mapping has become complex in terms of merging and co-registering a variety of different datasets for analysis and mapping. But it has also become more convenient when it comes to conducting actual (geoscientific) mapping with the help of desktop Geographic Information Systems (GIS). The complexity and variety of data, however, are major issues that need to be taken care of in order to provide mappers with a consistent and easy-to-use mapping basis. Furthermore, a high degree of functionality and interoperability of various commercial and open-source GIS and remote sensing applications allow mappers to organize map data, map components and attribute data in a more sophisticated and intuitional way when compared to workflows 15 years ago. Integration of mapping results of different groups becomes an awkward task as each mapper follows his/her own style, especially if mapping conduct is not coordinated and organized programmatically. Problems of data homogenization start with various interpretations and implementations of planetary map projections and reference systems which form the core component of any mapping and analysis work. If the data basis is inconsistent, mapping results in terms of objects georeference become hard to integrate. Apart from data organization and referencing issues, which are important on the mapping as well as the data-processing side of every project, the organization of planetary geologic map units and attributes, as well as their representation within a common GIS environment, are key components that need to be taken care of in a consistent and persistent way.

vanGasselt, Stephan↗

NASA NTRS2018

Diagnostic for Verifying the Thrust Vector Requirement of the AEPS Hall-Effect Thruster and Comparison to the NEXT-C Thrust Vector Diagnostic

A diagnostic has been designed and fabricated to verify the thrust vector requirement for the Advanced Electric Propulsion System (AEPS) Hall Effect Rocket with Magnetic Shielding (HERMeS) thruster. This diagnostic will be used to verify that the propulsion system thrust vector offset from the mounting surface normal vector does not exceed 1.5 degrees over the entire throttling range and over the course of 23,000 hours of thruster testing. The diagnostic will also not violate the thruster's required voltage standoff capability in the presence of carbon backsputter by being minimally intrusive and not significantly adding to the facility backsputtered rate. Based on these requirements, an appropriate diagnostic design was determined to comprise of an array of 23 Faraday probes swept through the plume in an arc 1m from the thruster to map the beam current density. The beam current density centroid of the plume is assumed to track the thrust vector within an acceptable level of uncertainty. Additionally, a reference system, including optical alignment to the mounting surface normal vector and tilt sensors, was devised to periodically calibrate the probe position and motion throughout the long duration wear test campaign. Initial measurements of the thruster plume have been acquired to demonstrate the diagnostics functionality, verify procedures, and assess any necessary improvements prior to implementation of the diagnostic during the AEPS Engineering Development Unit (EDU) long duration wear test. To illustrate the merits of differing approaches to thrust vector determination for different classes of electric propulsion thrusters, NASA's Evolutionary Xenon Thruster-Commercial (NEXT-C) thrust vector diagnostic design details and recent data are also discussed (Appendix A).

Benavides, Gabriel F.↗

NASA NTRS2018

Diagnostic for Verifying the Thrust Vector Requirement of the AEPS Hall-Effect Thruster and Comparison to the NEXT-C Thrust Vector Diagnostic

Benavides, Gabriel F.↗

NASA NTRS2018

Diagnostic for Verifying the Thrust Vector Requirement of the AEPS Hall-Effect Thruster and Comparison to the NEXT-C Thrust Vector Diagnostic

Advanced Electric Propulsion System (AEPS)↗

NASA NTRS1983

Basic research for the geodynamics program

Laser systems deployed in satellite tracking were upgraded to accuracy levels where biases from systematic unmodelled effects constitute the basic factor that prohibits extraction of the full amount of information contained in the observations. Taking into consideration that the quality of the instrument advances at a faster pace compared to the understanding and modeling of the physical processes involved, one can foresee that in the near future when all lasers are replaced with third generation ones the limiting factor for the estimated accuracies will be the aforementioned biases. Therefore, for the reduction of the observations, methods should be deployed in such a way that the effect of the biases will be kept well below the noise level. Such a method was proposed and studied. This method consists of using the observed part of the satellite pass and converting the laser ranges into range differences in hopes that they will be less affected by biases in the orbital models, the reference system, and the observations themselves.

Source record↗

NASA NTRS2005

Lunar and Planetary Science XXXVI, Part 8

The following topics were discussed: Why Small is Beautiful, and How to Detect Another 10 Billion Small Main Belt Asteroids; Basalts in Mare Humorum and S.E. Procellarum; Basalts in Mare Serenitatis, Lacus Somniorum, Lacus Mortis and Part of Mare Tranquillitatis; Revised Thorium Abundances for Lunar Red Spots; Integrating Global-Scale Mission Datasets - Understanding the Martian Crust; Comparing Goldstone Solar System Radar Earth-based Observations of Mars with Orbital Datasets; Water Ice Clouds in the Martian Atmosphere: A View from MGS TES; Lunar Meteorite Northeast Africa 001: An Anorthositic Regolith Breccia with Mixed Highland/Mare Components; One Spectrometer, Two Spectra: Complementary Hemispherical Reflectance and Thermal Emission Spectroscopy Using a Single FTIR Instrument; Alteration Phases Associated with High Concentrations of Orthopyroxene and Olivine on Mars; Experimental Crystallization of Fe-rich Basalt: Application to Cooling Rate and Oxygen Fugacity of Nakhlite MIL-03346; Thermo-Chemical Convection in Europa s Icy Shell with Salinity; Tectonic Pressurization of Aquifers in the Formation of Mangala and Athabasca Valles on Mars; 3D Structural Interpretation of the Eagle Butte Impact Structure, Alberta, Canada; Ultraviolet Views of Enceladus, Tethys, and Dione; Crustal Plateaus as Ancient Large Impact Features: A Hypothesis; New Observations of Crustal Plateau Surface Histories, Venus: Implications for Crustal Plateau Hypotheses; Detailed Mineralogical Characterizations of Four S-Asteroids: 138 Tolosa, 306 Unitas, 346 Hermentaria, and 480 Hansa; Working with Planetary Coordinate Reference Systems; Bilingual Map of Mercury; and The Io Mountain Online Database.

Source record↗

NASA NTRS1995

The Visible Imaging System (VIS) for the Polar Spacecraft

The Visible Imaging System (VIS) is a set of three low-light-level cameras to be flown on the POLAR spacecraft of the Global Geospace Science (GGS) program which is an element of the International Solar-Terrestrial Physics (ISTP) campaign. Two of these cameras share primary and some secondary optics and are designed to provide images of the nighttime auroral oval at visible wavelengths. A third camera is used to monitor the directions of the fields-of-view of these sensitive auroral cameras with respect to sunlit Earth. The auroral emissions of interest include those from N+2 at 391.4 nm, 0 I at 557.7 and 630.0 nm, H I at 656.3 nm, and 0 II at 732.0 nm. The two auroral cameras have different spatial resolutions. These resolutions are about 10 and 20 km from a spacecraft altitude of 8 R(sub e). The time to acquire and telemeter a 256 x 256-pixel image is about 12 s. The primary scientific objectives of this imaging instrumentation, together with the in-situ observations from the ensemble of ISTP spacecraft, are (1) quantitative assessment of the dissipation of magnetospheric energy into the auroral ionosphere, (2) an instantaneous reference system for the in-situ measurements, (3) development of a substantial model for energy flow within the magnetosphere, (4) investigation of the topology of the magnetosphere, and (5) delineation of the responses of the magnetosphere to substorms and variable solar wind conditions.

Frank, L. A.↗