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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Massive compression for high data rate macromolecular crystallography (HDRMX): impact on diffraction data and subsequent structural analysis

New higher-count-rate, integrating, large-area X-ray detectors with framing rates as high as 17400 images per second are beginning to be available. These will soon be used for specialized macromolecular crystallography experiments but will require optimal lossy compression algorithms to enable systems to keep up with data throughput. Some information may be lost. Can we minimize this loss with acceptable impact on structural information? To explore this question, we have considered several approaches: summing short sequences of images, binning to create the effect of larger pixels, use of JPEG-2000 lossy wavelet-based compression, and use of Hcompress, which is a Haar-wavelet-based lossy compression borrowed from astronomy. We also explore the effect of the combination of summing, binning, and Hcompress or JPEG-2000. In each of these last two methods one can specify approximately how much one wants the result to be compressed from the starting file size. These provide particularly effective lossy compressions that retain essential information for structure solution from Bragg reflections.

47 OTHER INSTRUMENTATION↗
NASA NTRS2018

Rapid Model Import Tool (RMIT)

Our project is about developing a tool to implement conversion of 3D Computer Aided Design (CAD) models produced with software such as Delmia, 3DS Max, or Maya, into a size and format compatible with the Unity 3D environment. RMIT will be used to aid KSC engineering personnel in the design, development, testing, operations, and training on spacecraft, launch vehicles, facilities, and ground support equipment. For our project, we are using Blender, a free/open-source 3D graphics software, with the goal of developing, testing, and deploying a 3D CAD model converter tool. I worked on using Blender to import 3D CAD models exported from CATIA software into Collada file format. The Collada file format has file extension DAE. Importing the Collada DAE file as is into Blender, generates dots and dashes. In 3DS Max, there is an existing OpenCollada plugin and with that plugin, 3DS Max can import the DAE file successfully. But, Blender does not seem to have an OpenCollada plugin, so I worked on writing a new OpenCollada plugin for Blender. Since 3DS Max was able to display the image, I looked into comparing differences between the original DAE file and the DAE file exported from 3DS Max using the OpenCollada plugin. As Collada documents describing digital assets are XML files with file extension DAE, Collada files contains XML tags, making them easily modifiable. After some research, it appears that Blender does not like primitive 2D tags like tristrips and trifans. Changing those tags to polygons slightly improved the image, but the pieces were exploded. I found after further research comparing differences between the original file and the file exported from 3DS Max that the values inside the translate tags in the original file are scaled down by a factor of 25.4 in the exported file from 3DS Max, representing the millimeters to inches conversion (1 inch = 25.4 millimeters). After scaling down values inside all of the translate tags by 25.4, the exploded pieces stuck back in, but the image needed further improvement. I have been able to create a new plugin in Blender that takes the original DAE file, replaces the primitive 2D tags tristrips and trifans with polygons, scales down the values inside the translate tags by a factor of 25.4, and saves the changes into a temporary DAE file. After the temporary DAE file is imported into Blender, the temp file is then deleted, keeping the original DAE file intact. Starting with a DAE file that is exported using the NASA Enterprise Visualization Application (NEVA), a Collada exporter, from CATIA gives better results. NEVA is a Design Visualization product that is used for exporting 3D models from CATIA. With NEVA, the up axis is defined in the top-level node if navigation gravity is enabled. With that file, just replacing the primitive tags tristrips and trifans with polygons in yields a much improved image in Blender. As we identify more differences between the original DAE file and the DAE file exported from 3DS Max, this plugin can be improved further. Our goal is to have a model that is formatted and sized for import into Unity, and we are trying out different 3D programs to see which will work best.

Ayyangar, Arjun↗
DOE OSTI2026

Evaluation of Best Practices in Mitigating Startup Costs on Leadership-Class Supercomputers

Supercomputers at Department of Energy (DOE) National Laboratories face a widening range of workloads, from traditional modeling and simulation to Artificial Intelligence model training or complex multi-stage workflows, and beyond. At DOE Leadership Computing Facilities like the Oak Ridge Leadership Computing Facility (OLCF), these workloads demand concurrent access to large portions of the supercomputer’s resources. Launching a job across massive supercomputers is challenging from the start; the file system struggles with a large backlog of metadata requests as tens of thousands of processes read thousands of the same files, and the compute job cannot start until this is completed. There are multiple existing approaches to calm this metadata storm, ranging from vendor-developed tools like sbcast to National Laboratory-developed tools like Spindle and Copper. In this paper, we benchmark and discuss three common approaches to improving compute job launch latencies on Frontier: Slurm’s sbcast tool, Spindle, and Copper. We evaluate these tools by measuring the launch latencies of four workloads: OSU Microbenchmark’s osu_init, Pynamic, Python import mpi4py, and Python import torch. We provide discussion of the results, highlighting data that meet expectations and that do not meet expectations.

Hagerty, Nick [ORNL] (ORCID:0000000330014414)↗
NASA NTRS2017

Precision Asteroid Astrometry

Among the methods used to guide spacecraft to their destinations, Optical Navigation (OpNav) remains an effective option. OpNav makes use of star fields and small body ephemerides to precisely locate spacecraft. To facilitate accurate OpNav, the small body ephemerides must be constantly updated; asteroid orbits accumulate errors each year of a few milliarcseconds. Through extended exposures, taken with strategic offsets, a least-squares solution can be found that determines updated ephemeris data. This updated data can also be used by occultation astronomers to gain further information about the small bodies, including their size and shape. Using the 24-inch telescope at the Caltech Table Mountain Observatory (TMO), we capture two or more 180 second exposures of each target. These images, combined with a file for the predicted background star field and two reference files, are then processed through a series of scripts and programs. Starting with a prediction file and two to five exposures of the asteroid, the data is processed. This original data is about 32MB per observation. Once the data are reduced to only Right Ascension and Declination for each target, the data are ready for delivery. This consists of text only, and for each target takes about 80 bytes; this resulting data reduction is about five orders of magnitude. This method produces observed positions that are refined by about 12 milliarcseconds, a refinement that is accomplished almost nowhere else. The occultation observations that are facilitated by the ephemerides being refined also produce results that are not possible in any other way from ground-based observations.

Dial, Jason↗
NASA NTRSDate not supplied

MISR Browse data (MISBR_V5)

A MISR Browse Tool is available to allow easy access to ellipsoid-derived, true-color images for each camera reduced to 2.2 km resolution. The MISR red, green and blue bands are used to create the true-color image in JPEG format. The image is intentionally clipped and gamma-stretched to make cloud, ocean and land features visible. The user may find the Browse Tool helpful before ordering data. The tool's purpose is to search and view images in the on-line user interface. However, the browse product can be ordered separately. The Browse Tool page has Orbit-to-Date and Lat/Lon-to-Path/Block conversion tools to help identify particular MISR geographic locations and data files. [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=2.2 km; Longitude_Resolution=2.2 km; Horizontal_Resolution_Range=1 km - < 10 km or approximately .01 degree - < .09 degree; Temporal_Resolution=about 15 orbits/day; Temporal_Resolution_Range=Daily - < Weekly].

EOSDIS↗
NASA NTRS2011

Rapid Calculation of Spacecraft Trajectories Using Efficient Taylor Series Integration

A variable-order, variable-step Taylor series integration algorithm was implemented in NASA Glenn's SNAP (Spacecraft N-body Analysis Program) code. SNAP is a high-fidelity trajectory propagation program that can propagate the trajectory of a spacecraft about virtually any body in the solar system. The Taylor series algorithm's very high order accuracy and excellent stability properties lead to large reductions in computer time relative to the code's existing 8th order Runge-Kutta scheme. Head-to-head comparison on near-Earth, lunar, Mars, and Europa missions showed that Taylor series integration is 15.8 times faster than Runge- Kutta on average, and is more accurate. These speedups were obtained for calculations involving central body, other body, thrust, and drag forces. Similar speedups have been obtained for calculations that include J2 spherical harmonic for central body gravitation. The algorithm includes a step size selection method that directly calculates the step size and never requires a repeat step. High-order Taylor series integration algorithms have been shown to provide major reductions in computer time over conventional integration methods in numerous scientific applications. The objective here was to directly implement Taylor series integration in an existing trajectory analysis code and demonstrate that large reductions in computer time (order of magnitude) could be achieved while simultaneously maintaining high accuracy. This software greatly accelerates the calculation of spacecraft trajectories. At each time level, the spacecraft position, velocity, and mass are expanded in a high-order Taylor series whose coefficients are obtained through efficient differentiation arithmetic. This makes it possible to take very large time steps at minimal cost, resulting in large savings in computer time. The Taylor series algorithm is implemented primarily through three subroutines: (1) a driver routine that automatically introduces auxiliary variables and sets up initial conditions and integrates; (2) a routine that calculates system reduced derivatives using recurrence relations for quotients and products; and (3) a routine that determines the step size and sums the series. The order of accuracy used in a trajectory calculation is arbitrary and can be set by the user. The algorithm directly calculates the motion of other planetary bodies and does not require ephemeris files (except to start the calculation). The code also runs with Taylor series and Runge-Kutta used interchangeably for different phases of a mission.

Scott, James R.↗
NASA NTRS1996

Parallel NPARC: Implementation and Performance

Version 3 of the NPARC Navier-Stokes code includes support for large-grain (block level) parallelism using explicit message passing between a heterogeneous collection of computers. This capability has the potential for significant performance gains, depending upon the block data distribution. The parallel implementation uses a master/worker arrangement of processes. The master process assigns blocks to workers, controls worker actions, and provides remote file access for the workers. The processes communicate via explicit message passing using an interface library which provides portability to a number of message passing libraries, such as PVM (Parallel Virtual Machine). A Bourne shell script is used to simplify the task of selecting hosts, starting processes, retrieving remote files, and terminating a computation. This script also provides a simple form of fault tolerance. An analysis of the computational performance of NPARC is presented, using data sets from an F/A-18 inlet study and a Rocket Based Combined Cycle Engine analysis. Parallel speedup and overall computational efficiency were obtained for various NPARC run parameters on a cluster of IBM RS6000 workstations. The data show that although NPARC performance compares favorably with the estimated potential parallelism, typical data sets used with previous versions of NPARC will often need to be reblocked for optimum parallel performance. In one of the cases studied, reblocking increased peak parallel speedup from 3.2 to 11.8.

Townsend, S. E.↗
DOE OSTI2025

Data for: Climatic Imprint on Interfacially-Controlled Platinum-Palladium Resources

Data package for manuscript "Climatic Imprint on Interfacially-Controlled Platinum-Palladium Resources" by Emily G. Wright, Ivey Wang, Yihang Fang, Elaine D. Flynn, and Jeffrey G. Catalano. This dataset contains adsorption results from experiments designed to investigate the effect of chloride on Pd(II) adsorption to goethite and Pt(II) adsorption to hematite and goethite, including lab experiments, X-ray absorption fine structure spectroscopy, and models of retention within a laterite. See the associated manuscript for full methods information. The file "Wright2025_PtAds_data.csv" contains the target starting Pt concentration (uM), final aqueous Pt and associated error (in uM), calculated adsorbed Pt and associated error (in umol/m2), target and measured aqueous chloride (mM), target aqueous nitrate (mM), final pH, and mineral concentration/loading (g/L). Associated mineral-free controls (mineral loading = 0 g/L) are included; the aqueous Pd error was not calculated and chloride was not measured in every sample. These data appear in Figures 1, S3, S4, S5, S20, and S22 in the associated manuscript. The file "Wright2025_PdAds_data.csv" contains the target starting Pd concentration (uM), final aqueous Pd and associated error (in uM), calculated adsorbed Pd and associated error (in umol/m2), target and measured aqueous chloride (mM), and mineral concentration/loading (g/L). Associated mineral-free controls (mineral loading = 0 g/L) are included; the aqueous Pd error was not calculated and chloride was not measured in every sample. These data appear in Figures 1, S3, S4, S5, and S20 in the associated manuscript. The file "Wright2025_MineralBatches_data.csv" contains the mineral identity and BET specific surface area (m2/g) for every mineral batch synthesized and used in experiments. The annealing time used is listed for hydrothermally annealed goethite. These data appear in Table S2 in the associated manuscript. The file "Wright2025_XRD_data.csv" contains the XRD patterns for every mineral batch synthesized as the counts as a function of two theta (in degrees). See "Wright2025_MineralBatches_data.csv" for more details on specific mineral batches. These data appear in Figure S2 in the associated manuscript. The file "Wright 2025_ZetaPotential_data.csv" contains the measured zeta potentials for samples of goethite (batch G2) at pH 4 the presence of varying amounts of sodium chloride. These data appear in Table S3 in the associated manuscript. The file "Wright2025_XAFSSamples_data.csv" contains the specific mineral batch, measured final aqueous Pd or Pt (uM), measured final aqueous chloride (mM), and estimated adsorbed Pd or Pt (umol/m2) of all XAFS samples. These data appear in Tables S4, S7, S8, and S10 in the associated manuscript. The files "Wright2025_PdXAFS_data.csv" and "Wright2025_PtXAFS_data.csv" contain the normalized spectra of Pd and Pt, respectively, adsorbed to minerals at varying chloride concentrations. See "Wright2025_XAFSSamples_data.csv" for a guide to sample names. Note that "05" in a sample name is equivalent to "0.5". These data appear in Figures 2, S6, S7, S8, S12, S13, and S14 in the associated manuscript. The file "Wright2025_LateriteProfileProfileModelParameters_data.csv" include the ratio of hematite to hematite and goethite in two synthetic, modeled profiles, as well as the modeled surface areas of goethite and hematite as a function of relative depth within the modeled weathering zone. These data were used, in conjunction with equations presented in the paper, to calculate the theoretical concentrations of Pd and Pt (and the resulting Pt/Pd ratio) within the profiles. These data appear in Figure 3 in the associated manuscript. The file "Wright2025_Imagery_data.zip" is a zipped folder containing the TEM and STEM images appear in Figures S18 and S19. Individual files are labeled as either STEM (Fig. S18) or TEM (Fig. S19) with a letter representing the part of the multipart figure.

58 GEOSCIENCES↗
NASA NTRS1982

PKI solar thermal plant evaluation at Capitol Concrete Products, Topeka, Kansas

A system feasibility test to determine the technical and operational feasibility of using a solar collector to provide industrial process heat is discussed. The test is of a solar collector system in an industrial test bed plant at Capitol Concrete Products in Topeka, Kansas, with an experiment control at Sandia National Laboratories, Albuquerque. Plant evaluation will occur during a year-long period of industrial utilization. It will include performance testing, operability testing, and system failure analysis. Performance data will be recorded by a data acquisition system. User, community, and environmental inputs will be recorded in logs, journals, and files. Plant installation, start-up, and evaluation, are anticipated for late November, 1981.

Hauger, J. S.↗
NASA NTRS2016

Utilizing HDF4 File Content Maps for the Cloud

We demonstrate a prototype study that HDF4 file content map can be used for efficiently organizing data in cloud object storage system to facilitate cloud computing. This approach can be extended to any binary data formats and to any existing big data analytics solution powered by cloud computing because HDF4 file content map project started as long term preservation of NASA data that doesn't require HDF4 APIs to access data.

Elastic Search↗
NASA NTRS2016

GeneLab Phase 2: Integrated Search Data Federation of Space Biology Experimental Data

The GeneLab project is a science initiative to maximize the scientific return of omics data collected from spaceflight and from ground simulations of microgravity and radiation experiments, supported by a data system for a public bioinformatics repository and collaborative analysis tools for these data. The mission of GeneLab is to maximize the utilization of the valuable biological research resources aboard the ISS by collecting genomic, transcriptomic, proteomic and metabolomic (so-called omics) data to enable the exploration of the molecular network responses of terrestrial biology to space environments using a systems biology approach. All GeneLab data are made available to a worldwide network of researchers through its open-access data system. GeneLab is currently being developed by NASA to support Open Science biomedical research in order to enable the human exploration of space and improve life on earth. Open access to Phase 1 of the GeneLab Data Systems (GLDS) was implemented in April 2015. Download volumes have grown steadily, mirroring the growth in curated space biology research data sets (61 as of June 2016), now exceeding 10 TB/month, with over 10,000 file downloads since the start of Phase 1. For the period April 2015 to May 2016, most frequently downloaded were data from studies of Mus musculus (39) followed closely by Arabidopsis thaliana (30), with the remaining downloads roughly equally split across 12 other organisms (each 10 of total downloads). GLDS Phase 2 is focusing on interoperability, supporting data federation, including integrated search capabilities, of GLDS-housed data sets with external data sources, such as gene expression data from NIHNCBIs Gene Expression Omnibus (GEO), proteomic data from EBIs PRIDE system, and metagenomic data from Argonne National Laboratory's MG-RAST. GEO and MG-RAST employ specifications for investigation metadata that are different from those used by the GLDS and PRIDE (e.g., ISA-Tab). The GLDS Phase 2 system will implement a Google-like, full-text search engine using a Service-Oriented Architecture by utilizing publicly available RESTful web services Application Programming Interfaces (e.g., GEO Entrez Programming Utilities) and a Common Metadata Model (CMM) in order to accommodate the different metadata formats between the heterogeneous bioinformatics databases. GLDS Phase 2 completion with fully implemented capabilities will be made available to the general public in September 2017.

Space Biology↗
NASA NTRS2001

Feasibility of Actively Cooled Silicon Nitride Airfoil for Turbine Applications Demonstrated

Nickel-base superalloys currently limit gas turbine engine performance. Active cooling has extended the temperature range of service of nickel-base superalloys in current gas turbine engines, but the margin for further improvement appears modest. Therefore, significant advancements in materials technology are needed to raise turbine inlet temperatures above 2400 F to increase engine specific thrust and operating efficiency. Because of their low density and high-temperature strength and thermal conductivity, in situ toughened silicon nitride ceramics have received a great deal of attention for cooled structures. However, the high processing costs and low impact resistance of silicon nitride ceramics have proven to be major obstacles for widespread applications. Advanced rapid prototyping technology in combination with conventional gel casting and sintering can reduce high processing costs and may offer an affordable manufacturing approach. Researchers at the NASA Glenn Research Center, in cooperation with a local university and an aerospace company, are developing actively cooled and functionally graded ceramic structures. The objective of this program is to develop cost-effective manufacturing technology and experimental and analytical capabilities for environmentally stable, aerodynamically efficient, foreign-object-damage-resistant, in situ toughened silicon nitride turbine nozzle vanes, and to test these vanes under simulated engine conditions. Starting with computer aided design (CAD) files of an airfoil and a flat plate with internal cooling passages, the permanent and removable mold components for gel casting ceramic slips were made by stereolithography and Sanders machines, respectively. The gel-cast part was dried and sintered to final shape. Several in situ toughened silicon nitride generic airfoils with internal cooling passages have been fabricated. The uncoated and thermal barrier coated airfoils and flat plates were burner rig tested for 30 min without and with air cooling. Without cooling, the surface temperature of the flat plate reached approximately 2350 F. Starting with computer aided design (CAD) files of an airfoil and a flat plate with internal cooling passages, the permanent and removable mold components for gel casting ceramic slips were made by stereolithography and Sanders machines, respectively. The gel-cast part was dried and sintered to final shape. Several in situ toughened silicon nitride generic airfoils with internal cooling passages have been fabricated. The uncoated and thermal barrier coated airfoils and flat plates were burner rig tested for 30 min without and with air cooling. Without cooling, the surface temperature of the flat plate reached approximately 2350 F. With cooling, the surface temperature decreased to approximately 1910 F--a drop of approximately 440 F. This preliminary study demonstrates that a near-net-shape silicon nitride airfoil can be fabricated and that silicon nitride can sustain severe thermal shock and the thermal gradients induced by cooling and, thus, is a viable candidate for cooled components.

Bhatt, Ramakrishna T.↗
NASA NTRS2008

Exploration Supply Chain Simulation

The Exploration Supply Chain Simulation project was chartered by the NASA Exploration Systems Mission Directorate to develop a software tool, with proper data, to quantitatively analyze supply chains for future program planning. This tool is a discrete-event simulation that uses the basic supply chain concepts of planning, sourcing, making, delivering, and returning. This supply chain perspective is combined with other discrete or continuous simulation factors. Discrete resource events (such as launch or delivery reviews) are represented as organizational functional units. Continuous resources (such as civil service or contractor program functions) are defined as enabling functional units. Concepts of fixed and variable costs are included in the model to allow the discrete events to interact with cost calculations. The definition file is intrinsic to the model, but a blank start can be initiated at any time. The current definition file is an Orion Ares I crew launch vehicle. Parameters stretch from Kennedy Space Center across and into other program entities (Michaud Assembly Facility, Aliant Techsystems, Stennis Space Center, Johnson Space Center, etc.) though these will only gain detail as the file continues to evolve. The Orion Ares I file definition in the tool continues to evolve, and analysis from this tool is expected in 2008. This is the first application of such business-driven modeling to a NASA/government-- aerospace contractor endeavor.

Source record↗
DOE OSTI2025

Continuous snow depth and temperature measurements from dense network of above-ground distributed temperature profiling systems from 2021-09-23 to 2024-08-23, Seward Peninsula, Alaska

The dataset contains temperature measurements from distributed temperature profiling (DTP) systems (Dafflon et al., 2022; Wielandt et al., 2022; Wang et al., 2024a; Fiolleau et al., 2024) deployed vertically above the ground surface at a large number of locations from 2021 to 2024. The research is designed to improve understanding of the local heterogeneity in snow depth and snow thermal insulation dynamics, as well as their interactions in a discontinuous permafrost region (Wang et al., 2025). The DTP systems were deployed at 96 locations in a watershed along the Nome-Teller road at mile marker 27 (T27) and at 54 locations on a hillslope along the Kougarok road at mile marker 64 (K64) in the Seward Peninsula, Alaska. The probe location information is stored in Probe_locations_T27.csv and Probe_locations_K64.csv. Temperature measurements were recorded at 15-minute intervals using high-precision digital sensors (accuracy: ±0.1°C, resolution: 0.0078°C). The temperature probes, either 1.4 m or 1.6 m long, contain sensors spaced every 5 cm or 10 cm along their length. The temperature data are stored in compressed files following the format: DTP_snow_air_temperature_(site)_(start)_(end).zip, where site is either T27 or K64, and start and end represent the time series period. Within each ZIP file, individual CSV files are named by probe ID and contain temperature records at different heights above the ground surface.This dataset also includes derived snow depth time series over three snow seasons, estimated from temperature measurements. Snow depth was estimated by identifying the consecutive sensor pair that exhibited the largest drop in high-frequency temperature fluctuations (detailed in the methods). These data are stored in: Snow_depths_flags_(site)_(start)_(end).csv, which includes snow depth time series and corresponding quality flags (defined in the methods) from different probes. Additionally, the dataset includes derived metrics and supporting measurements at selected locations over two snow seasons, contributing to the manuscript of Wang et al., 2025. These locations were chosen based on the availability of high-quality snow depth time series during both seasons. The additional data include: (1) Air temperature proxies measured from the top sensors on the pole when they were not buried by snow, stored in Air_temperature_proxies_(site)_(start)_(end).csv (2) Ground interface temperature, recorded at 3 cm above the ground, stored in Ground_interface_temperature_(site)_(start)_(end).csv (3) Site characteristics, including vegetation height, elevation, and the topographic position index (TPI) within a 50 m radius, stored in Selected_probe_locations_gps_vegheight_tpi_elevation_(site).csv. These metrics were derived from 1 m resolution summer LiDAR-based digital elevation models and digital surface models from Singhania et al., 2023, DOI:10.5440/1832016. Metadata files include data descriptions (_dd.csv) for tabular data. All included files are listed and described in xxxx_flmd.csv.This dataset is an updated version of a previous archive (Wang et al., 2024b, DOI: 10.15485/2475020), incorporating multiple seasons and improved snow depth estimation. Please note that due to large amount of information present in this dataset, many specificities associated with the acquisition of snow temperature, air temperature proxy and estimation of snow depth, and the future archiving of additional datasets on the soil temperature, thaw depth and soil characteristics at these locations, the author would welcome being contacted by people planning to use this dataset.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1984

LANDSAT instruments characterization

Several studies were performed using LANDSAT-4 and -5 simultaneous overpath data 40608-15472 and 50014-15465 over Pensacola, FL. The overlap region of these two scenes was determined visually on the IAT and then sampled into 32 x 32 segments. The mean and standard deviation (SD) for each segment were calculated. In general, the plots of the means of LANDSAT-4 versus LANDSAT-5 lie on the diagonal line. Some of the data lie out of the diagonal line, which indicates a possible bidirectional observation effect occurs. In addition to editing the five FCL files on CALDUMP tapes into seven 1000 minor frame (MF). CAL files, program LEE.FOR was modified to use information from start of shutter obscuration extracted from program START.FOR to create seven 200 MF.CAL files that can be run through the current TRAPP program for TM sensor characterization. The location of start of shutter obscuration was determined for both LANDSAT-4 and -5.

Lee, Y.↗
DOE OSTI2026

MCNPy

SAND2026-20425O MCNPy runs and analyzes simulations from MCNP, a software that models radiation transport of neutrons and gamma rays. MCNPy uses Python to start MCNP, retrieve event data files, and convert them into graph structures for detailed analysis. It offers visualization tools, including 2D views of particle histories, making complex simulation data easier to interpret for researchers and engineers. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Nowack, Aaron [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2026

Raw Lidar and Camera Data Synchronized with Precipitation and Present Weather Data

As part of the sensor characterization task of the SMART 2.0 project, this dataset includes raw data from three spinning lidars ([Ouster OS2-128](https://ouster.com/products/scanning-lidar/os2-sensor/), [Velodyne Puck (VLP-16)](https://velodynelidar.com/products/puck/), and [Velodyne Ultra Puck (VLP-32)](https://velodynelidar.com/products/ultra-puck/)), one camera ([Mako G-319](https://www.alliedvision.com/en/camera-selector/detail/mako/g-319/)), and one present weather sensor ([Vaisala FD-70](https://www.vaisala.com/en/products/weather-environmental-sensors/forward-scatter-fd70)). All data were synchronized, with the log start time indicated in the file name (HHMMSS). The data can be filtered by date, log time (HHMMSS), sensor, frame ID, and weather classification. These data were gathered statically at the Argonne Testbed for Multiscale Observational Science (ATMOS). Two target stop signs were placed in view of the sensors to contribute a target for comparing sensor data under different conditions. The weather data for each day are stored in netCDF “.nc” files. The lidar data contain the X, Y, Z, intensity, reflectivity, and ring from Ouster OS2-128 rev6, Velodyne VLP-16, and Velodyne VLP-32 lidars. ![raw lidar image](LiDAR_pointcloud_ATMOS.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗