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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Digital Twin Technology for Aviation

As technology progresses, so have the tools for data visualization. This project presents a digital twin model of the San Francisco airport displaying a 10-minute window of historical flight data, visualizing the trajectory data of airplanes and vehicles in three dimensions. Multiple different cameras where implemented to fully utilize the 3D visualization. This is a 100:1 feet scale model created in Autodesk Maya, using the airport center as the origin and recalculating all coordinates accordingly featuring the airport, some surrounding buildings, and the bay. For this project, six different models of airplanes were modeled at a 50:1 feet scale with texturing to mimic real-world aircraft models along with certain airlines. The animation is driven through archived data captured from NASA’s Sherlock Open-Data Portal, cleaned of noisy data points, processed into useable data formats, and implemented into a Maya ASCII file of animation paths with the corresponding previously-stated airplane models attached all using Java based conversion program.

Aleksander Schade↗
DOE OSTI2025

Technical Track on Biomass Carbon Removal and Storage (BiCRS): Mapping bioresources, phase 1 - Consistency check comparing Mission Innovation’s Data Visualization Tool for Bioresources and the Clean Energy Ministerial Biofuture Initiative Global Biomass data accessible via the US Department of Energy’s Bioenergy Knowledge Discovery Framework (KDF)

The Mission Innovation (MI) Carbon Dioxide Removal (CDR) Mission, Technical Track on Biomass Carbon Dioxide Removal and Storage (BiCRS), has produced a biomass resource database for its members. In parallel, Oak Ridge National Laboratory (ORNL) developed the International Feedstock Reporting data portal—herein referred to as the CEM Biofuture-KDF data—on behalf of the Clean Energy Ministerial Biofuture Initiative (CEM Biofuture), as a specific task under Biofuture’s 2024–25 Action Plan. This work was conducted at the request of CEM Biofuture and funded by the U.S. Department of Energy in support of that initiative, and it is hosted within DOE’s Knowledge Discovery Framework (KDF).

09 BIOMASS FUELS↗
NASA NTRS2005

An XMM-Newton Search for Crab-like Supernova Remnants

The primary goals of the study are to search for evidence of non-thermal emission that would suggest the presence of a pulsar in this compact SNR. We have performed the reduction of the EPIC data for this observation, cleaning the data to remove time intervals of enhanced particle background, and have created maps in several energy bands, and on a variety of smoothing scales. We find no evidence for emission from the SNR. Given the small angular size of the SNR, we conclude that rather than being a young remnant, it is actually fairly old, but distant. At its current stage of evolution, the remnant shell has apparently entered the radiative phase, wherein the shell temperature has cooled sufficiently to be either below X-ray-emitting temperatures or at temperatures easily absorbed the foreground interstellar material. We have thus concluded that this SNR is not a viable candidate for a young ejecta-rich or pulsar-driven SNR.

Mushotzky, Richard↗
NASA NTRS1986

An experimental study of the aerodynamics of a NACA 0012 airfoil with a simulated glaze ice accretion

An experimental study was conducted in the Ohio State University subsonic wind tunnel to measure the detailed aerodynamic characteristics of an airfoil with a simulated glaze ice accretion. A NACA 0012 model with interchangeable leading edges and pressure taps every one percent chord was used. Surface pressure and wake data were taken on the airfoil clean, with forced transition and with a simulated glaze ice shape. Lift and drag penalties due to the ice shape were found and the surface pressure clearly showed that large separation bubbles were present. Both total pressure and split-film probes were used to measure velocity profiles, both for the clean model and for the model with a simulated ice accretion. A large region of flow separation was seen in the velocity profiles and was correlated to the pressure measurements. Clean airfoil data were found to compare well to existing airfoil analysis methods.

Bragg, M. B.↗
NASA NTRS2020

ICESat-2 Tracking App for Public Engagement

Information regarding the predicted ground tracks of Earth observing satellites is typically difficult to find and understand for the general public. This paper will describe and demonstrate an iOS mobile application for tracking NASA’s ICESat-2 (Ice, Cloud, and land Elevation Satellite 2), making it easier for users to see exactly when the satellite will be passing over any location in the world. ICESat-2, launched in 2018, uses green lasers to track elevation changes in polar ice and indirectly measure trees, land, and water, providing a precise height map of our planet. Most satellite tracking mobile apps display information about where a specific satellite is at that given moment and when it will be passing over the user’s location in the near future. ICESat-2 the app differs by allowing users to search for data about the satellite’s future flybys relative to a specific search location and radius that they get to choose. The search results include points up to three months into the future. By supplying user-centric results, users are provided relevant data in an easy-to- access manner. Additionally, this data is very clear to visualize in-app through maps, pins, and ground track lines. This becomes an incredibly useful tool not only to plan an observation of the satellite as it passes, but also for knowing when elevation data for a specific area will be available. In addition, this flyby information helps students and citizen scientists take more valuable tree height measurements to better validate the elevation data collected by the satellite. To achieve this outreach product, three key components were developed. These include a Python script to simplify the raw ground track data, a Node.js server that actively takes requests, and the iOS app itself. From initial beta tests, it has been noted that providing users with a map for visual awareness of search radius and result coordinates gives them a more comprehensive understanding of the data. Overall, by cleaning the raw data and making it available in a much more accessible and easier to understand way, ICESat-2 the app has exceeded expectations in providing educational and exciting data for all.

Harbeck, Kaitlin↗
DOE OSTI2026

Automated point dendrometer, soil moisture and temperature, and meteorological variables datasets, Oct 2024 – Nov 2025, G.A. Pearson Natural Area, Flagstaff, AZ, USA

This data package includes parsed, cleaned, and calibrated data from 48 TOMST automated point dendrometers, 48 TOMST 15 cm soil moisture sensors, and 12 TOMST 30 cm soil moisture sensors. The point dendrometers were cleaned with the “dendRoAnalyst” package in RStudio. The soil sensors were cleaned and calibrated for volumetric water content (VWC) with the “myClim” package in RStudio using the soil texture of the site (sandy clay loam). Additionally, this data package also includes raw data from 2 METER weather stations. Dendrometers and soil sensors have both their sensor ID, as well as the ID for the specific tree they were instrumented on at the G.A. Pearson Natural Area (GPNA) site and their experimental group. The purpose of these data is to understand how ponderosa pine trees in restored (thinned and burned) vs. unrestored (no treatment) areas are responding to drought and seasonal precipitation. These data use radial growth and soil moisture data to answer the following question: how are active season length, growth on different time scales (weekly, monthly, seasonally, and annually), growth during dry periods and after precipitation events, and environmental and biological drivers of radial growth different between restored versus unrestored areas?

Air temperature↗
DOE OSTI2025

Review of Presentation by Carter Wolf

The presentation was informative on the different techniques used to study the electronic dynamics of materials on the femtosecond scale, such as ARPES and TR ARPES. The presentation also discussed in depth code analysis, and the procedure used to remove noise from noisy data via machine learning procedures. The presentation first focused on an overview of the ARPES technique, and how TR-ARPES differs from it. The presentation then explored computational techniques used for fitting ARPES and TR-ARPES data, using the pyARPES framework. After that, the presentation discussed machine learning approaches for denoising noisy data such as Noise2Noise and Noise2Self. The main issue of denoising existing ARPES data and the necessity of machine learning approaches due to the lack of clean ARPES data were very clearly articulated as a major part of the project. The implementation, training and refinement and optimization were clearly detailed, along with a full documentation of attempts that worked and attempts that did not, thus thoroughly and clearly showing the research process. Experimental techniques regarding ARPES were also briefly documented.

36 MATERIALS SCIENCE↗
NASA NTRS2023

Easy, Scalable Subsetting of GEDI Point Clouds

The GEDI Subsetter, a Python tool developed for NASA’s Multi-mission Algorithm and Analysis Platform (MAAP), optimizes the accessibility and visualization of GEDI point clouds by enabling users to efficiently subset data in a convenient, scalable manner. Complex science data often requires users to learn new software skills and handle many large files. Handling and cleaning large data sets is tedious and error-prone. These challenges significantly impede analysis. One of the goals of NASA's MAAP is to provide a platform that lowers the barrier to conducting research and analysis at scale. When a group of MAAP users wanted to conduct above-ground biomass estimation using GEDI data, we found that their existing workflow for leveraging GEDI data suffered from the barriers mentioned above. Furthermore, their workflow did not scale easily beyond a small number of granules. We found that existing tools related to GEDI data retrieval and subsetting were too limiting, so the GEDI Subsetter was written to support MAAP users’ needs. Being able to run many subsetting jobs simultaneously in the MAAP, and parallelizing the code itself, has led to significant speed improvements in obtaining relevant data, reducing subsetting time from hours to minutes. MAAP users can now more quickly and easily obtain only the data relevant to their research, by choosing which GEDI collection they want to work with (L1A, L2A, L2B, or L4A), and how they want to subset it, by specifying an area of interest, a temporal range, and relevant attributes. This has significantly reduced the feedback loop for users, allowing them to much more quickly subset GEDI data and begin their analysis. Although the GEDI Subsetter originally targeted users of the MAAP, it is generalized such that it can also be used outside of the MAAP and includes a command-line interface for convenience. Furthermore, with minor modifications, it should be possible to use it with non-GEDI data as the general pattern should be applicable to other sparse/track-based sensors.

Charles Daniels↗
DOE OSTI2025

ChemEcho v1.0

ChemEcho is a tool that converts tandem mass spectra into embeddings used to build machine learning (ML) models with fully explainable predictions. It provides an API for transforming raw tandem mass spectral data into embeddings, along with functions for training and validating ML models. Additionally, it includes utilities for retrieving and cleaning training data. ChemEcho is broadly applicable in ML pipelines that use tandem mass spectra for a variety of tasks, such as chemical classification or bioactivity mining. While there are existing methods to generate embeddings from fragmentation data, ChemEcho's approach ensures that predictions remain interpretable, enabling experts to evaluate results and generate hypotheses about the underlying data.

Harwood, Thomas [Lawrence Berkeley National Labora↗
NASA NTRS1990

Spacecraft thermal blanket cleaning: Vacuum bake of gaseous flow purging

The mass losses and the outgassing rates per unit area of three thermal blankets consisting of various combinations of Mylar and Kapton, with interposed Dacron nets, were measured with a microbalance using two methods. The blankets at 25 deg C were either outgassed in vacuum for 20 hours, or were purged with a dry nitrogen flow of 3 cu. ft. per hour at 25 deg C for 20 hours. The two methods were compared for their effectiveness in cleaning the blankets for their use in space applications. The measurements were carried out using blanket strips and rolled-up blanket samples fitting the microbalance cylindrical plenum. Also, temperature scanning tests were carried out to indicate the optimum temperature for purging and vacuum cleaning. The data indicate that the purging for 20 hours with the above N2 flow can accomplish the same level of cleaning provided by the vacuum with the blankets at 25 deg C for 20 hours, In both cases, the rate of outgassing after 20 hours is reduced by 3 orders of magnitude, and the weight losses are in the range of 10E-4 gr/sq cm. Equivalent mass loss time constants, regained mass in air as a function of time, and other parameters were obtained for those blankets.

Scialdone, John J.↗
NASA NTRS1992

Spacecraft thermal blanket cleaning - Vacuum baking or gaseous flow purging

The mass losses and the outgassing rates per unit area of three thermal blankets consisting of various combinations of Mylar and Kapton, with interposed Dacron nets, were measured with a microbalance using two methods. The blankets at 25 deg C were either outgassed in vacuum for 20 hours, or were purged with a dry nitrogen flow of 3 cu. ft. per hour at 25 deg C for 20 hours. The two methods were compared for their effectiveness in cleaning the blankets for their use in space applications. The measurements were carried out using blanket strips and rolled-up blanket samples fitting the microbalance cylindrical plenum. Also, temperature scanning tests were carried out to indicate the optimum temperature for purging and vacuum cleaning. The data indicate that the purging for 20 hours with the above N2 flow can accomplish the same level of cleaning provided by the vacuum with the blankets at 25 deg C for 20 hours. In both cases, the rate of outgassing after 20 hours is reduced by 3 orders of magnitude, and the weight losses are in the range of 10E-4 gr/sq cm. Equivalent mass loss time constants, regained mass in air as a function of time, and other parameters were obtained for those blankets.

Scialdone, John J.↗
NASA NTRS1953

Measurements obtained during the glide-flight program of the Bell X-2 research airplane

Results obtained during the glide-flight program of the Bell X-2 research airplane are presented. Landing characteristics and limited data evaluating static longitudinal stability at low speeds are included. The data indicated positive static longitudinal stability in unaccelerated flight from indicated airspeeds of 152 to 178 miles per hour for the clean configuration, between 142 and 171 miles per hour with the flaps up and gear extended, and between 142 and 204 miles per hour with flaps and gear extended. A region of neutral stability, both stick fixed and stick free, was apparent between 178 and 192 miles per hour for the clean configuration. Data obtained during a turn made at an indicated airspeed of approximately 235 miles per hour with steadily increasing acceleration indicated positive stick-free and stick-fixed longitudinal stability. Stick force per unit normal acceleration was approximately 15 pounds. Pilots' notes indicated that dynamic stability in each plane of reference was apparently positive with satisfactory damping for the speed range covered. The main landing-skid surface area was enlarged 300 percent for flights 2 and 3 with a resulting improvement in landing characteristics. Average longitudinal deceleration during the ground run was decreased from 0.7 unit of acceleration for flight 1 to a value of 0.3 unit of acceleration for flights 2 and 3. Normal acceleration at the nose wheel was correspondingly reduced from 4 to 2.8 units of acceleration. The addition of inboard wing skids prevented rolling onto a wing tip during ground run.

Richard E Day↗
DOE OSTI2026

An Autonomous MCP Bridge to Rucio: Enhancing Data Management Accessibility for High Energy Physics

The Rucio Data Management System [1] is an important tool used by High Energy Physics experiments, including those at Fermi National Accelerator Laboratory, to store and manage exabyte-scale scientific datasets. Despite its central role in coordinating data across globally distributed storage sites, Rucio's command line interface (CLI) presents a steep learning curve, and makes it difficult for scientists to navigate through. To solve this issue, a containerized Model Context Protocol (MCP) [2] server was built that connects Large Language Models directly to Rucio, allowing AI agents to handle data tasks by using simple, natural language rather than memorized terminal commands. The core engineering focus of this project was moving the server away from slow terminal commands that require text parsing and replacing them with a native Python Client API toolset and a planned REST API framework. Moving to the Python API handles data operations directly in memory, which helps clear up formatting errors, provides the AI with clean, structured JSON data and speeds up tool execution. To prove that the system actually works, a benchmarking pipeline was also built with various questions to test the AI across four different model configurations. The questions included finding data scopes, tracking down specific datasets, and checking replication rules. Through benchmarking, early runs showed that with raw terminal text, the model would get confused and stuck, whereas switching to the Python API to feed the AI clean, structured data yielded massive improvement. By creating an intelligent and autonomous bridge to a storage network, this project shows how AI can be implemented in scientific data management, which ultimately helps scientists at Fermilab spend less time sorting through data and more time focusing on their experiments and analysis.

Akella, Kashyap [William Rainey Harper Coll.]↗
DOE OSTI2025

Mapping and Synthesis of International Biomass Supply Assessments

This report, Mapping and Synthesis of International Biomass Supply Assessments (or Global Biomass Resource Assessment) is the first step in a long-term process to assemble data from around the globe into a virtual repository that can be updated and provide user-friendly access to the data. The Clean Energy Ministerial (CEM) Biofuture Platform Initiative recommended that research be completed to “address the need for internationally accepted benchmarks quantifying sustainable biomass feedstock supplies.” To act upon the CEM Biofuture recommendation, in 2024, the U.S. Department of Energy (DOE) commissioned Oak Ridge National Laboratory (ORNL) to prepare this report as the primary deliverable for a one-year assignment to assemble data into a citable form that could help resolve the persistent question presented related to bioenergy policy, “Is there enough sustainable biomass?” In response to that query, this report includes information received by August 2024 from national CEM representatives, collaborators, and public sources on current and future sustainable biomass supplies in 62 nations,and subsequently documents (a) the approach used by ORNL to analyze and categorize the information received in a manner that enables aggregation and comparability; and (b) recommendations for next steps and guidelines to help others update and harmonize future assessments of global sustainable biomass supplies.

09 BIOMASS FUELS↗
DOE OSTI2024

Generic and ML Workloads in an HPC Datacenter: Node Energy, Job Failures, and Node-Job Analysis

HPC datacenters offer a backbone to the modern digital society. Increasingly, they run Machine Learning (ML) jobs next to generic, compute-intensive workloads, supporting science, business, and other decision-making processes. However, understanding how ML jobs impact the operation of HPC datacenters, relative to generic jobs, remains desirable but understudied. In this work, we leverage long-term operational data, collected from a national-scale production HPC datacenter, and statistically compare how ML and generic jobs can impact the performance, failures, resource utilization, and energy consumption of HPC datacenters. Our study provides key insights, e.g., ML-related power usage causes GPU nodes to run into temperature limitations, median/mean runtime and failure rates are higher for ML jobs than for generic jobs, both ML and generic jobs exhibit highly variable arrival processes and resource demands, significant amounts of energy are spent on unsuccessfully terminating jobs, and concurrent jobs tend to terminate in the same state. We open-source our cleaned-up data traces on Zenodo (https://doi. org/10.5281/zenodo.13685426), and provide our analysis toolkit as software hosted on GitHub (https://github.com/atlarge-research/2024-icpads-hpc-workload-characterization). This study offers multiple benefits for data center administrators, who can improve operational efficiency, and for researchers, who can further improve system designs, scheduling techniques, etc.

crossanalysis↗
NASA NTRS2007

Triaxial Probe Magnetic Data Analysis

The Triaxial Magnetic Moment Analysis software uses measured magnetic field test data to compute dipole and quadrupole moment information from a hardware element. It is used to support JPL projects needing magnetic control and an understanding of the spacecraft-generated magnetic fields. Evaluation of the magnetic moment of an object consists of three steps: acquisition, conditioning, and analysis. This version of existing software was extensively rewritten for easier data acquisition, data analysis, and report presentation, including immediate feedback to the test operator during data acquisition. While prior JPL computer codes provided the same data content, this program has a better graphic display including original data overlaid with reconstructed results to show goodness of fit accuracy and better appearance of the report graphic page. Data are acquired using three magnetometers and two rotations of the device under test. A clean acquisition user interface presents required numeric data and graphic summaries, and the analysis module yields the best fit (least squares) for the magnetic dipole and/or quadrupole moment of a device. The acquisition module allows the user to record multiple data sets, selecting the best data to analyze, and is repeated three times for each of the z-axial and y-axial rotations. In this update, the y-axial rotation starting position has been changed to an option, allowing either the x- or z-axis to point towards the magnetometer. The code has been rewritten to use three simultaneous axes of magnetic data (three probes), now using two "rotations" of the device under test rather than the previous three rotations, thus reducing handling activities on the device under test. The present version of the software gathers data in one-degree increments, which permits much better accuracy of the fit ted data than the coarser data acquisition of the prior software. The data-conditioning module provides a clean data set for the analysis module. For multiple measurements at a given degree, the first measurement is used. For omitted measurements, the missing field is estimated by linear interpolation between the two nearest measurements. The analysis module was rewritten for the dual rotation, triaxial probe measurement process and now has better moment estimation accuracy, based on the finer one degree of data acquisition resolution. The magnetic moments thus computed are used as an input to summarize the total spacecraft field.

Shultz, Kimberly↗
NASA NTRS2004

JDD, Inc. Database

JDD Inc, is a maintenance and custodial contracting company whose mission is to provide their clients in the private and government sectors "quality construction, construction management and cleaning services in the most efficient and cost effective manners, (JDD, Inc. Mission Statement)." This company provides facilities support for Fort Riley in Fo,rt Riley, Kansas and the NASA John H. Glenn Research Center at Lewis Field here in Cleveland, Ohio. JDD, Inc. is owned and operated by James Vaughn, who started as painter at NASA Glenn and has been working here for the past seventeen years. This summer I worked under Devan Anderson, who is the safety manager for JDD Inc. in the Logistics and Technical Information Division at Glenn Research Center The LTID provides all transportation, secretarial, security needs and contract management of these various services for the center. As a safety manager, my mentor provides Occupational Health and Safety Occupation (OSHA) compliance to all JDD, Inc. employees and handles all other issues (Environmental Protection Agency issues, workers compensation, safety and health training) involving to job safety. My summer assignment was not as considered "groundbreaking research" like many other summer interns have done in the past, but it is just as important and beneficial to JDD, Inc. I initially created a database using a Microsoft Excel program to classify and categorize data pertaining to numerous safety training certification courses instructed by our safety manager during the course of the fiscal year. This early portion of the database consisted of only data (training field index, employees who were present at these training courses and who was absent) from the training certification courses. Once I completed this phase of the database, I decided to expand the database and add as many dimensions to it as possible. Throughout the last seven weeks, I have been compiling more data from day to day operations and been adding the information to the database. It now consists of seven different categories of data (carpet cleaning, forms, NASA Event Schedules, training certifications, wall and vent cleaning, work schedules, and miscellaneous) . I also did some field inspecting with the supervisors around the site and was present at all of the training certification courses that have been scheduled since June 2004. My future outlook for the JDD, Inc. database is to have all of company s information from future contract proposals, weekly inventory, to employee timesheets all in this same database.

Miller, David A., Jr.↗