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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 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Can Tracked Great Frigatebirds (Fregata minor) be Used to Measure the Dynamics of the Planetary Boundary Layer Height?

Observing the dynamics of the planetary boundary layer (PBL) will require creative combinations of space-based and in-situ measurements. Animals fitted with biologgers have been used to gather data and parameterize physical models in various settings and may be a useful addition to the suite of space-based measurements that NASA is considering. For example, great frigatebirds (Fregata minor) generally fly below 700m but have been shown to routinely climb up to 2,000 meters during the day, occasionally reaching heights over 4,000 meters. This suggests that they may be tracking boundary layer height dynamics as they soar and glide on air currents with little energetic cost. We show here that the high-altitude climbing heights of great frigatebirds carrying GPS/accelerometer biologgers (e-obs GmbH Bird Solar 15g) at Palmrya Atoll, Pacific Ocean, are consistent with the long-term climatology (2006-2019) of the local planetary boundary layer height, and that these soaring heights can be reliably extracted from the tag data. Additional study is needed to determine how well movement data from tracked birds can constrain PBL height, but this work demonstrates the potential for animal-borne sensors to complement space-based measurements. Biologging data were contributed by the USGS and the Nature Conservancy's Palmyra Bluewater Research (PBR) study and analyzed as part of NASA’s Internet of Animals project.

Planetary Boundary Layer Height↗
DOE OSTI2025

Data Selection Improvement For MicroBooNE

Data selection is an extremely important part of data analysis for any experiment. Finding a physics result is often the result of sifting through a massive amount of data, keeping data that we believe to be signal, and throwing out data we do not. This process is called data selection. Creating a selection algorithm is an intensive process that must balance keeping enough data to have statistics and maximizing the signal purity of that data. We also need to choose the right reconstruction method, a tool to take raw data from the detector and convert it into physics results. In this study, we used three different reconstruction tools, Pandora, WireCell, and LANTERN, for the MicroBooNE experiment in conjunction to improve the selection algorithm for analysis. For the case of this study, we look into the charged current N proton 0 pions (CCNp0$\pi$) interaction channel. This is the dominant channel for the Short Baseline Neutrino (SBN) program and is expected to be a large contributor to the Deep Underground Neutrino Experiment (DUNE). We first investigated each of the three tools to find out more about their strengths and weaknesses as reconstructions, and compared them to the truth information directly from the MicroBooNE simulation pipeline. We then put together a direct comparison of the three methods to find which method or combination of methods would return the best result for us. While the study is ongoing, we have learned a lot about data selection for the experiment and the differences between the reconstruction tools.

Dillon, Brayden [Fermilab]↗
NASA NTRS1974

Planetary entry aerothermodynamics. II - Computational analyses and flight experience

Limitations concerning the possibility to simulate all the significant flow and thermal phenomena occurring during the entry of a space vehicle into a planetary atmosphere make it necessary to rely on computational analyses to obtain the required data for the design of the spacecraft needed for the NASA missions planned for the next two decades. 'Benchmark' computer programs concerned with complete, detailed, and accurate computational solutions of entry problems are considered along with programs representing engineering approximations for cases in which the accuracy provided by the benchmark programs is not needed. The information obtainable by computational analysis has to be supplemented by actual flight experience in order to meet the goals of the NASA entry-technology program. The individual space missions planned for the coming years are examined together with the possibilities for obtaining the data needed to satisfy the entry requirements in each case.

Olstad, W. B.↗
NASA NTRS1997

Querying Semi-Structured Data

The amount of data of all kinds available electronically has increased dramatically in recent years. The data resides in different forms, ranging from unstructured data in the systems to highly structured in relational database systems. Data is accessible through a variety of interfaces including Web browsers, database query languages, application-specic interfaces, or data exchange formats. Some of this data is raw data, e.g., images or sound. Some of it has structure even if the structure is often implicit, and not as rigid or regular as that found in standard database systems. Sometimes the structure exists but has to be extracted from the data. Sometimes also it exists but we prefer to ignore it for certain purposes such as browsing. We call here semi-structured data this data that is (from a particular viewpoint) neither raw data nor strictly typed, i.e., not table-oriented as in a relational model or sorted-graph as in object databases. As will seen later when the notion of semi-structured data is more precisely de ned, the need for semi-structured data arises naturally in the context of data integration, even when the data sources are themselves well-structured. Although data integration is an old topic, the need to integrate a wider variety of data- formats (e.g., SGML or ASN.1 data) and data found on the Web has brought the topic of semi-structured data to the forefront of research. The main purpose of the paper is to isolate the essential aspects of semi- structured data. We also survey some proposals of models and query languages for semi-structured data. In particular, we consider recent works at Stanford U. and U. Penn on semi-structured data. In both cases, the motivation is found in the integration of heterogeneous data.

DATA MANAGEMENT↗
NASA NTRS2003

Greatly enhanced deep space mission data return using verv large DSN arrays

The purposes of the Array System are: To fulfill the mission needs for higher data rates in the post-2010 era - Reduce the cost per data bit by two orders of magnitude. Specific Goal: Achieve a factor of 10 increase in DSN reception capability per decade from 2012 through 2027 (an average of 1 dB per year).

array↗
NASA NTRSDate not supplied

Development of an Apparatus and Process for Precision Measurement of Cryogenic Thermal Expansion of Materials

NASA frequently needs thermal contraction data for materials to be used in cryogenic space flight missions. To satisfy this need, we developed an apparatus and a high-precision technique for performing such measurements using a commercial fiber-optic-based position sensor. We describe the measurement process and its verification using a copper sample. We also present data for two materials which we characterized for potential NASA use.

Cryogenic↗
NASA NTRS1994

A format for the interchange of scheduling models

In recent years a variety of space-activity schedulers have been developed within the aerospace community. Space-activity schedulers are characterized by their need to handle large numbers of activities which are time-window constrained and make high demands on many scarce resources, but are minimally constrained by predecessor/successor requirements or critical paths. Two needs to exchange data between these schedulers have materialized. First, there is significant interest in comparing and evaluating the different scheduling engines to ensure that the best technology is applied to each scheduling endeavor. Second, there is a developing requirement to divide a single scheduling task among different sites, each using a different scheduler. In fact, the scheduling task for International Space Station Alpha (ISSA) will be distributed among NASA centers and among the international partners. The format used to interchange scheduling data for ISSA will likely use a growth version of the format discussed in this paper. The model interchange format (or MIF, pronounced as one syllable) discussed in this paper is a robust solution to the need to interchange scheduling requirements for space activities. It is highly extensible, human-readable, and can be generated or edited with common text editors. It also serves well the need to support a 'benchmark' data case which can be delivered on any computer platform.

Jaap, John P.↗
NASA NTRS1992

Data distribution satellite

The Data Distribution Satellite (DDS), operating in conjunction with the planned space network, the National Research and Education Network and its commercial derivatives, would play a key role in networking the emerging supercomputing facilities, national archives, academic, industrial, and government institutions. Centrally located over the United States in geostationary orbit, DDS would carry sophisticated on-board switching and make use of advanced antennas to provide an array of special services. Institutions needing continuous high data rate service would be networked together by use of a microwave switching matrix and electronically steered hopping beams. Simultaneously, DDS would use other beams and on board processing to interconnect other institutions with lesser, low rate, intermittent needs. Dedicated links to White Sands and other facilities would enable direct access to space payloads and sensor data. Intersatellite links to a second generation ATDRS, called Advanced Space Data Acquisition and Communications System (ASDACS), would eliminate one satellite hop and enhance controllability of experimental payloads by reducing path delay. Similarly, direct access would be available to the supercomputing facilities and national data archives. Economies with DDS would be derived from its ability to switch high rate facilities amongst users needed. At the same time, having a CONUS view, DDS would interconnect with any institution regardless of how remote. Whether one needed high rate service or low rate service would be immaterial. With the capability to assign resources on demand, DDS will need only carry a portion of the resources needed if dedicated facilities were used. Efficiently switching resources to users as needed, DDS would become a very feasible spacecraft, even though it would tie together the space network, the terrestrial network, remote sites, 1000's of small users, and those few who need very large data links intermittently.

Stevens, Grady H.↗
DOE OSTI2025

Circularity Futures Workshop Series: Summary Report

The aim of this report is to synthesize key feedback received from the three-part Circularity Futures workshop series held in Spring 2024. The workshop series was conducted by the National Renewable Energy Laboratory (NREL) on behalf of U.S. Department of Energy, Office Energy Efficiency and Renewable Energy (EERE), and was broken into three workshops: Workshop 1 - Circularity Analysis Needs and Priorities; Workshop 2 - Circularity Metrics and Indicators; and Workshop 3 - Circularity Data. Together, the workshops focused on identifying the existing priorities and gaps in the circularity modeling space, understanding different stakeholders' use and interpretation of circularity metrics and indicators, identifying common data gaps and data quality challenges, and assessing the robustness of available solutions. The workshop series brought a diverse group of stakeholders - including representatives from U.S. government offices, national labs, nonprofit organizations, industry, and academia - to collect first-hand feedback on needs, priorities, challenges and opportunities in the circularity modeling and analysis space. The workshop discussions highlighted numerous common needs, priorities and challenges among the interviewed groups. Several topics were frequently discussed, including: 1) Circularity as a pathway for sustainable economic growth: While circularity is generally defined in terms of resource conservation and reducing wasteful disposal of materials, participants agreed that circular strategies should serve broader economic, environmental, and social goals. It is therefore crucial for circularity analysis to look beyond waste reduction and instead evaluate a variety of impact metrics such as cost savings, job creation, air quality, and pollutant emissions. Mutli-criteria decision-making frameworks may be useful for making sense of disparate metrics and evaluating tradeoffs between impact categories.; 2) Economic and social factors are not well understood: Underdevelopment of existing end-of-life (EOL) management infrastructure, inconsistent standardization codes and policy space in reusing recycled content, and suboptimal collection and sorting strategies collectively contribute to uncertainty about the economic potential of circular pathways. The latter observation is consistent among all technologies but more emphasized for renewable energy systems. Social impacts of circularity practices are less understood and less researched than other sustainability aspects.; 3) Inconsistent methods for assessing emerging technologies: LCA and TEA results vary widely depending on the assumptions made with regards to market adoption of new technologies. Emerging technologies suffer limited availability of data needed to conduct a robust circularity analysis. Yet, understanding projected impacts of proposed nascent technology is a key need for different stakeholder groups.; and 4) Lack of temporally and geospatially explicit data: There is a need for open data that represents variations in circularity technologies over time and location. The lack thereof leads to aggregated and potentially misrepresented results in circularity analysis. Sensitivity analyses should be included to verify whether options perceived as more sustainable align with real-world practices.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2000

Wavelet-Based Interpolation and Representation of Non-Uniformly Sampled Spacecraft Mission Data

A well-documented problem in the analysis of data collected by spacecraft instruments is the need for an accurate, efficient representation of the data set. The data may suffer from several problems, including additive noise, data dropouts, an irregularly-spaced sampling grid, and time-delayed sampling. These data irregularities render most traditional signal processing techniques unusable, and thus the data must be interpolated onto an even grid before scientific analysis techniques can be applied. In addition, the extremely large volume of data collected by scientific instrumentation presents many challenging problems in the area of compression, visualization, and analysis. Therefore, a representation of the data is needed which provides a structure which is conducive to these applications. Wavelet representations of data have already been shown to possess excellent characteristics for compression, data analysis, and imaging. The main goal of this project is to develop a new adaptive filtering algorithm for image restoration and compression. The algorithm should have low computational complexity and a fast convergence rate. This will make the algorithm suitable for real-time applications. The algorithm should be able to remove additive noise and reconstruct lost data samples from images.

Bose, Tamal↗
NASA NTRS2019

Citizen Science Twitter Data Management for Earth Science Applications

Social media data can provide useful real-time and historical information relating to the natural world, but managing this data poses challenges. Scientists at GES DISC are exploring the potential of Twitter data to augment precipitation data from the Global Precipitation Measurement (GPM) mission. However, the format of Twitter data is unconventional in the context of NASA data centers, resulting in frustration for scientists who need to work with the data. This study investigated procedures and standards needed to properly manage Twitter data to make them compatible with these data centers. After comparing databases, the study found that the MongoDB database was best suited for the storage of raw Twitter data due to its flexibility, ability to be accessed by multiple users, and querying functionality. The study used the Python package Zarr to transform processed Twitter data into a gridded format similar to that of satellite data. Each Tweet was mapped onto a time-space grid; each grid location contained information about Tweet attributes and precipitation. The study developed a pipeline for downloading, storing, and gridding Twitter data and transformed Twitter data into an understandable format for users of NASA satellite data.

Li, Rachel↗
DOE OSTI2025

PDV Methods and Analysis for Surveillance of Explosive Components

The Weapons Evaluation Test Laboratory (WETL) at Sandia is collaborating with Lawrence Livermore National Laboratory (LLNL) to enhance explosive surveillance testing by integrating Photon Doppler Velocimetry (PDV) data. This project has streamlined the testing environment, reducing hardware costs and training needs while improving data collection efficiency and usability for lab technicians.

Kress, Matthew Kip [Sandia National Laboratories (↗
NASA NTRS1992

Residual acceleration data on IML-1: Development of a data reduction and dissemination plan

The main thrust of our work in the third year of contract NAG8-759 was the development and analysis of various data processing techniques that may be applicable to residual acceleration data. Our goal is the development of a data processing guide that low gravity principal investigators can use to assess their need for accelerometer data and then formulate an acceleration data analysis strategy. The work focused on the flight of the first International Microgravity Laboratory (IML-1) mission. We are also developing a data base management system to handle large quantities of residual acceleration data. This type of system should be an integral tool in the detailed analysis of accelerometer data. The system will manage a large graphics data base in the support of supervised and unsupervised pattern recognition. The goal of the pattern recognition phase is to identify specific classes of accelerations so that these classes can be easily recognized in any data base. The data base management system is being tested on the Spacelab 3 (SL3) residual acceleration data.

Rogers, Melissa J. B.↗
NASA NTRS2012

Continental-scale Validation of MODIS-based and LEDAPS Landsat ETM+ Atmospheric Correction Methods

The potential of Landsat data processing to provide systematic continental scale products has been demonstrated by several projects including the NASA Web-enabled Landsat Data (WELD) project. The recent free availability of Landsat data increases the need for robust and efficient atmospheric correction algorithms applicable to large volume Landsat data sets. This paper compares the accuracy of two Landsat atmospheric correction methods: a MODIS-based method and the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) method. Both methods are based on the 6SV radiative transfer code but have different atmospheric characterization approaches. The MODIS-based method uses the MODIS Terra derived dynamic aerosol type, aerosol optical thickness, and water vapor to atmospherically correct ETM+ acquisitions in each coincident orbit. The LEDAPS method uses aerosol characterizations derived independently from each Landsat acquisition and assumes a fixed continental aerosol type and uses ancillary water vapor. Validation results are presented comparing ETM+ atmospherically corrected data generated using these two methods with AERONET corrected ETM+ data for 95 10 km×10 km 30 m subsets, a total of nearly 8 million 30 m pixels, located across the conterminous United States. The results indicate that the MODIS-based method has better accuracy than the LEDAPS method for the ETM+ red and longer wavelength bands.

Landsat↗
NASA NTRS2015

Evolvable Cryogenics (ECRYO) Pressure Transducer Calibration Test

This paper provides a summary of the findings of recent activities conducted by Marshall Space Flight Center's (MSFC) In-Space Propulsion Branch and MSFC's Metrology and Calibration Lab to assess the performance of current "state of the art" pressure transducers for use in long duration storage and transfer of cryogenic propellants. A brief historical narrative in this paper describes the Evolvable Cryogenics program and the relevance of these activities to the program. This paper also provides a review of three separate test activities performed throughout this effort, including: (1) the calibration of several pressure transducer designs in a liquid nitrogen cryogenic environmental chamber, (2) the calibration of a pressure transducer in a liquid helium Dewar, and (3) the calibration of several pressure transducers at temperatures ranging from 20 to 70 degrees Kelvin (K) using a "cryostat" environmental chamber. These three separate test activities allowed for study of the sensors along a temperature range from 4 to 300 K. The combined data shows that both the slope and intercept of the sensor's calibration curve vary as a function of temperature. This homogeneous function is contrary to the linearly decreasing relationship assumed at the start of this investigation. Consequently, the data demonstrates the need for lookup tables to change the slope and intercept used by any data acquisition system. This ultimately would allow for more accurate pressure measurements at the desired temperature range. This paper concludes with a review of a request for information (RFI) survey conducted amongst different suppliers to determine the availability of current "state of the art" flight-qualified pressure transducers. The survey identifies requirements that are most difficult for the suppliers to meet, most notably the capability to validate the sensor's performance at temperatures below 70 K.

Diaz, Carlos E., Jr.↗
NASA NTRSDate not supplied

AI Foundation Models for Science: An Open Collaborative Initiative

Foundation Models (FMs), AI models designed to replace task-specific models, are increasingly being recognized for their versatility across numerous downstream applications. These models, trained using self-supervised techniques on any type of sequence data, circumvent the need for large annotated datasets, a major bottleneck in traditional AI model development. FMs can be applied to downstream tasks using few-shot learning and fine-tuning, significantly reducing the need for large labeled training datasets and computational resources. However, the development of FMs requires substantial resources, including access to data and compute power, expertise in the latest models, and specialized scientific knowledge for systematic evaluation. It is challenging for a single group to possess all these capabilities. To address this, NASA IMPACT has initiated an open collaborative effort, leveraging partnerships with the private sector and other groups within and outside NASA, to jointly build FMs. The overarching goal is to develop a consistent and collaborative approach to building FMs for high-value science datasets. This initiative has fostered collaboration within NASA and with external partners, including IBM Research, Clark University, DOE’s ORNL, ESA, and USGS. The effort focuses on identifying key datasets with a wide range of downstream applications, pretraining and building FMs using modified transformer architectures, evaluating compute infrastructure needs, and sharing models, pretraining and fine-tuning code, and data with the community. Furthermore, it aims to train the Earth science community to fine-tune these models for various downstream applications. Our initial effort resulted in the creation of a 100 million parameter HLS Geospatial Model within six months, which was released on HuggingFace. We are now expanding our scope to include data from weather and climate models and investigating multimodal models. We invite those interested in participating in this effort to join us by sharing their use cases, expertise, or data.

Rahul Ramachandran↗
NASA NTRS2007

ShareSync: A Solution for Deterministic Data Sharing over Ethernet

As part of upgrading the Contact Dynamics Simulation Laboratory (CDSL) at the NASA Marshall Space Flight Center (MSFC), a simple, cost effective method was needed to communicate data among the networked simulation machines and I/O controllers used to run the facility. To fill this need and similar applicable situations, a generic protocol was developed, called ShareSync. ShareSync is a lightweight, real-time, publish-subscribe Ethernet protocol for simple and deterministic data sharing across diverse machines and operating systems. ShareSync provides a simple Application Programming Interface (API) for simulation programmers to incorporate into their code. The protocol is compatible with virtually all Ethernet-capable machines, is flexible enough to support a variety of applications, is fast enough to provide soft real-time determinism, and is a low-cost resource for distributed simulation development, deployment, and maintenance. The first design cycle iteration of ShareSync has been completed, and the protocol has undergone several testing procedures including endurance and benchmarking tests and approaches the 2001ts data synchronization design goal for the CDSL.

Dunn, Daniel J., II↗