Search NASA⌕ Search

SEARCH · Search NASA

Results for “data usage”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Statistical Evaluation of Utilization of the ISS

PayLoad Utilization Modeler (PLUM) is a statistical-modeling computer program used to evaluate the effectiveness of utilization of the International Space Station (ISS) in terms of the number of research facilities that can be operated within a specified interval of time. PLUM is designed to balance the requirements of research facilities aboard the ISS against the resources available on the ISS. PLUM comprises three parts: an interface for the entry of data on constraints and on required and available resources, a database that stores these data as well as the program output, and a modeler. The modeler comprises two subparts: one that generates tens of thousands of random combinations of research facilities and another that calculates the usage of resources for each of those combinations. The results of these calculations are used to generate graphical and tabular reports to determine which facilities are most likely to be operable on the ISS, to identify which ISS resources are inadequate to satisfy the demands upon them, and to generate other data useful in allocation of and planning of resources.

Andrews, Ross↗
DOE OSTI2025

Direct I/O for RNTuple Columnar Data

RNTuple is the new columnar data format designed as the successor to ROOT’s TTree format. It allows to make use of modern hardware capabilities and is expected to be used in production by the LHC experiments during the HL-LHC. In this paper, we discuss the usage of Direct I/O to fully exploit modern SSDs, especially in the context of the recent addition of parallel RNTuple writing. We describe the alignment requirements imposed by Direct I/O and approaches to meet them for columnar data formats. Finally, we discuss performance results for both writing and reading, in synthetic benchmarks as well as real-world applications.

Hahnfeld, Jonas [CERN; Goethe U., Frankfurt (main)↗
NASA NTRS2005

Materials Control for Aerospace Applications

The distant future of mankind and the ultimate survivability of the human race, as it is known today, will depend on mans' ability to break earthly bonds and establish new territorial positions throughout the universe. Man must therefore be positioned to not only travel to, but also, to readily adapt to numerous and varying environments. For this mass migration across the galaxies nothing is as import to the human race as is NASA's future missions into Low Earth Orbit (LEO), to the moon, and/or Mars. These missions will form the building blocks to eternity for mankind. From these missions, NASA will develop the foundations for these building blocks based on sound engineering and scientific principles, both known and yet to be discovered. The integrity of the program will lead to development, tracking and control of the most basic elements of hardware production: That being development and control of applications of space flight materials. Choosing the right material for design purposes involves many considerations, such as governmental regulations associated with manufacturing operations, both safety of usage and of manufacturing, general material usage requirements, material longevity and performance requirements, material interfacing compatibility and material usage environments. Material performance is subject to environmental considerations in as much as a given material may perform exceptionally well at standard temperatures and pressures while performing poorly under non-standard conditions. These concerns may be found true for materials relative to the extreme temperatures and vacuum gradients of high altitude usage. The only way to assure that flight worthy materials are used in design is through testing. However, as with all testing, it requires both time on schedule and cost to the operation. One alternative to this high cost testing approach is to rely on a materials control system established by NASA. The NASA community relies on the MAPTIS materials control system founded at MSFC and supported by the other NASA Centers. This system is a data bank of all materials used in space flight operations. These materials are rated for several characteristics that are common concerns in high altitude or deep space usage: Odor, off gassing, material fluid compatibility, toxicity, corrosion susceptibility, stress corrosion susceptibility, etc.

Ferguson, Michael↗
DOE OSTI2024

LIB Design Module for Grid Energy System Application

We will employ a machine learning approach with intelligent data mining and database construction to analyze enormous data repositories for identifying and extracting geographic-dependent cell design specifications from publicly accessible grid-scale energy storage usage databases in an automated way at scale.

Liu, Dianying [Pacific Northwest National Laborato↗
DOE OSTI2024

Radioisotope Analysis of Wastewater from Livermore Site Retention Tanks by Gel Laboratory Gross Alpha, Gross Beta and Tritium Sampling Method

Lawrence Livermore National Laboratory discharged approximately 4.4% of the City of Livermore’s total wastewater in 2022 (LLNL’s Annual Site Environmental Report, Chapter 5, 2022). This volume includes wastewater from Sandia National Laboratories (SNL) and some process wastewater from Site 300. Due to the high volume and constituents of the discharge, LLNL works alongside the City of Livermore under permit #1250, requiring wastewater generated to be monitored and sampled in accordance with permit limits. Process wastewater, from buildings with the highest risk to sewer, is collected by wastewater retention tanks throughout the Livermore Site and sampled prior to discharge. Domestic wastewater directly discharges to sanitary sewer. To maintain permit requirements and ensure proper wastewater discharge practices, an internal wastewater audit was conducted during the summer of 2023. Current wastewater practices, regulatory knowledge and risk management across various Livermore Site buildings were evaluated. Workspaces connected to a wastewater retention tank and sanitary sewer drains were major focus areas. Data collected from walk-throughs prompted further evaluation as many practices were reported to be done based on historical usage. A table of concerns was created to showcase reasons for auditing and proceeding action. An analysis of current and historical retention tank usage throughout LLNL Livermore Site buildings with radioisotope results over a 5-year period from 2019 to 2024, was done to assess building trends and any significant changes throughout the 5-year period. Analytes evaluated were Gross Alpha, Gross Beta and Tritium (GABT) of eleven buildings at the Livermore Site, posing the highest risk to sanitary sewer for radioisotopes.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1974

A real-time digital computer program for the simulation of a single rotor helicopter

A computer program was developed for the study of a single-rotor helicopter on the Langley Research Center real-time digital simulation system. Descriptions of helicopter equations and data, program subroutines (including flow charts and listings), real-time simulation system routines, and program operation are included. Program usage is illustrated by standard check cases and a representative flight case.

Houck, J. A.↗
NASA NTRS1988

An operations manual for the digital data system

The Digital Data System (DDS) was designed to incorporate the analog-to-digital conversion process into the initial data acquisition stage and to store the data in a digital format. This conversion is done as part of the acquisition process. Consequently, the data are ready to be analyzed as soon as the test is completed. This capability permits the researcher to alter test parameters during the course of the experiment based on the information acquired in a prior portion of the test. The DDS is currently able to simultaneously acquire up to 10 channels of data. The purpose of this document is fourfold: (1) to describe the capabilities of the hardware in sufficient detail to allow the reader to determine whether the DDS is the optimum system for a particular experiment; (2) to present some of the more significant software developed to provide analyses within a short time of the completion of data acquisition; (3) to provide the reader with sample runs of major software routines to demonstrate their convenience and simple usage; and (4) a portion of the document is used to describe software which uses an FFT-box to provide a means of comparison against which the DDS can be checked.

Jones, Michael G.↗
NASA NTRS1994

A Review of Transmission Diagnostics Research at NASA Lewis Research Center

This paper presents a summary of the transmission diagnostics research work conducted at NASA Lewis Research Center over the last four years. In 1990, the Transmission Health and Usage Monitoring Research Team at NASA Lewis conducted a survey to determine the critical needs of the diagnostics community. Survey results indicated that experimental verification of gear and bearing fault detection methods, improved fault detection in planetary systems, and damage magnitude assessment and prognostics research were all critical to a highly reliable health and usage monitoring system. In response to this, a variety of transmission fault detection methods were applied to experimentally obtained fatigue data. Failure modes of the fatigue data include a variety of gear pitting failures, tooth wear, tooth fracture, and bearing spalling failures. Overall results indicate that, of the gear fault detection techniques, no one method can successfully detect all possible failure modes. The more successful methods need to be integrated into a single more reliable detection technique. A recently developed method, NA4, in addition to being one of the more successful gear fault detection methods, was also found to exhibit damage magnitude estimation capabilities.

Zakajsek, James J.↗
NASA NTRS1995

A seismic data compression system using subband coding

This article presents a study of seismic data compression techniques and a compression algorithm based on subband coding. The algorithm includes three stages: a decorrelation stage, a quantization stage that introduces a controlled amount of distortion to allow for high compression ratios, and a lossless entropy coding stage based on a simple but efficient arithmetic coding method. Subband coding methods are particularly suited to the decorrelation of nonstationary processes such as seismic events. Adaptivity to the nonstationary behavior of the waveform is achieved by dividing the data into separate blocks that are encoded separately with an adaptive arithmetic encoder. This is done with high efficiency due to the low overhead introduced by the arithmetic encoder in specifying its parameters. The technique could be used as a progressive transmission system, where successive refinements of the data can be requested by the user. This allows seismologists to first examine a coarse version of waveforms with minimal usage of the channel and then decide where refinements are required. Rate-distortion performance results are presented and comparisons are made with two block transform methods.

Kiely, A. B.↗
NASA NTRS2010

Exploiting the Free Landsat Archive for Operational Monitoring of Ecosystem Condition and Change Across the Chesapeake Bay Watershed

For the first time, all imagery acquired by the Landsat series of satellites is being made available by the USGS to users at no cost. This represents a key opportunity to use Landsat in a truly operational monitoring framework: large regions of the U.S. such as the Chesapeake Bay Watershed can now be analyzed using "wall-to-wall" imagery at timescales from approximately 1 month to several years. With the future launch of the Landsat Data Continuity Mission (LDCM) and Decadal Survey missions such as the hyperspectral HyspIRI, it is imperative to develop robust processing systems to perform annual ecosystem assessments over large regions such as the Chesapeake Bay. We have been working at NASA's Goddard Space Flight Center (GSFC) to develop an integrative framework for inserting 30m, annual, Landsat based data and derived products into the existing decision support system for the Bay, with a particular focus on ecosystem condition and changes over the entire watershed. The basic goal is to use a 'stack' of Landsat imagery with 40% or less cloud cover to produce multi-date (2005-2009 period), cloud/shadow/gap-free composited surface reflectance products that will support the creation of watershed scale land cover/ use products and the monitoring of ecosystem change across the Bay. Our scientific focus extends beyond the conventional definition of land cover (i.e. a classification of vegetation type) as we propose to monitor both changes in surface type (e.g. forest to urban), vegetation structure (e.g. forest disturbance due to logging or insect damage), as well as winter crop cover. These processes represent a continuum from large, interannual changes in land cover type, to subtler, intra-annual changes associated with short-term disturbance. The free Landsat data are being processed to surface reflectance and composited using the existing Landsat Ecosystem Disturbance Adaptive Processing System here at NASA/ GSFC, and land cover products (type, tree cover, impervious cover, winter cover) are being produced using well-established decision tree and regression tree algorithms. The goal of this session is to present the data products that we have been developing to the Bay science community and to discuss potential avenues for improvements and usage of the products for decision support.

BrowndeColstoun, Eric↗
NASA NTRS2013

A Formal Basis for Safety Case Patterns

By capturing common structures of successful arguments, safety case patterns provide an approach for reusing strategies for reasoning about safety. In the current state of the practice, patterns exist as descriptive specifications with informal semantics, which not only offer little opportunity for more sophisticated usage such as automated instantiation, composition and manipulation, but also impede standardization efforts and tool interoperability. To address these concerns, this paper gives (i) a formal definition for safety case patterns, clarifying both restrictions on the usage of multiplicity and well-founded recursion in structural abstraction, (ii) formal semantics to patterns, and (iii) a generic data model and algorithm for pattern instantiation. We illustrate our contributions by application to a new pattern, the requirements breakdown pattern, which builds upon our previous work

Formal Methods↗
NASA NTRS2024

Enhancing Metal Additive Manufacturing Training with the Advanced Vision Language Model: A Pathway to Immersive Augmented Reality Training for Non-Experts

This paper introduces an innovative training system for the Renishaw AM400 metal printer, leveraging the synergy of the advanced Vision Language Model (VLM) with Augmented Reality (AR) within the Digital Twins (DT) framework. Aimed at overcoming the limitations of conventional training methods in metal additive manufacturing (AM), our system integrates AR to provide an immersive learning environment, enhancing the real-world experience with interactive digital overlays. The core of the system lies in its use of VLM, which, pre-trained on diverse datasets, excels in processing multi-modal data, thereby offering nuanced and contextually relevant guidance for trainees. Key experiments demonstrate the system’s effectiveness, particularly highlighting the usage of VLM as an Artificial Intelligence (AI) agent to integrate external tools like YOLO-v7 for valve state classification and CRAFT for control panel text recognition. This approach significantly improves recognition accuracy, operational understanding, and human–machine interaction, especially for non-expert users, making complex metal AM operations more accessible. The research not only showcases the potential of AR and VLM in industrial training but also sets a new standard for smart manufacturing practices, indicating broader applications in various industrial domains.

Metal additive manufacturing↗
NASA NTRSDate not supplied

A Hybrid Approach to Labeling Datasets in Earth Science Publications

NASA Data Centers provide the public with thousands of datasets that result in published papers, reports, and conference proceedings. Collecting accurate metrics on usage of these datasets is key to connecting different areas of knowledge and evaluating the datasets’ impact. While most of the datasets have Digital Object Identifiers (DOIs) assigned, most publications do not cite them hampering the automated search of these publications. Instead, articles mention attributes like organization, instrument, mission, variable, or a publication describing the dataset. Often only domain experts can deduce the dataset that was used in the publication text. The lack of a citation slows the spread of information and reduces the research’s impact. With thousands of papers produced each year, an automated means of labeling datasets is critical. This paper explores a hybrid approach of heuristics and a Natural Language Processing (NLP) Named Entity Recognition (NER) model to find and label the datasets used within Earth Science papers. Heuristics are used to produce the labelled sentences and any potential dataset candidates that can be derived from a sentence. The heuristic labels the sentences with the names of mission, instrument, re-analysis models, and science keywords taken from the Global Change Master Directory (GCMD) ontology. Additionally, it uses those labels to generate the dataset citation candidates. If the mission, instrument, and variable are sufficient to create the citation for the dataset the citation and the label the domain expert reviews the output without going through the NLP model. If the extracted label is not sufficient to label the dataset on its own, the sentence and its associated dataset labels will be inputted into the NER model. The model outputs the labeled sentence and the potential dataset candidates with their associated probabilities. The domain expert then reviews the NER model’s output and the correct labels are determined. The newly labelled papers can then be used as additional training data. This creates an iterative process for the approach to continuously improve. Because all the possible mentions are gathered by the model, the domain expert can quickly and easily label the papers resulting in large time savings.

Jacob Atkins↗
NASA NTRS2013

Validation of Helicopter Gear Condition Indicators Using Seeded Fault Tests

A "seeded fault test" in support of a rotorcraft condition based maintenance program (CBM), is an experiment in which a component is tested with a known fault while health monitoring data is collected. These tests are performed at operating conditions comparable to operating conditions the component would be exposed to while installed on the aircraft. Performance of seeded fault tests is one method used to provide evidence that a Health Usage Monitoring System (HUMS) can replace current maintenance practices required for aircraft airworthiness. Actual in-service experience of the HUMS detecting a component fault is another validation method. This paper will discuss a hybrid validation approach that combines in service-data with seeded fault tests. For this approach, existing in-service HUMS flight data from a naturally occurring component fault will be used to define a component seeded fault test. An example, using spiral bevel gears as the targeted component, will be presented. Since the U.S. Army has begun to develop standards for using seeded fault tests for HUMS validation, the hybrid approach will be mapped to the steps defined within their Aeronautical Design Standard Handbook for CBM. This paper will step through their defined processes, and identify additional steps that may be required when using component test rig fault tests to demonstrate helicopter CI performance. The discussion within this paper will provide the reader with a better appreciation for the challenges faced when defining a seeded fault test for HUMS validation.

Dempsey, Paula↗
NASA NTRS2024

Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM2.5 Monitoring in Accra, Ghana

Particulate matter air pollution is a leading cause of global mortality, particularly in Asia and Africa. Addressing the high and wide-ranging air pollution levels requires ambient monitoring, but many low- and middle-income countries (LMICs) remain scarcely monitored. To address these data gaps, recent studies have utilized low-cost sensors. These sensors have varied performance, and little literature exists about sensor intercomparison in Africa. By colocating 2 QuantAQ Modulair-PM, 2 PurpleAir PA-II SD, and 16 Clarity Node-S Generation II monitors with a reference-grade Teledyne monitor in Accra, Ghana, we present the first intercomparisons of different brands of low-cost sensors in Africa, demonstrating that each type of low-cost sensor PM2.5 is strongly correlated with reference PM2.5, but biased high for ambient mixture of sources found in Accra. When compared to a reference monitor, the QuantAQ Modulair-PM has the lowest mean absolute error at 3.04 μg/m3, followed by PurpleAir PA-II (4.54 μg/m3) and Clarity Node-S (13.68 μg/m3). We also compare the usage of 4 statistical or machine learning models (Multiple Linear Regression, Random Forest, Gaussian Mixture Regression, and XGBoost) to correct low-cost sensors data, and find that XGBoost performs the best in testing (R2: 0.97, 0.94, 0.96; mean absolute error: 0.56, 0.80, and 0.68 μg/m3 for PurpleAir PA-II, Clarity Node-S, and Modulair-PM, respectively), but tree-based models do not perform well when correcting data outside the range of the colocation training. Therefore, we used Gaussian Mixture Regression to correct data from the network of 17 Clarity Node-S monitors deployed around Accra, Ghana, from 2018 to 2021. We find that the network daily average PM2.5 concentration in Accra is 23.4 μg/m3, which is 1.6 times the World Health Organization Daily PM2.5 guideline of 15 μg/m3. While this level is lower than those seen in some larger African cities (such as Kinshasa, Democratic Republic of the Congo), mitigation strategies should be developed soon to prevent further impairment to air quality as Accra, and Ghana as a whole, rapidly grow.

Humidity↗
DOE OSTI2025

AI-based Cyber Event OSINT via Twitter Data

Open-Source Intelligence (OSINT) is largely regarded as a necessary component for cybersecurity intelligence gathering to secure network systems. With the advancement of artificial intelligence (AI) and increasing usage of social media, like Twitter, we have a unique opportunity to obtain and aggregate information from social media. In this study, we propose an AI-based scheme capable of automatically pulling information from Twitter, filtering out security-irrelevant tweets, performing natural language analysis to correlate the tweets about each cybersecurity event (e.g., a malware campaign), and validating the information. This scheme has many applications, such as providing a means for security operators to gain insight into ongoing events and helping them prioritize vulnerabilities to deal with. To give examples of the possible uses, we present three case studies demonstrating the event discovery and investigation processes.

Dale, Dakota↗
DOE OSTI2024

A Stochastic Framework for Estimating Load Profiles at EV Fast Charging Stations

This paper formulates a methodology for estimating the average daily load profiles of EV fast charging stations over a planning horizon of five to ten years. The developed methodology uses historic vehicle registration data, state-level EV adoption targets, seasonal driving patterns, local demographics, competition, and traffic volume information to predict average station usage. Through Monte Carlo simulations, an average daily load profile is obtained for each month in the planning horizon, and prediction uncertainty is quantified. The proposed framework will facilitate the accurate estimation of energy and demand costs incurred by the charging station over the planning period, thereby informing return-on-investment calculations.

Biswas, Shuchismita↗
DOE OSTI2025

Data Management in the Continuum: Cross-facility Object-based Data Transfers

Scientific workflows are evolving from relying on a monolithic storage subsystem at a single High-Performance Computing (HPC) facility to using geographically distributed file systems, repositories, and cloud storage. As a result, storing, accessing, transferring, and managing scientific data have become highly complex and prone to performance inefficiencies. This paper delves into these challenges by exploring an optimized end-to-end interface designed to seamlessly connect various local and remote storage systems, enabling efficient data movement of objects across HPC–Cloud and HPC–HPC environments. We showcase this capability through an object-focused data management runtime system, discuss the effects of relaxed consistency semantics in distributed object scenarios, and illustrate its application in an earthquake simulation workflow. Besides reducing the amount of data by selectively transferring regions of interest, our facility-local results achieved a speedup of 45 × over an optimized HDF5 usage and 15 × over the HDF5 with caching by using the new interface in PDC-XF.

Bez, Jean Luca↗