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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 1,261 records · Page 70

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NASA NTRS1986

The Tasseled Cap de-mystified

The fundamental concepts on which the Tasseled Cap transformations of MSS and TM data are based - particularly the identification of inherent data structures - are explained and discussed. Emphasis on the structures present in data from any given sensor, which are themselves the expression of physical characteristics of scene classes, provides a number of advantages, including (a) reduction in data volume with minimal information loss; (b) spectral features which can be applied, without re-definition or adjustment, to any data set for a given sensor; (c) spectral features which can be directly associated with important physical parameters; and (d) easier integration of data from multiple sensors.

Crist, E. P.↗

NASA NTRS1986

A specification-based approach to concurrent structure verification in multiprocessor systems

A recently initiated research project concerned with the concurrent detection of software errors and errors due to physical failures in the hardware of multiprocessor systems is described in this paper. An approach to error detection is described, which is specification based and relies on the structural verification of program control flow and data structure integrity. The techniques discussed utilize the hardware redundancy inherent in parallel processing systems to provide verification of both program structure and data concurrently with program execution.

Fuchs, W. Kent↗

NASA NTRS2014

New Approach to Monitor Transboundary Particulate Pollution over Northeast Asia

A new approach to more accurately monitor and evaluate transboundary particulate matter (PM) pollution is introduced based on aerosol optical products from Korea's Geostationary Ocean Color Imager (GOCI). The area studied is Northeast Asia (including eastern parts of China, the Korean peninsula and Japan), where GOCI has been monitoring since June 2010. The hourly multi-spectral aerosol optical data that were retrieved from GOCI sensor onboard geostationary satellite COMS (Communication, Ocean, and Meteorology Satellite) through the Yonsei aerosol retrieval algorithm were first presented and used in this study. The GOCI-retrieved aerosol optical data are integrated with estimated aerosol distributions from US EPA Models-3/CMAQ (Community Multi-scale Air Quality) v4.5.1 model simulations via data assimilation technique, thereby making the aerosol data spatially continuous and available even for cloud contamination cells. The assimilated aerosol optical data are utilized to provide quantitative estimates of transboundary PM pollution from China to the Korean peninsula and Japan. For the period of 1 April to 31 May, 2011 this analysis yields estimates that AOD as a proxy for PM2.5 or PM10 during long-range transport events increased by 117-265% compared to background average AOD (aerosol optical depth) at the four AERONET sites in Korea, and average AOD increases of 121% were found when averaged over the entire Korean peninsula. This paper demonstrates that the use of multi-spectral AOD retrievals from geostationary satellites can improve estimates of transboundary PM pollution. Such data will become more widely available later this decade when new sensors such as the GEMS (Geostationary Environment Monitoring Spectrometer) and GOCI-2 are scheduled to be launched.

Data Assimilation↗

NASA NTRS2020

Global earth mineral inventory: A data legacy

Minerals contain important clues to understanding the complex geologic history of Earth and other planetary bodies. Therefore, geologists have been collecting mineral samples and compiling data about these samples for centuries. These data have been used to better understand the movement of continental plates, the oxidation of Earth's atmosphere and the water regime of ancient martian landscapes. Datasets found at ‘RRUFF.info/Evolution’ and ‘mindat.org’ have documented a wealth of mineral occurrences around the world. One of the main goals in geoinformatics has been to facilitate discovery by creating and merging datasets from various scientific fields and using statistical methods and visualization tools to inspire and test hypotheses applicable to modelling Earth's past environments. To help achieve this goal, we have compiled physical, chemical and geological properties of minerals and linked them to the above-mentioned mineral occurrence datasets. As a part of the Deep Time Data Infrastructure, funded by the W.M. Keck Foundation, with significant support from the Deep Carbon Observatory (DCO) and the A.P. Sloan Foundation, GEMI (‘Global Earth Mineral Inventory’) was developed from the need of researchers to have all of the required mineral data visible in a single portal, connected by a robust, yet easy to understand schema. Our data legacy integrates these resources into a digestible format for exploration and analysis and has allowed researchers to gain valuable insights from mineralogical data. GEMI can be considered a network, with every node representing some feature of the datasets, for example, a node can represent geological parameters like colour, hardness or lustre. Exploring subnetworks gives the researcher a specific view of the data required for the task at hand. GEMI is accessible through the DCO Data Portal (https://dx.deepcarbon.net/11121/6200-6954-6634-8243-CC). We describe our efforts in compiling GEMI, the Data Policies for usage and sharing, and the evaluation metrics for this data legacy.

data legacy↗

DOE OSTI2020

Improved Access and Analysis of Data Provides New Opportunities for Smart Cities

Making smart, informed, data-driven energy decisions for cities requires large amounts of high-value data and analysis. Cities’ energy data has traditionally been difficult to access and costly to store and manage for city administrators. When data is available, many cities lack the expertise to properly analyze and model large data sets. NREL’s transparency and knowledge about data availability, integration, and interpretation is critical to decision-making for cities.

cities' data↗

DOE OSTI2024

Geospatial Data Platform for All

Spatiotemporal data has evolved in scale due to augmented use in cross-domain applications. Simultaneously, there is substantial growth in the availability of Geographic Information Systems (GIS) data provided by the United States Geological Survey (USGS) along with other federal, state, county, or local agencies through open-data portals and public access APIs. However, data availability does not equate with accessibility. Large-scale analyses and applications require robust, performant data management with co-location of data storage and computing. The insufficiency of data management infrastructure compels researchers to adopt ad hoc project- specific GIS data storage solutions (e.g., copying data to High-Performance computer file systems). As an ad hoc storage strategy does not scale, it hampers cross-domain analyses causing difficulty in data reuse and utilizing existing code bases. Furthermore, GIS data is complex and requires expertise to analyze and manipulate due to its intricate data structures and data-specific projection transformations. Despite the challenges, we recognize that derived GIS data products, e.g., satellite or LIDAR-based images, can be used in downstream applications such as AI by domain, but non-GIS experts. To address the data needs and overcome the challenges, we are working towards a GIS Data Platform focused on efficient data storage, data discovery and access, and an API to enable common workflows. We propose a knowledge-graph (KG) approach for data discovery, whereby datasets are semantically linked to higher- level constructs such as projects and research areas. The semantic data links enable researchers to explore datasets in a top-down approach by specifying relevant and meaningful terms (assists in finding hidden data). An advantage is that the nodes and edges in a knowledge graph create built-in semantic documentation. Deeper spatiotemporal connections between data sources can be encoded via Graph Neural Networks (GNN) (Zhang et al., 2021). The KG approach can be extended to integrate the data itself in a Virtual KG (VKG). Our work will derive inspiration from large-scale VKG efforts that have been undertaken or are currently underway as part of the OpenStreetMap project (Ding et al., 2021). For DOE Data Days, we share the proposed geospatial data platform hybrid (cloud/on-prem) architecture, our work-to-date on storing, retrieving, and transforming LiDAR and raster data relevant to two important NREL use-cases, including the Renewable Energy Potential (reV) Model, and present our proposal for a KG based data discovery engine.

data platform↗

NASA NTRS2013

The Challenges of Integrating Instrumentation with Inflatable Aerodynamic Decelerators

To realize the National Aeronautics and Space Administration s (NASA) goal of landing humans on Mars, development of technologies to facilitate the landing of heavy payloads are being explored. Current entry, decent, and landing technologies are not practical when utilizing these heavy payloads due to mass and volume constraints dictated by limitations imposed by current launch vehicle fairings. Therefore, past and present technologies are now being considered to provide a mass and volume efficient solution, including Inflatable Aerodynamic Decelerators (IADs) [1]. IAD ground and flight tests are currently being conducted to develop and characterize their performance under flight-like conditions. The integrated instrumentation systems, which are key to the performance characterization in each of these tests, have proven to be a challenge compared to the instrumentation of traditional rigid aeroshells. To overcome these challenges, flexible and embedded sensing systems have been developed, along with improved instrumenting techniques. This development opportunity faces many difficult aspects specific to inflatable structures in extreme environments. These include but are not limited to: physical flexibility, packaging, temperature, structural integration and data acquisition [2]. To better define the instrumentation challenges posed by IAD technology development, a survey was conducted to identify valuable measurements for ground and flight testing. From this survey many sensing technologies were explored, resulting in a down-selection to the most viable prospects. These systems were then iterated upon in design to determine the best integration techniques specific to a 3m and 6m stacked torus IAD. Each sensing system was then integrated and employed to support the IAD testing in the National Full-Scale Aerodynamics Complex 40 x 80 wind tunnel at NASA Ames Research Center in the summer of 2012. Another challenge that has been explored is the data acquisition of IAD sensing technologies. Traditionally all space based sensing systems transmit their data through a wired interface. This limits the amount of sensors able to be integrated within the IAD due to volume and routing restrictions of the supporting signal and excitation wires. To alleviate this situation, multiple wireless data acquisition technologies have been researched and developed through rapid prototyping efforts. The final custom multi-nodal wireless system utilized during the summer 2012 IAD test series consisted of four remote nodes and one receiving base station. The system reliably conditioned and acquired 20+ sensors over the course of the wind tunnel test series. These developments in wireless data acquisition techniques can eliminate the need for structural feedthroughs and reduce system mass associated with wiring and wire harnesses. This makes the utilization of flight instrumentation more attractive to future missions, which would result in further improved characterization of IAD technology, and overall, increased scientific knowledge regarding the response of inflatable structures to extreme entry environments. [

Swanson, Gregory T.↗

DOE OSTI2020

Measurement of the integrated luminosity of the Phase 2 data of the Belle II experiment

From April to July 2018, a data sample at the peak energy of the Υ(4S) resonance was collected with the Belle II detector at the SuperKEKB electron-positron collider. This is the first data sample of the Belle II experiment. Using Bhabha and digamma events, we measure the integrated luminosity of the data sample to be (496.3±0.3±3.0)pb –1 , where the first uncertainty is statistical and the second is systematic. This work provides a basis for future luminosity measurements at Belle II.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1973

ERTS-1 - Teaching us a new way to see.

The ERTS-1 payload is discussed, giving attention to three television cameras, which view the same area in three different spectral bands. The payload includes also a multispectral scanner subsystem and a data collection system which collects information from some 150 remote, unattended, instrumented ground platforms. Many government agencies use ERTS-1 data as integral parts of their ongoing programs. Through its EROS program, the Interior Department represents the largest single recipient and user agency of data obtained from NASA aircraft and spacecraft designed to gather repetitive information related to a wide variety of earth-science and natural-resources disciplines. Questions of environmental impact are considered together with applications in agriculture, forestry, marine resources, geography, and the survey of water resources.

Mercanti, E. P.↗

NASA NTRS1981

IPAD applications to the design, analysis, and/or machining of aerospace structures

A computer software system for the processing and integration of engineering data and programs, called IPAD (Integrated Programs for Aerospace-Vehicle Design), is described. The ability of the system to relieve the engineer of the mundane task of input data preparation is demonstrated by the application of a prototype system to the design, analysis, and/or machining of three simple structures. Future work to further enhance the system's automated data handling and ability to handle larger and more varied design problems are also presented.

Blackburn, C. L.↗

NASA NTRS1986

Monitoring East African vegetation using AVHRR data

NOAA Advanced Very High Resolution Radiometer satellite data are applied to regional vegetation monitoring in East Africa. Normalized Difference Vegetation Index (NDVI) data for a one-year period from May 1983 are used to examine the phenology of a range of vegetation types. The integrated NDVI data for the same period are compared with an ecoclimatic zone map of the region and show marked similarities. Particular emphasis is placed on quantifying the phenology of the Acacia Commiphora bushlands. Considerable variation was found in the phenology of the bushlands as determined by the satellite NDVI, and is explained through the high spatial variability in the distribution of rainfall and the resulting green-up of the vegetation. The relationship between rainfall and NDVI is further examined for selected meteorological stations existing within the bushland. A preliminary estimate is made of the length of growing season using an NDVI thresholding technique.

Justice, C. O.↗

NASA NTRS1991

Techniques for containing error propagation in compression/decompression schemes

Data compression has the potential for increasing the risk of data loss. It can also cause bit error propagation, resulting in catastrophic failures. There are a number of approaches possible for containing error propagation due to data compression: (1) data retransmission; (2) data interpolation; (3) error containment; and (4) error correction. The most fruitful techniques will be ones where error containment and error correction are integrated with data compression to provide optimal performance for both. The error containment characteristics of existing compression schemes should be analyzed for their behavior under different data and error conditions. The error tolerance requirements of different data sets need to be understood, so guidelines can then be developed for matching error requirements to suitable compression algorithms.

Kobler, Ben↗

NASA NTRS1988

Robot Design

Martin Marietta Aero and Naval Systems has advanced the CAD art to a very high level at its Robotics Laboratory. One of the company's major projects is construction of a huge Field Material Handling Robot for the Army's Human Engineering Lab. Design of FMR, intended to move heavy and dangerous material such as ammunition, was a triumph in CAD Engineering. Separate computer problems modeled the robot's kinematics and dynamics, yielding such parameters as the strength of materials required for each component, the length of the arms, their degree of freedom and power of hydraulic system needed. The Robotics Lab went a step further and added data enabling computer simulation and animation of the robot's total operational capability under various loading and unloading conditions. NASA computer program (IAC), integrated Analysis Capability Engineering Database was used. Program contains a series of modules that can stand alone or be integrated with data from sensors or software tools.

Source record↗

NASA NTRS2023

NASA’s Digital Information Platform to Accelerate the Transformation of the National Airspace System

In order to accelerate the digital transformation of airspace operations, a foundational framework and infrastructure for providing sustainable, data-driven, and cohesive decision-making digital services for both traditional and emergent air vehicles is being developed. The reference implementation of Digital Information Platform builds an ecosystem for the aviation community by providing access to a secure and trusted source of aviation data and services. Several key features and services have been implemented to enable secure data sharing, communication, and service registration on the Platform. The technical approach used to implement these features is presented here. NASA-developed integrated aviation data and machine learning based prediction services to optimize airspace operations are available on the Platform. These services are being evaluated in an operational environment by flight operators and the real-world benefits are being captured. The Platform fosters collaboration among industry and researchers to develop complex aviation services and the aim is to make it publicly accessible for consumption by the aviation community.

Digital Transformation↗

NASA NTRSDate not supplied

NASA’s Digital Information Platform to Accelerate the Transformation of the National Airspace System

Digital Transformation↗

DOE OSTI2024

Automated electrosynthesis reaction mining with multimodal large language models (MLLMs)

Leveraging the chemical data available in legacy formats such as publications and patents is a significant challenge for the community. Automated reaction mining offers a promising solution to unleash this knowledge into a learnable digital form and therefore help expedite materials and reaction discovery. However, existing reaction mining toolkits are limited to single input modalities (text or images) and cannot effectively integrate heterogeneous data that is scattered across text, tables, and figures. In this work, we go beyond single input modalities and explore multimodal large language models (MLLMs) for the analysis of diverse data inputs for automated electrosynthesis reaction mining. We compiled a test dataset of 65 articles (MERMES-T24 set) and employed it to benchmark five prominent MLLMs against two critical tasks: (i) reaction diagram parsing and (ii) resolving cross-modality data interdependencies. The frontrunner MLLM achieved ≥96% accuracy in both tasks, with the strategic integration of single-shot visual prompts and image pre-processing techniques. We integrate this capability into a toolkit named MERMES (multimodal reaction mining pipeline for electrosynthesis). Our toolkit functions as an end-to-end MLLM-powered pipeline that integrates article retrieval, information extraction and multimodal analysis for streamlining and automating knowledge extraction. This work lays the groundwork for the increased utilization of MLLMs to accelerate the digitization of chemistry knowledge for data-driven research.

Leong, Shi Xuan↗

DOE OSTI2026

A ModEx Framework for Watershed Subsurface Investigation With Limited Geophysical Data Using Machine Learning and Hydrologic Modeling

Abstract Subsurface heterogeneity influences watershed hydrology strongly but remains difficult to characterize at catchment scales with sparse and costly field data. Geophysical surveys such as electromagnetic induction (EMI) provide local spatial subsurface images yet scaling them to watershed scales and converting EMI‐derived resistivity into hydraulic properties remains a challenge. We present a Model–Experiment (ModEx) framework that integrates limited EMI data with machine learning (ML) and hydrologic modeling to improve process representation and guide field investigations. Sparse EMI surveys were scaled to the catchment scale using a Random Forest model, and the resulting resistivity fields were combined with nearby borehole constraints to parameterize a hydrologic model. The EMI‐informed hydrological simulations improved predictions of streamflow sustained by subsurface flow and shallow saturation patterns. By combining EMI data and ML with hydrologic modeling, the ModEx framework guides future subsurface surveys, providing a transferable and efficient strategy for data–model integration across diverse watersheds. Plain Language Summary Mapping the underground network of soil and rock that controls water is essential for predicting floods and droughts, but seeing underground is difficult and expensive. We cannot drill everywhere, so scientists use geophysical tools to scan broad areas. There are two key challenges: these geophysical scans are often sparse across the whole watershed, and the geophysical data is hard to translate into water‐related properties. We used artificial intelligence to solve these problems. We taught a computer to find patterns linking the limited geophysical data to the land surface properties. This allowed it to fill in the gaps and create a complete, useful subsurface map for the entire watershed. This new map improves hydrologic simulations, leading to more accurate predictions of water movement in the watershed. It also helps scientists build better models with less data and generates a priority map showing where to measure next, making future investigations more efficient. Key Points Limited EMI scaled with ML improves catchment‐scale subsurface parameterization for hydrologic models The framework integrates hydrologic modeling with limited geophysical data to support subsurface investigation design ModEx framework offers a transferable data–model integration strategy that quantifies and reduces uncertainty guiding watershed studies

Chen, Hang↗

DOE OSTI2025

Floating Wind Array Ontology and Modeling Framework

While there are many tools for designing and modeling a single floating turbine, array level design and modeling has much more to consider. Designing floating wind arrays requires a coupled approach considering many variables, from bathymetry to installation and maintenance to failure and risk analysis. With all of these considerations, an array-level modeling tool is needed to quickly evaluate array designs. The Floating Array Model (FAModel) tool developed at the National Renewable Energy Laboratory was created to fill this gap in low-fidelity array modeling. FAModel is a python framework created to streamline holistic low-fidelity floating wind modeling for array-level analysis. FAModel integrates site data and models with a variety of open-source modeling tools developed by NREL, including FLORIS, RAFT, MoorPy, and anchor capacity models. The integration of these tools allows users to quickly and holistically design an array by considering forces, area analysis, visualization, annual energy production, failure modeling, and component costs.

17 WIND ENERGY↗