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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Open Energy Data Initiative: Advancing Analytics and Research Innovation through Improved Data Access

The Open Energy Data Initiative (OEDI) aims to improve and automate access of high-value energy data sets across the U.S. Department of Energy's (DOE's) programs, offices, and national laboratories. Sponsored by DOE, this platform is being implemented by the National Renewable Energy Laboratory (NREL) to make data actionable and discoverable by researchers and industry to accelerate analysis and advance innovation. Partners at other national laboratories will help utilize the platform for analysis, can be directly involved in determining requirements, and will be resources for providing new datasets that can be shared with the public to expand innovation.

14 SOLAR ENERGY↗
DOE OSTI2015

Enabling Exascale Hardware and Software Design through Scalable System Virtualization (Final Report)

The purpose of this project has been to extend the state of the art of systems software for high-end computing (HEC) platforms, and to use systems software to better enable the evaluation of potential future HEC platforms, for example exascale platforms. Such platforms, and their systems software, have the goal of providing scientific computation at new scales, thus enabling new research in the physical sciences and engineering. Over time, the innovations in systems software for such platforms also become applicable to more widely used computing clusters, data centers, and clouds. This was a five-institution project, centered on the Palacios virtual machine monitor (VMM) systems software, a project begun at Northwestern, and originally developed in a previous collaboration between Northwestern University and the University of New Mexico. In this project, Northwestern (including via our subcontract to the University of Pittsburgh) contributed to the continued development of Palacios, along with other team members. We took the leadership role in (1) continued extension of support for emerging Intel and AMD hardware, (2) integration and performance enhancement of overlay networking, (3) connectivity with architectural simulation, (4) binary translation, and (5) support for modern Non-Uniform Memory Access (NUMA) hosts and guests. We also took a supporting role in support for specialized hardware for I/O virtualization, profiling, configurability, and integration with configuration tools. The efforts we led (1-5) were largely successful and executed as expected, with code and papers resulting from them. The project demonstrated the feasibility of a virtualization layer for HEC computing, similar to such layers for cloud or datacenter computing. For effort (3), although a prototype connecting Palacios with the GEM5 architectural simulator was demonstrated, our conclusion was that such a platform was less useful for design space exploration than anticipated due to inherent complexity of the connection between the instruction set architecture level and the microarchitectural level. For effort (4), we found that a code injection approach proved to be more fruitful. The results of our efforts are publicly available in the open source Palacios codebase and published papers, all of which are available from the project web site, v3vee.org. Palacios is currently one of the two codebases (the other being Sandia’s Kitten lightweight kernel) that underlies the node operating system for the DOE Hobbes Project, one of two projects tasked with building a systems software prototype for the national exascale computing effort.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Energy Efficient Material Processing through Automated Process Monitoring and Controls

Smart manufacturing is bound to play a crucial role in reducing global energy consumption while accelerating economic development. This phenomenon is evident in advanced sensing technology developments, data analytics and machine learning/AI, automated controls, cloud computing, etc. The immediate opportunity for smart manufacturing is to improve the energy efficiency of manufacturing operations through innovations in processes and controls. The manufacturing industry still consumes about 30% of total global energy production, which is significant. This program focused on the recommendation of heterogeneous sensors to monitor the Chemical Vapor Infiltration (CVI) process parameters, states, and key performance indices and utilize cloud computing to sort and analyze the real-time data. Learning through data analytics helps guide the control parameters affecting energy usage. The target process in the program is the CVI process at the Honeywell South Bend facility, which is one of the most energy-intensive manufacturing industries.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Dashboard for Marine Energy Site Assessment and Monitoring

The marine energy (ME) industry presently relies upon fragmented site assessment solutions that require high resource expenditure for deployment at each site and do not leverage the wealth of readily available tools and information. A wave energy resource assessment dashboard, currently in development, will substantially improve siting, permitting, operations, and maintenance of ME projects by providing an integrated solution that is a one-stop-shop for a developer’s needs. The Site Energy Assessment and MOnitoring Dashboard (SEAMOD) will be of commercial interest to anyone seeking to deploy an ME project and is easily expandable to include tidal and wind energy site assessments. The integrated dashboard is being developed using state-of-the-art database and cloud computing methods and data-assimilative modeling tools that can be coupled with low-cost, rapidly deployable wave buoys and environmental sensing hardware. The combined software and hardware dashboard will reduce wave energy site characterization and wave climate monitoring costs by more than 60 percent and provide assessments that meet international industry standards. To realize a thriving global ME industry, the physical environment at a potential deployment site must be understood, not only for resource characterization, but also for optimization of device and power conversion performance. SEAMOD directly addresses these needs with a commercially marketable product. SEAMOD is a low-cost solution that provides comprehensive ME resource assessments, baseline environmental monitoring, and offshore characterizations required for successful ME development. The key technical objectives for Phase I were a series of software development goals, which when implemented with monitoring solutions, produced an initial proof-of-concept low-cost wave energy resources dashboard. In Phase II, the development of the prototype SEAMOD continued. The basic framework employed was the development of a revised dashboard and monitoring tool customized for ME applications by focusing on IEC site assessment and method requirements. Development was focused on the integration of full hindcast metocean products to provide hindcast resource characterization and environmental information. The final integrated dashboard provides a low-cost solution that delivers comprehensive, scalable, industry-standard energy resource assessments and offshore characterizations required for successful ME development. The integrated dashboard offers visibility of the most recent site modeling, measurements, and historical data. The application and integration of consensus-based standards for wave energy resource assessment, as determined by the International Electrotechnical Commission (IEC), are crucial for the impact and value of SEAMOD. SEAMOD includes monthly, seasonal, and yearly statistics, as well as the total 30-year record, offering temporal resolution of the IEC parameters to aid potential developers in determining the available wave energy resources in their area of interest.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Angle Assessment for Upper Limb Rehabilitation: A Novel Light Detection and Ranging (LiDAR)-Based Approach

The accurate measurement of joint angles during patient rehabilitation is crucial for informed decision making by physiotherapists. Presently, visual inspection stands as one of the prevalent methods for angle assessment. Although it could appear the most straightforward way to assess the angles, it presents a problem related to the high susceptibility to error in the angle estimation. In light of this, this study investigates the possibility of using a new approach to angle calculation: a hybrid approach leveraging both a camera and LiDAR technology, merging image data with point cloud information. This method employs AI-driven techniques to identify the individual and their joints, utilizing the cloud-point data for angle computation. The tests, considering different exercises with different perspectives and distances, showed a slight improvement compared to using YOLO v7 for angle calculation. However, the improvement comes with higher system costs when compared with other image-based approaches due to the necessity of equipment such as LiDAR and a loss of fluidity during the exercise performance. Therefore, the cost–benefit of the proposed approach could be questionable. Nonetheless, the results hint at a promising field for further exploration and the potential viability of using the proposed methodology.

Klein, Luan C. (ORCID:000000016306574X)↗
NASA NTRS2009

Astrochemical Modeling In the ALMA Era

Interstellar chemistry provides a natural laboratory for studying exotic species and processes at densities, temperatures, and reaction rates, that are difficult or impractical to address in the laboratory. Thus, many chemical reactions considered too slow by the standards of terrestrial chemistry, can be observed' and modeled. Various proposals concerning the nature and chemistry of complex interstellar organic molecules will be described. Catalytic reactions on grain surfaces can, in principle, lead to a large variety of species and this has motivated many laboratory and theoretical studies. Gas phase processes may also build large species in molecular clouds. Future laboratory data and computational tools needed to construct accurate chemical models of various sources to be observed by ALMA will be outlined.

Charnley, Steven↗
NASA NTRS2009

Theoretical Models of Astrochemical Processes

Interstellar chemistry provides a natural laboratory for studying exotic species and processes at densities, temperatures, and reaction rates. that are difficult or impractical to address in the laboratory. Thus, many chemical reactions considered too sloe by the standards of terrestrial chemistry, can be 'observed and modeled. Curious proposals concerning the nature and chemistry of complex interstellar organic molecules will be described. Catalytic reactions on "rain surfaces can, in principle, lead to a lame variety of species and this has motivated many laboratory and theoretical studies. Gas phase processes may also build lame species in molecular clouds. Future laboratory data and computational tools needed to construct accurate chemical models of various astronomical sources to be observed by Herschel and ALMA will be outlined.

Charnley, Steven↗
NASA NTRS2011

Superpixel-Augmented Endmember Detection for Hyperspectral Images

Superpixels are homogeneous image regions comprised of several contiguous pixels. They are produced by shattering the image into contiguous, homogeneous regions that each cover between 20 and 100 image pixels. The segmentation aims for a many-to-one mapping from superpixels to image features; each image feature could contain several superpixels, but each superpixel occupies no more than one image feature. This conservative segmentation is relatively easy to automate in a robust fashion. Superpixel processing is related to the more general idea of improving hyperspectral analysis through spatial constraints, which can recognize subtle features at or below the level of noise by exploiting the fact that their spectral signatures are found in neighboring pixels. Recent work has explored spatial constraints for endmember extraction, showing significant advantages over techniques that ignore pixels relative positions. Methods such as AMEE (automated morphological endmember extraction) express spatial influence using fixed isometric relationships a local square window or Euclidean distance in pixel coordinates. In other words, two pixels covariances are based on their spatial proximity, but are independent of their absolute location in the scene. These isometric spatial constraints are most appropriate when spectral variation is smooth and constant over the image. Superpixels are simple to implement, efficient to compute, and are empirically effective. They can be used as a preprocessing step with any desired endmember extraction technique. Superpixels also have a solid theoretical basis in the hyperspectral linear mixing model, making them a principled approach for improving endmember extraction. Unlike existing approaches, superpixels can accommodate non-isometric covariance between image pixels (characteristic of discrete image features separated by step discontinuities). These kinds of image features are common in natural scenes. Analysts can substitute superpixels for image pixels during endmember analysis that leverages the spatial contiguity of scene features to enhance subtle spectral features. Superpixels define populations of image pixels that are independent samples from each image feature, permitting robust estimation of spectral properties, and reducing measurement noise in proportion to the area of the superpixel. This permits improved endmember extraction, and enables automated search for novel and constituent minerals in very noisy, hyperspatial images. This innovation begins with a graph-based segmentation based on the work of Felzenszwalb et al., but then expands their approach to the hyperspectral image domain with a Euclidean distance metric. Then, the mean spectrum of each segment is computed, and the resulting data cloud is used as input into sequential maximum angle convex cone (SMACC) endmember extraction.

Thompson, David R.↗
NASA NTRS2012

Theoretical Models of Astrochemical Processes

Interstellar chemistry provides a natural laboratory for studying exotic species and processes at densities, temperatures, and reaction rates, that are difficult or impractical to address in the laboratory. Thus, many chemical reactions considered too slow by the standards of terrestrial chemistry, can be 'observed' and modeled. Various proposals concerning the nature and chemistry of complex interstellar organic molecules will be described. Catalytic reactions on grain surfaces can, in principle, lead to a large variety of species and this has motivated many laboratory and theoretical studies. Gas phase processes may also build large species in molecular clouds. Future laboratory data and computational tools needed to construct accurate chemical models of various astronomical sources to be observed by Herschel and ALMA will be outlined

Charnley, Steven B.↗
NASA NTRS2017

NASA's EOSDIS Cumulus: Ingesting, Archiving, Managing, and Distributing Earth Science Data from the Commercial Cloud

NASA's Earth Observing System Data and Information System (EOSDIS) has been working towards a vision of a cloud-based, highly-flexible, ingest, archive, management, and distribution system for its ever-growing and evolving data holdings. This system, Cumulus, is emerging from its prototyping stages and is poised to make a huge impact on how NASA manages and disseminates its Earth science data. This talk will outline the motivation for this work, present the achievements and hurdles of the past 18 months and will chart a course for the future expansion of the Cumulus expansion. We will explore on not just the technical, but also the socio-technical challenges that we face in evolving a system of this magnitude into the cloud and how we are rising to meet those challenges through open collaboration and intentional stakeholder engagement.

Earth Scienc↗
NASA NTRS2018

Accessing Data Stored in Amazon S3 Using the Hyrax OPeNDAP Server

For three years we have been investigating data storage and retrieval in the Amazon Cloud, striving to optimize both storage structures and (cache-enhanced retrieval processes. These optimizations are hidden from data users, who simply employ the Data Access Protocol (DAP), a popular, Web-based Application Programmer Interface (API) developed by OPeNDAP. We present our collected findings and their realization in the Hyrax data server. Specific techniques include caching data on spinning disk; accessing sharded data in place; optimizing the organizational structures for data within the flat key-value space of Simple Storage Service (S3); and employing an API extension that permits simultaneous operations on many datasets in a single request.

Data access↗
NASA NTRSDate not supplied

Pyroscopegridding: Geo-Leo Aerosol Data Fusion (an Open-Source Package)

The retrieval of aerosol optical depths (AODs) from sun-synchronous polar orbiting satellites, such as MODISs, VIIRSs, OMI, TROPOMI, etc., has been widely adopted as a method for obtaining information regarding particulate matter (PM) and related atmospheric processes. However, the advent of recently launched geostationary satellites, such as GOES-16/17/18, Himawari-8/9, and Meteosat Third Generation (MTG), has led to an increased temporal resolution of AOD observations (order of 10 minutes), resulting in typically one or more images per hour during daylight hours, compared to the once-per-day observations obtained from LEO satellites. By integrating these observations, the diurnal cycle of global AOD can be characterized at local, regional, and global scales. The scientific community is still examining the novel data from geostationary satellite observations and evaluating methods for effectively merging these observations with differing spatial and temporal resolutions. This presents a significant ""Big Data"" challenge, encompassing not only data storage, but also data discoverability, accessibility, and migration within cloud computing environments. This study presents our attempts at fusing Level 2 aerosol data from six satellites, three of which are geostationary (GOES-16/17 and Himawari-8) and three of which are polar orbiting (TERRA/MODIS, AQUA/MODIS, and SNPP-VIIRS), using the Dark Target aerosol retrieval algorithm. The ability to fuse remote sensing products on demand into desired temporal and spatial domains empowers researchers and practitioners to more efficiently work with satellite and sensor data. It is our hope that through making our open-source package and accompanying functionality available, the scientific community will have improved access to aerosol data processing resources.

Jennifer Wei↗
NASA NTRSDate not supplied

Efficient Access to MERRA-2 Reanalysis Data from the Cloud Archives

The Modern-Era Retrospective analysis for Research and Applications, Version (MERRA-2) dataset is now available in the AWS West Region-2 cloud. The free data downloading policy remains unchanged. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) offers cloud services such as OPeNDP subsetting, and time series services. This presentation will showcase use cases that testing the procedures and find the most efficiency workflows to access and process the data for finding climate extremes. The use cases include a) accessing and processing data directly in the cloud S3 bucket without downloading it, and b) downloading subset data to process on the user’s local system.

data access, cloud computing, climate extremes↗
DOE OSTI2014

Cloud Based Applications and Platforms (Presentation)

Presentation to the Cloud Computing East 2014 Conference, where we are highlighting our cloud computing strategy, describing the platforms on the cloud (including Smartgrid.gov), and defining our process for implementing cloud based applications.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Surface Radiation Budget

The Surface Radiation Budget (SRB) data sets contain global 3-hourly, daily and monthly averages of surface longwave and shortwave radiative properties, cloud amount, and meteorological properties computed using models. The main input data for these models include cloud information, top-of-atmosphere radiances and profiles of atmospheric water vapor and temperature. Some of the input data include Earth Radiation Budget Energy (ERBE) top-of-atmosphere clear-sky albedo and International Satellite Cloud Climatology Project (ISCCP) radiances and cloud amount. SRB parameters derived for the renewable energy community are also available from the Surface meteorology and Solar Energy (SSE) data set. Other SRB data are available from Clouds and the Earth's Radiant Energy System (CERES) and Multi-angle Imaging SpectroRadiometer (MISR). [Mission Objectives] The objective of the SRB Project is to produce and archive a global data set of shortwave (SW) and longwave (LW) surface and top of the atmosphere parameters. The data generated in the SRB project may be used in conjunction with other data sets to facilitate the development of renewable energy resources and increase understanding of radiative properties within the meteorological community. [Temporal_Coverage: Start_Date=1983-07-01; Stop_Date=2005-06-30] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180].

Stackhouse, Paul W.↗
DOE OSTI2023

Measurement and applications: Exploring the challenges and opportunities of hierarchical federated learning in sensor applications

Sensor applications have become ubiquitous in modern society as the digital age continues to advance. AI-based techniques (e.g., machine learning) are effective at extracting actionable information from large amounts of data. An example would be an automated water irrigation system that uses AI-based techniques on soil quality data to decide how to best distribute water. However, these AI-based techniques are costly in terms of hardware resources, and Internet-of-Things (IoT) sensors are resource-constrained with respect to processing power, energy, and storage capacity. These limitations can compromise the security, performance, and reliability of sensor-driven applications. To address these concerns, cloud computing services can be used by sensor applications for data storage and processing. Unfortunately, cloud-based sensor applications that require real-time processing, such as medical applications (e.g., fall detection and stroke prediction), are vulnerable to issues such as network latency due to the sparse and unreliable networks between the sensor nodes and the cloud server [1]. As users approach the edge of the communications network, latency issues become more severe and frequent. A promising alternative is edge computing, which provides cloud-like capabilities at the edge of the network by pushing storage and processing capabilities from centralized nodes to edge devices that are closer to where the data are gathered, resulting in reduced network delays [2], [3].

Po-Leen Ooi, Melanie↗