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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

A New User Interface for On-Demand Customizable Data Products for Sensors in a SensorWeb

A SensorWeb is a set of sensors, which can consist of ground, airborne and space-based sensors interoperating in an automated or autonomous collaborative manner. The NASA SensorWeb toolbox, developed at NASA/GSFC in collaboration with NASA/JPL, NASA/Ames and other partners, is a set of software and standards that (1) enables users to create virtual private networks of sensors over open networks; (2) provides the capability to orchestrate their actions; (3) provides the capability to customize the output data products and (4) enables automated delivery of the data products to the users desktop. A recent addition to the SensorWeb Toolbox is a new user interface, together with web services co-resident with the sensors, to enable rapid creation, loading and execution of new algorithms for processing sensor data. The web service along with the user interface follows the Open Geospatial Consortium (OGC) standard called Web Coverage Processing Service (WCPS). This presentation will detail the prototype that was built and how the WCPS was tested against a HyspIRI flight testbed and an elastic computation cloud on the ground with EO-1 data. HyspIRI is a future NASA decadal mission. The elastic computation cloud stores EO-1 data and runs software similar to Amazon online shopping.

Mandl, Daniel↗
NASA NTRS2015

Climate Model Diagnostic Analyzer

The comprehensive and innovative evaluation of climate models with newly available global observations is critically needed for the improvement of climate model current-state representation and future-state predictability. A climate model diagnostic evaluation process requires physics-based multi-variable analyses that typically involve large-volume and heterogeneous datasets, making them both computation- and data-intensive. With an exploratory nature of climate data analyses and an explosive growth of datasets and service tools, scientists are struggling to keep track of their datasets, tools, and execution/study history, let alone sharing them with others. In response, we have developed a cloud-enabled, provenance-supported, web-service system called Climate Model Diagnostic Analyzer (CMDA). CMDA enables the physics-based, multivariable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. At the same time, CMDA provides a crowd-sourcing space where scientists can organize their work efficiently and share their work with others. CMDA is empowered by many current state-of-the-art software packages in web service, provenance, and semantic search.

cloud computing↗
NASA NTRS2014

Proto-Examples of Data Access and Visualization Components of a Potential Cloud-Based GEOSS-AI System

Once a research or application problem has been identified, one logical next step is to search for available relevant data products. Thus, an early component of a potential GEOSS-AI system, in the continuum between observations and end point research, applications, and decision making, would be one that enables transparent data discovery and access by users. Such a component might be effected via the systems data agents. Presumably, some kind of data cataloging has already been implemented, e.g., in the GEOSS Common Infrastructure (GCI). Both the agents and cataloging could also leverage existing resources external to the system. The system would have some means to accept and integrate user-contributed agents. The need or desirability for some data format internal to the system should be evaluated. Another early component would be one that facilitates browsing visualization of the data, as well as some basic analyses.Three ongoing projects at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) provide possible proto-examples of potential data access and visualization components of a cloud-based GEOSS-AI system. 1. Reorganizing data archived as time-step arrays to point-time series (data rods), as well as leveraging the NASA Simple Subset Wizard (SSW), to significantly increase the number of data products available, at multiple NASA data centers, for production as on-the-fly (virtual) data rods. SSWs data discovery is based on OpenSearch. Both pre-generated and virtual data rods are accessible via Web services. 2. Developing Web Feature Services to publish the metadata, and expose the locations, of pre-generated and virtual data rods in the GEOSS Portal and enable direct access of the data via Web services. SSW is also leveraged to increase the availability of both NASA and non-NASA data.3.Federating NASA Giovanni (Geospatial Interactive Online Visualization and Analysis Interface), for multi-sensor data exploration, that would allow each cooperating data center, currently the NASA Distributed Active Archive Centers (DAACs), to configure its own Giovanni deployment, while also allowing all the deployments to incorporate each others data. A federated Giovanni comprises Giovanni Virtual Machines, which can be run on local servers or in the cloud.

access↗
DOE OSTI2022

Development of an end state vision to implement digital monitoring in nuclear plants

Transitioning from an onsite Maintenance & Diagnostics Center to cloud-based services offers many new opportunities with computing power and storage, but also new challenges in terms of networking and security. This report will cover everything required for that transition including data processing and uploading to cloud services, feature selection, model creation, and result visualization for decision making. Although there are several other cloud-based services (e.g. Amazon Web Services and Google Cloud), this report explores Microsoft Azure to simplify nomenclature and maintain a consistent focus. Many of the services offered by Microsoft Azure are also available in the other cloud-based services, and their differences have been recorded in other literature. The Azure services most important to a nuclear power plant including networking & security, storage & databases, and Artificial Intelligence (AI) are reviewed here. Networking covers all aspects related to communication to Azure resources including security, privacy, and redundancy. Storage & databases includes data storage, upgrading, patching, backups, and monitoring. The AI services allows the user access to the machine learning (ML) techniques developed with Azure including automated ML, anomaly detection, computer vision, and natural language processing. This report summaries the features, capabilities, and challenges when using cloud-based services in a user-friendly manner.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRSDate not supplied

Changes in Characteristics of Future Climate Across the U.S.: Time Series Analysis of Climate Model Data by NASA POWER

NASA’s Prediction of Worldwide Energy Resource (POWER) project facilitates the use of NASA Earth Science data holdings within the energy, agricultural, and building heating/cooling design industries. POWER packages solar and meteorological data at various temporal levels from several NASA projects in a user friendly GIS-enabled web services system (https://power.larc.nasa.gov). Data users can access these data either through an intuitive data viewer, image services fully integrable with GIS analysis, connections in the cloud through an Amazon Web Services S3 Bucket, or fully customizable access through an API. Data provided by POWER has been used to remotely monitor solar array fields and integrated in a sizing tool for off-grid solar and storage systems. POWER data has also been coupled with key building decision tools to support design and retrofitting of building energy systems for energy efficiency and reduction of greenhouse gases. POWER is now developing capabilities to provide time series of the projected future evolution of surface quantities important to future energy production and use, such as heating/cooling degree days, temperature, wind speed, and downwelling solar flux. We present here a range of possible future changes in these quantities at locations throughout the continental United States. We show how both average and extreme values of the quantities will evolve from present-day to future climate conditions. We plan to provide these projections for users in the energy and sustainable energy communities.

Bradley M. Hegyi↗
NASA NTRS2004

Grid Technology as a Cyberinfrastructure for Delivering High-End Services to the Earth and Space Science Community

Grid technology consists of middleware that permits distributed computations, data and sensors to be seamlessly integrated into a secure, single-sign-on processing environment. In &is environment, a user has to identify and authenticate himself once to the grid middleware, and then can utilize any of the distributed resources to which he has been,panted access. Grid technology allows resources that exist in enterprises that are under different administrative control to be securely integrated into a single processing environment The grid community has adopted commercial web services technology as a means for implementing persistent, re-usable grid services that sit on top of the basic distributed processing environment that grids provide. These grid services can then form building blocks for even more complex grid services. Each grid service is characterized using the Web Service Description Language, which provides a description of the interface and how other applications can access it. The emerging Semantic grid work seeks to associates sufficient semantic information with each grid service such that applications wii1 he able to automatically select, compose and if necessary substitute available equivalent services in order to assemble collections of services that are most appropriate for a particular application. Grid technology has been used to provide limited support to various Earth and space science applications. Looking to the future, this emerging grid service technology can provide a cyberinfrastructures for both the Earth and space science communities. Groups within these communities could transform those applications that have community-wide applicability into persistent grid services that are made widely available to their respective communities. In concert with grid-enabled data archives, users could easily create complex workflows that extract desired data from one or more archives and process it though an appropriate set of widely distributed grid services discovered using semantic grid technology. As required, high-end computational resources could be drawn from available grid resource pools. Using grid technology, this confluence of data, services and computational resources could easily be harnessed to transform data from many different sources into a desired product that is delivered to a user's workstation or to a web portal though which it could be accessed by its intended audience.

Hinke, Thomas H.↗
NASA NTRS2017

NASA Enterprise Managed Cloud Computing (EMCC): Delivering an Initial Operating Capability (IOC) for NASA use of Commercial Infrastructure-as-a-Service (IaaS)

In 2016, Ames supported the NASA CIO in delivering an initial operating capability for Agency use of commercial cloud computing. This presentation provides an overview of the project, the services approach followed, and the major components of the capability that was delivered. The presentation is being given at the request of Amazon Web Services to a contingent representing the Brazilian Federal Government and Defense Organization that is interested in the use of Amazon Web Services (AWS). NASA is currently a customer of AWS and delivered the Initial Operating Capability using AWS as its first commercial cloud provider. The IOC, however, designed to also support other cloud providers in the future.

IT Governance for Cloud Computing↗
NASA NTRS2011

Using Open and Interoperable Ways to Publish and Access LANCE AIRS Near-Real Time Data

The Atmospheric Infrared Sounder (AIRS) Near-Real Time (NRT) data from the Land Atmosphere Near real-time Capability for EOS (LANCE) element at the Goddard Earth Sciences Data and Information Services Center (GES DISC) provides information on the global and regional atmospheric state, with very low temporal latency, to support climate research and improve weather forecasting. An open and interoperable platform is useful to facilitate access to, and integration of, LANCE AIRS NRT data. As Web services technology has matured in recent years, a new scalable Service-Oriented Architecture (SOA) is emerging as the basic platform for distributed computing and large networks of interoperable applications. Following the provide-register-discover-consume SOA paradigm, this presentation discusses how to use open-source geospatial software components to build Web services for publishing and accessing AIRS NRT data, explore the metadata relevant to registering and discovering data and services in the catalogue systems, and implement a Web portal to facilitate users' consumption of the data and services.

Zhao, Peisheng↗
NASA NTRSDate not supplied

NASA POWER: Providing Present and Future Climate Services Based on NASA Data for the Energy, Agricultural, and Sustainable Buildings Communities

NASA’s Prediction of Worldwide Energy Resource (POWER) project facilitates the use of NASA Earth Science data holdings within the renewable energy, agricultural, and building heating/cooling design industries. POWER packages solar and meteorological data at various temporal levels from several NASA projects in a user friendly GIS-enabled web services system (https://power.larc.nasa.gov). Data users can access these data either through an intuitive data viewer, image services fully integrable with GIS analysis, connections in the cloud through an Amazon Web Services S3 Bucket, or fully customizable access through an API. Data provided by POWER has been successfully used by decision makers to support actions that address climate change. For example, POWER data has been used to remotely monitor solar array fields and integrated in a sizing tool for off-grid solar and storage systems. POWER data has also been coupled with key building decision tools to support design and retrofitting of building energy systems for energy efficiency and reduction of greenhouse gases. POWER is now developing climate services to provide time series of the projected future evolution of key quantities that interest our users, such as heating/cooling degree days, temperature, wind speed, and downwelling solar flux. We demonstrate the potential of the new climate services by presenting here a range of possible future changes in these quantities at different NASA centers across the continental United States. These data services are based on downscaled climate model data from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) data set. We highlight the important insights that new climate services can provide. Our climate services will help our user communities quantify the impacts of climate change to support their key decisions in planning for the future, both inside and outside the Federal Government, especially for decisions in renewable energy and in building heating and cooling.

Bradley Hegyi↗
NASA NTRSDate not supplied

DAAC Collaboration Overview for AGU

The Atmospheric Science Data Center (ASDC), Goddard Earth Sciences Data and Information Systems Center (GES DISC), Socioeconomic Data and Applications Center (SEDAC), Oak Ridge National Laboratory (ORNL), Land Processes DAAC, Alaska Satellite Facility (ASF), as well as NASA Global Imagery Browse Service (GIBS), Earth Science Data Systems (ESDS) Geographic Information Systems Team (EGIST), Earthdata Content Delivery Team, ArcGIS Online Governance Team, and the Systematic Data Transformation ACCESS team have come together to establish the ArcGIS DAAC Collaboration. This coalition of participants will demonstrate their use of the ArcGIS Enterprise to support Earth science research, applied science, and outreach using Earth Observing System Data and Information System (EOSDIS) data. This includes the use of web services to fuse data products across space and time through services (e.g. ArcGIS Image Services, Open Geospatial Consortium (OGC) Web Coverage Service (WCS), and OGC Web Mapping Services (WMS)) for analysis in Jupyter notebooks, desktop tools, and web based applications.

Matthew Steven Tisdale↗
NASA NTRS2017

Trade Study: Storing NASA HDF5/netCDF-4 Data in the Amazon Cloud and Retrieving Data Via Hyrax Server Data Server

This study explored three candidate architectures with different types of objects and access paths for serving NASA Earth Science HDF5 data via Hyrax running on Amazon Web Services (AWS). We studied the cost and performance for each architecture using several representative Use-Cases. The objectives of the study were: Conduct a trade study to identify one or more high performance integrated solutions for storing and retrieving NASA HDF5 and netCDF4 data in a cloud (web object store) environment. The target environment is Amazon Web Services (AWS) Simple Storage Service (S3). Conduct needed level of software development to properly evaluate solutions in the trade study and to obtain required benchmarking metrics for input into government decision of potential follow-on prototyping. Develop a cloud cost model for the preferred data storage solution (or solutions) that accounts for different granulation and aggregation schemes as well as cost and performance trades.We will describe the three architectures and the use cases along with performance results and recommendations for further work.

AWS cost↗
NASA NTRS2011

Addressing and Presenting Quality of Satellite Data via Web-Based Services

With the recent attention to climate change and proliferation of remote-sensing data utilization, climate model and various environmental monitoring and protection applications have begun to increasingly rely on satellite measurements. Research application users seek good quality satellite data, with uncertainties and biases provided for each data point. However, different communities address remote sensing quality issues rather inconsistently and differently. We describe our attempt to systematically characterize, capture, and provision quality and uncertainty information as it applies to the NASA MODIS Aerosol Optical Depth data product. In particular, we note the semantic differences in quality/bias/uncertainty at the pixel, granule, product, and record levels. We outline various factors contributing to uncertainty or error budget; errors. Web-based science analysis and processing tools allow users to access, analyze, and generate visualizations of data while alleviating users from having directly managing complex data processing operations. These tools provide value by streamlining the data analysis process, but usually shield users from details of the data processing steps, algorithm assumptions, caveats, etc. Correct interpretation of the final analysis requires user understanding of how data has been generated and processed and what potential biases, anomalies, or errors may have been introduced. By providing services that leverage data lineage provenance and domain-expertise, expert systems can be built to aid the user in understanding data sources, processing, and the suitability for use of products generated by the tools. We describe our experiences developing a semantic, provenance-aware, expert-knowledge advisory system applied to NASA Giovanni web-based Earth science data analysis tool as part of the ESTO AIST-funded Multi-sensor Data Synergy Advisor project.

Leptoukh, Gregory↗
NASA NTRS2015

Finding Atmospheric Composition (AC) Metadata

The Atmospheric Composition Portal (ACP) is an aggregator and curator of information related to remotely sensed atmospheric composition data and analysis. It uses existing tools and technologies and, where needed, enhances those capabilities to provide interoperable access, tools, and contextual guidance for scientists and value-adding organizations using remotely sensed atmospheric composition data. The initial focus is on Essential Climate Variables identified by the Global Climate Observing System CH4, CO, CO2, NO2, O3, SO2 and aerosols. This poster addresses our efforts in building the ACP Data Table, an interface to help discover and understand remotely sensed data that are related to atmospheric composition science and applications. We harvested GCMD, CWIC, GEOSS metadata catalogs using machine to machine technologies - OpenSearch, Web Services. We also manually investigated the plethora of CEOS data providers portals and other catalogs where that data might be aggregated. This poster is our experience of the excellence, variety, and challenges we encountered.Conclusions:1.The significant benefits that the major catalogs provide are their machine to machine tools like OpenSearch and Web Services rather than any GUI usability improvements due to the large amount of data in their catalog.2.There is a trend at the large catalogs towards simulating small data provider portals through advanced services. 3.Populating metadata catalogs using ISO19115 is too complex for users to do in a consistent way, difficult to parse visually or with XML libraries, and too complex for Java XML binders like CASTOR.4.The ability to search for Ids first and then for data (GCMD and ECHO) is better for machine to machine operations rather than the timeouts experienced when returning the entire metadata entry at once. 5.Metadata harvest and export activities between the major catalogs has led to a significant amount of duplication. (This is currently being addressed) 6.Most (if not all) Earth science atmospheric composition data providers store a reference to their data at GCMD.

metadata search↗
NASA NTRS2017

Task 28: Web Accessible APIs in the Cloud Trade Study

This study explored three candidate architectures for serving NASA Earth Science Hierarchical Data Format Version 5 (HDF5) data via Hyrax running on Amazon Web Services (AWS). We studied the cost and performance for each architecture using several representative Use-Cases. The objectives of the project are: Conduct a trade study to identify one or more high performance integrated solutions for storing and retrieving NASA HDF5 and Network Common Data Format Version 4 (netCDF4) data in a cloud (web object store) environment. The target environment is Amazon Web Services (AWS) Simple Storage Service (S3).Conduct needed level of software development to properly evaluate solutions in the trade study and to obtain required benchmarking metrics for input into government decision of potential follow-on prototyping. Develop a cloud cost model for the preferred data storage solution (or solutions) that accounts for different granulation and aggregation schemes as well as cost and performance trades.

cost model↗
NASA NTRS2010

Observing System Simulation Experiment (OSSE) for the HyspIRI Spectrometer Mission

The OSSE software provides an integrated end-to-end environment to simulate an Earth observing system by iteratively running a distributed modeling workflow based on the HyspIRI Mission, including atmospheric radiative transfer, surface albedo effects, detection, and retrieval for agile exploration of the mission design space. The software enables an Observing System Simulation Experiment (OSSE) and can be used for design trade space exploration of science return for proposed instruments by modeling the whole ground truth, sensing, and retrieval chain and to assess retrieval accuracy for a particular instrument and algorithm design. The OSSE in fra struc ture is extensible to future National Research Council (NRC) Decadal Survey concept missions where integrated modeling can improve the fidelity of coupled science and engineering analyses for systematic analysis and science return studies. This software has a distributed architecture that gives it a distinct advantage over other similar efforts. The workflow modeling components are typically legacy computer programs implemented in a variety of programming languages, including MATLAB, Excel, and FORTRAN. Integration of these diverse components is difficult and time-consuming. In order to hide this complexity, each modeling component is wrapped as a Web Service, and each component is able to pass analysis parameterizations, such as reflectance or radiance spectra, on to the next component downstream in the service workflow chain. In this way, the interface to each modeling component becomes uniform and the entire end-to-end workflow can be run using any existing or custom workflow processing engine. The architecture lets users extend workflows as new modeling components become available, chain together the components using any existing or custom workflow processing engine, and distribute them across any Internet-accessible Web Service endpoints. The workflow components can be hosted on any Internet-accessible machine. This has the advantages that the computations can be distributed to make best use of the available computing resources, and each workflow component can be hosted and maintained by their respective domain experts.

Turmon, Michael J.↗
DOE OSTI2024

Accelerating science: The usage of commercial clouds in ATLAS Distributed Computing

The ATLAS experiment at CERN is one of the largest scientific machines built to date and will have ever growing computing needs as the Large Hadron Collider collects an increasingly larger volume of data over the next 20 years. ATLAS is conducting R&D projects on Amazon Web Services and Google Cloud as complementary resources for distributed computing, focusing on some of the key features of commercial clouds: lightweight operation, elasticity and availability of multiple chip architectures. The proof of concept phases have concluded with the cloud-native, vendoragnostic integration with the experiment’s data and workload management frameworks. Google Cloud has been used to evaluate elastic batch computing, ramping up ephemeral clusters of up to O(100k) cores to process tasks requiring quick turnaround. Amazon Web Services has been exploited for the successful physics validation of the Athena simulation software on ARM processors. We have also set up an interactive facility for physics analysis allowing endusers to spin up private, on-demand clusters for parallel computing with up to 4 000 cores, or run GPU enabled notebooks and jobs for machine learning applications. The success of the proof of concept phases has led to the extension of the Google Cloud project, where ATLAS will study the total cost of ownership of a production cloud site during 15 months with 10k cores on average, fully integrated with distributed grid computing resources and continue the R&D projects.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2011

From Science to e-Science to Semantic e-Science: A Heliosphysics Case Study

The past few years have witnessed unparalleled efforts to make scientific data web accessible. The Semantic Web has proven invaluable in this effort; however, much of the literature is devoted to system design, ontology creation, and trials and tribulations of current technologies. In order to fully develop the nascent field of Semantic e-Science we must also evaluate systems in real-world settings. We describe a case study within the field of Heliophysics and provide a comparison of the evolutionary stages of data discovery, from manual to semantically enable. We describe the socio-technical implications of moving toward automated and intelligent data discovery. In doing so, we highlight how this process enhances what is currently being done manually in various scientific disciplines. Our case study illustrates that Semantic e-Science is more than just semantic search. The integration of search with web services, relational databases, and other cyberinfrastructure is a central tenet of our case study and one that we believe has applicability as a generalized research area within Semantic e-Science. This case study illustrates a specific example of the benefits, and limitations, of semantically replicating data discovery. We show examples of significant reductions in time and effort enable by Semantic e-Science; yet, we argue that a "complete" solution requires integrating semantic search with other research areas such as data provenance and web services.

Narock, Thomas↗
NASA NTRS2017

Educational and Scientific Applications of Climate Model Diagnostic Analyzer

Climate Model Diagnostic Analyzer (CMDA) is a web-based information system designed for the climate modeling and model analysis community to analyze climate data from models and observations. CMDA provides tools to diagnostically analyze climate data for model validation and improvement, and to systematically manage analysis provenance for sharing results with other investigators. CMDA utilizes cloud computing resources, multi-threading computing, machine-learning algorithms, web service technologies, and provenance-supporting technologies to address technical challenges that the Earth science modeling and model analysis community faces in evaluating and diagnosing climate models. As CMDA technology and infrastructure have matured, we have developed the educational and scientific applications of CMDA. Educationally, CMDA supported the summer school of the JPL Center for Climate Sciences in 2014, 2015, and 2016. In the summer school, the students work on group research projects where CMDA provide datasets, analysis tools, and provenance support utility tools. Each student is assigned to a virtual machine with CMDA installed in Amazon Web Services. Scientifically, we have developed several science use cases of CMDA covering various topics, datasets, and analysis types. Each of the science use cases is described in terms of a scientific goal, datasets used, the analysis tools used, scientific results discovered, an analysis result such as output plots and data files, and a link to the corresponding analysis service call with all the input arguments filled.

Bao, Qihao↗