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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

CO 2 Storage Site Screening Platform Development and CO 2 Storage Resource Analysis in SECARB Offshore Reservoirs Using SAS Viya

A major goal of the SECARB Offshore Partnership (DE-FE0031557) is to screen deep saline aquifers and hydrocarbon reservoirs in the central Gulf of Mexico for CO 2 sequestration and CO 2 -enhanced oil and gas recovery (EOR/EGR) and estimate the corresponding CO 2 storage resources for select reservoirs. CO 2 storage potential associated with offshore CO 2 -EOR is considerable and likely represents “low hanging fruit” for near-term CO 2 storage given the in-place infrastructure in the region. It is for these reasons that this assessment focuses on oil and gas fields. To this end, three major objectives have been completed and include (1) managing geological data derived from different sources, (2) building a reservoir screening platform for CO 2 storage, and (3) ranking the reservoirs based on the estimated CO 2 storage resources. The SAS ® Viya platform was used for data management and analytics. The Viya platform is a cloud service platform that provides data integration, data management, quick analytics, data visualization, machine learning functions, and application programming interfaces (APIs) for multi-programming languages. Different sources of data containing geologic information, reservoir properties, and EOR/EGR information were collected, cleaned, formatted, and loaded into the SAS ® Viya platform for evaluation. The major geological characteristics of both shelf and deep-water areas of the central Gulf were examined and compared to define the appropriate reservoir screening criteria. Next, a CO 2 storage site screening system was built in the SAS ® Viya platform with the pre-defined criteria. Finally, the CO 2 storage resources of the screened reservoirs were calculated and reported at the BOEM field level to identify fields with the highest estimated CO 2 storage resource. The fields with the largest total estimated CO 2 storage resource are located in the Mississippi Canyon protraction area. Due to proximity to the Mississippi Delta (indicative of less infrastructure) and large estimated CO 2 storage resources, future development activities may wish to focus efforts in the Mississippi Canyon protraction area.

02 PETROLEUM↗
DOE OSTI2017

Standard Energy Efficiency Data Platform® [SWR-17-25]

The Standard Energy Efficiency Data Platform® (SEED Platform) provides public agencies and other organizations with a standardized but flexible, cost-effective, secure, enterprise data platform to manage portfolio scale building performance data from a variety of sources. The SEED Platform streamlines the administrative effort required by public agencies implementing building performance and transparency programs.

Source record↗
DOE OSTI2019

Grid Data Crossing Platform (GriD-Xing)

This project is to develop a data management and analytics platform called Grid Data Crossing (GriD-Xing) that integrates data processing, storage, analysis, and visualization functionalities for users at industry (e.g., EMS and DMS) and universities. It intends to increase the applicability of large set of multi-source data (e.g., SCADA, PMU, μPMU, AMI, inverter, load/DER forecasting, and cyber data) and eventually improve the resilience, reliability, and security of critical energy infrastructure. GriD-Xing also intends to be a research platform to identify the usefulness of large multi-source data via case studies of interest to industry (e.g., 2011 southwest cold weather and 2016 Blue Cut fire events). In the first year, the team focused on the development and demonstration of large multi-source data (e.g., PMU, μPMU, SCADA, AMI, and cyber data) processing, storage, and analysis. Specifically, the team designed GriD-Xing framework in functional and structural aspects, developed GriD-Xing algorithms for large multi-source grid data processing, storage, and analysis, and finally demonstrated GriD-Xing prototype algorithms performance through a series of tests and case studies using real-world and simulation data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Xi-cam : a versatile interface for data visualization and analysis

Xi-cam is an extensible platform for data management, analysis and visualization. Xi-cam aims to provide a flexible and extensible approach to synchrotron data treatment as a solution to rising demands for high-volume/high-throughput processing pipelines. The core of Xi-cam is an extensible plugin-based graphical user interface platform which provides users with an interactive interface to processing algorithms. Plugins are available for SAXS/WAXS/GISAXS/GIWAXS, tomography and NEXAFS data. With Xi-cam 's `advanced' mode, data processing steps are designed as a graph-based workflow, which can be executed live, locally or remotely. Remote execution utilizes high-performance computing or de-localized resources, allowing for the effective reduction of high-throughput data. Xi-cam 's plugin-based architecture targets cross-facility and cross-technique collaborative development, in support of multi-modal analysis. Xi-cam is open-source and cross-platform, and available for download on GitHub.

Pandolfi, Ronald J. (ORCID:0000000308248548)↗
DOE OSTI2023

Unified Software Architecture for Advanced Materials and Manufacturing Technologies Data Management and Processing: FY 2023 Multidimensional Data Correlation Platform

This report details the various digital manufacturing activities ongoing at ORNL as part of the Advanced Materials and Manufacturing Technologies (AMMT) program. The AMMT program is exploring a data-driven approach to demonstrate the use of AM for the fabrication of components for nuclear applications, with the goal of providing a greater understanding of manufacturing quality outcomes that would pave the way toward the development of standards for certification and qualification. The objective of this work package is to establish a digital manufacturing discipline common to all participants of the AMMT program to improve the performance, reliability, and lifetime of nuclear components. As part of this effort, we will develop a unified software architecture for AMMT data management and processing, deploy the digital platform across AMMT participants’ facilities, and generate pedigreed datasets in a common format across multiple labs and facilities. To this end, the MDDC work package has focused on three activities during FY23. First, the MDF Digital Tool was overhauled to better serve the needs of the AMMT program. Next, multiple laser powder bed fusion (L-PBF) systems at the MDF were upgraded to a common sensor package for collecting comparable in situ data across machines. Finally, various improvements relevant to the AMMT program were implemented in the ORNL-developed software tool, Peregrine. This report marks the completion of FY23 milestone M3CR-22OR0403051: Report Describing the Architecture of the Digital Platform to Support AMMT Activities.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Big-data Efficient and Automated Science Transfer (BEAST): An Open-Source Software Architecture for Arc Jet Data Management, Modeling, and Automation

Big-data Efficient and Automated Science Transfer (BEAST) is a facility data management application developed for the NASA Ames arc jet facilities. The current decentralized data management practices limit statistical tracking, synchronization between video/time series, search capability, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management↗
DOE OSTI2019

Reimagining the potential of Earth observations for ecosystem service assessments

The benefits nature provides to people, called ecosystem services, are increasingly recognized and accounted for in assessments of infrastructure development, agricultural management, conservation prioritization, and sustainable sourcing. These assessments are often limited by data, however, a gap with tremendous potential to be filled through Earth observations (EO), which produce a variety of data across spatial and temporal extents and resolutions. Despite widespread recognition of this potential, in practice few ecosystem service studies use EO. Here, we identify challenges and opportunities to using EO in ecosystem service modeling and assessment. Some challenges are technical, related to data awareness, processing, and access. These challenges require systematic investment in model platforms and data management. Other challenges are more conceptual but still systemic; they are byproducts of the structure of existing ecosystem service models and addressing them requires scientific investment in solutions and tools applicable to a wide range of models and approaches. We also highlight new ways in which EO can be leveraged for ecosystem service assessments, identifying promising new areas of research. More widespread use of EO for ecosystem service assessment will only be achieved if all of these types of challenges are addressed. Furthermore, this will require non-traditional funding and partnering opportunities from private and public agencies to promote data exploration, sharing, and archiving. Investing in this integration will be reflected in better and more accurate ecosystem service assessments worldwide.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Big-data Efficient and Automated Science Transfer (BEAST): An Open-Source Software Architecture for Arc Jet Data Management, Modeling, and Automation

Big-data Efficient and Automated Science Transfer (BEAST) was conceived to address the existing ground testing data management of the NASA Ames arc jet facilities (e.g., manually entered Excel files and USB drive data transfers). These data management practices were seen as a choke point for future thermal protection system (TPS) development as they limit statistical tracking, resolution of diagnostics, coordination between video/time series, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management↗
NASA NTRSDate not supplied

Big-data Efficient Automated Science Transfer (BEAST): an open-source software architecture for arc jet data management, modeling, and automation

Big-data Efficient and Automated Science Transfer (BEAST) was conceived to address the existing ground testing data management of the NASA Ames arc jet facilities (e.g., manually entered Excel files and USB drive data transfers). These data management practices were seen as a choke point for future thermal protection system (TPS) development as they limit statistical tracking, resolution of diagnostics, coordination between video/time series, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management↗
DOE OSTI2023

A National Infrastructure for Artificial Intelligence on the Grid (NI4AI) (Final Scientific/Technical Report)

Electric utilities have traditionally taken a very pragmatic yet myopic approach with grid sensors and the resulting collected data. Sensors are purchased and deployed to solve a specific, known problem that has risen to sufficient awareness as to justify the effort of deploying sensors and the needed capital investment. This sensor data flows into proprietary software packages with limited functionality intended only to address the initial problem. This approach aligns with the financial incentives of the utility to deploy capital into fixed hardware assets for which the corporations earn a rate of return. This mentality stands in stark contrast to the big data revolution that started nearly 25 years ago with the rise of Google. In this worldview, data is a fundamental business asset; successful organizations collect, store, explore, merge, and exploit as much data as possible to not only solve problems well understood today but also to tackle new problems that will inevitably rise tomorrow. The ARPA-E Open Innovation 2018 project entitled A National Infrastructure for Artificial Intelligence on the Grid or NI4AI for short was designed to demonstrate this alternative paradigm for using data. To do this, the project was composed of three key thrust areas. The first major component deployed a variety of high-frequency grid sensors and captured terabytes of both wide-scale and localized grid measurements, generating high-value datasets for grid research and algorithm development. The second aspect made available PingThings’ PredictiveGridTM, a horizontally scalable, cloud-based data management and AI platform built for time series data to explore and exploit the collected data. Finally, the project fostered a diverse and open research community composed of experts from numerous fields through focused educational content, code sharing, and data science competitions. Shifting away from “single use” sensors and closed data silos within electric utilities is a major benefit to the public at large. This legacy approach to data is incredibly (1) capital intensive (new sensors must be deployed for each new problem and problems tend to arise continuously) and (2) painfully slow (new problems must be identified first and then new sensors must be deployed to collect data to begin to address the issue). The transition to a carbon neutral grid requires a massive transformation of the existing grid infrastructure and will continue to challenge the legacy grid in unforeseen ways. The only way to make the energy transition cost effective is for utilities to abandon this dated data paradigm and adopt more contemporary approaches. NI4AI has shown that it is technically possible and economically feasible to ingest, explore, and exploit grid data collected from even very high frequency sensing, such as continuous point on wave sensors collecting measurements 10,000 times a second. In fact, the PredictiveGrid platform used is commercially available and deployed at several utilities in the United States. Project accomplishments were numerous and included (1) making available a state of the art time series platform to the community, (2) collecting over 520 streams of time series data from grid sensors totaling over 1 trillion grid measurements, and (3) developing and nurturing a community within the industry focused on the use of data to create value for utilities and, ultimately, end consumers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

National Renewable Energy Laboratory, South Table Mountain Campus, Golden, Colorado

The National Renewable Energy Laboratory (NREL) South Table Mountain campus, located in Golden, Colorado, has multiyear datasets from buildings with approximately 1,100,000 sq ft of floor area. These 16 buildings include a large office, nine laboratories, and other education or public assembly facilities such as an education center, a warehouse, quick service restaurants, two site entrance security buildings, and a parking garage—all constructed and improved from 1982 to 2014. These datasets are also available from the NREL data storage and management platform SkySparks. The datasets contain electricity meter data (poser, voltage, and current) with 1-minute resolution and building automation system (BAS) data with resolutions of 5 or 15 minutes. Some subsystems (e.g., lighting) or equipment electricity data have the same intervals as above. Some mechanical equipment state or condition data, such as flow and temperature, are captured with 5- to 15-minute intervals. The types of measurement points (i.e., electrical meter power and zone temperature) and associated data quality across the campus are typical, which helps reduce the unreliability of data quality.

STM, Systems and equipment operational, Energy use↗
DOE OSTI2024

DOE Data Days 2023 Report

The DOE Data Days (D3) workshop brings together data managers, developers, researchers, and program managers across the Department of Energy (DOE) and national laboratories to highlight data management successes, identify potential synergies and common problems, and establish channels for collaboration across the DOE data management community. The fourth D3 workshop was held on October 24th to 26th, 2023 held entirely in-person at Lawrence Livermore National Laboratory (LLNL). The workshop was organized by a multi laboratory committee in an effort to bring data management practitioners at the DOE laboratories together to share their work and results, facilitating knowledge transfers and best practices across project teams. Tools and platforms to support data management and analysis are rapidly evolving and provide enormous opportunities. This report summarizes the important discussions and recommendations from the different working sessions and contains the agenda, submitted abstracts, presentations with links to recorded presentations, breakout session summaries, list of registered attendees, and lessons learned for future organizing committee. The report will be distributed to the DOE, each participating institution’s programmatic stakeholders, and attendees. The dedicated D3 website will host presentations, agenda, and report that is accessible by all labs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Instantiation of the Damara Tern Platform for Advanced Materials and Manufacturing Technologies (AMMT) Program Collaborative Data Management

This work package focused on deploying an instance of the Damara Tern platform to support AMMT collaborative research activities. The objectives were to provide selected AMMT collaborators with access to a shared environment for capturing operations, trackables, and associated metadata, and to implement data entry functionalities that reflect site-specific procedures. Key activities included creating configurable, schema-driven entry forms and validating the data collection process. The report details the deployment process, the platform infrastructure, and the implemented data entry workflows, providing a reference for end users and establishing a foundation for future production-scale deployments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Advances in building data management for building performance standards using the SEED platform

Reducing energy consumption and greenhouse gas emissions in the built environment is a critical step in achieving emission goals to mitigate climate change impacts. Local, federal, and international jurisdictions are deploying several methods to reduce energy and emissions such as voluntary and mandatory benchmarking and building performance standards, requiring building owners to reach energy and emission targets. Jurisdictions leveraging benchmarking and building performance standards require knowledge of the buildings covered; which is a large task due to staffing constraints, limited information on building characteristics and tax parcel data, and the need for advanced data management techniques to align datasets. This paper describes an open-source platform's recent advances to create consistent taxonomies, identify erroneous data, enable auditability, and track building performance. The paper concludes with two use cases on how the platform has been used by jurisdictions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

The IsoGenie database: an interdisciplinary data management solution for ecosystems biology and environmental research

Modern microbial and ecosystem sciences require diverse interdisciplinary teams that are often challenged in “speaking” to one another due to different languages and data product types. Here we introduce the IsoGenie Database, a de novo developed data management and exploration platform, as a solution to this challenge of accurately representing and integrating heterogenous environmental and microbial data across ecosystem scales. The IsoGenieDB is a public and private data infrastructure designed to store and query data generated by the IsoGenie Project, a ~10 year DOE-funded project focused on discovering ecosystem climate feedbacks in a thawing permafrost landscape. The IsoGenieDB provides (i) a platform for IsoGenie Project members to explore the project’s interdisciplinary datasets across scales through the inherent relationships among data entities, (ii) a framework to consolidate and harmonize the datasets needed by the team’s modelers, and (iii) a public venue that leverages the same spatially explicit, disciplinarily integrated data structure to share published datasets. The IsoGenieDB is also being expanded to cover the NASA-funded Archaea to Atmosphere (A2A) project, which scales the findings of IsoGenie to a broader suite of Arctic peatlands, via the umbrella A2A Database (A2A-DB). The IsoGenieDB’s expandability and flexible architecture allow it to serve as an example ecosystems database.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

DOE Data Day 2019 Report

The Department of Energy (DOE) has joined the larger scientific community in the promotion of data management as a means to higher quality, more efficient research and analysis, and as a critical component of data science. Tools and platforms to support data management and analysis are rapidly evolving and provide enormous opportunities. They also pose challenges that can be specific to DOE but are common across DOE mission areas and organizations. The DOE Data Day (D3) workshop was held on September 25–26, 2019, and organized by a team at Lawrence Livermore National Laboratory (LLNL) in an effort to gather the data management practitioners at the DOE labs together to share their work and results, facilitating knowledge transfers and best practices across project teams. Over 100 attendees participated from multiple national labs—LLNL, Sandia (SNL), Lawrence Berkeley (LBNL), Los Alamos (LANL), Oak Ridge (ORNL), Argonne (ANL), Pacific Northwest (PNNL), Stanford Linear Accelerator (SLAC)—along with Mission Support and Test Services (MSTS), National Energy Technology Laboratory (NETL), Pantex, National Security Complex (Y-12), National Nuclear Security Administration (NNSA), the DOE Office of Scientific and Technical Information (OSTI), and elsewhere in the DOE complex.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗