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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

OPTIMADE, an API for exchanging materials data

Abstract The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2014

Data Preparation Process for the Buildings Performance Database

The Buildings Performance Database (BPD) includes empirically measured data from a variety of data sources with varying degrees of data quality and data availability. The purpose of the data preparation process is to maintain data quality within the database and to ensure that all database entries have sufficient data for meaningful analysis and for the database API. Data preparation is a systematic process of mapping data into the Building Energy Data Exchange Specification (BEDES), cleansing data using a set of criteria and rules of thumb, and deriving values such as energy totals and dominant asset types. The data preparation process takes the most amount of effort and time therefore most of the cleansing process has been automated. The process also needs to adapt as more data is contributed to the BPD and as building technologies over time. The data preparation process is an essential step between data contributed by providers and data published to the public in the BPD.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1992

Ramp Technology and Intelligent Processing in Small Manufacturing

To address the issues of excessive inventories and increasing procurement lead times, the Navy is actively pursuing flexible computer integrated manufacturing (FCIM) technologies, integrated by communication networks to respond rapidly to its requirements for parts. The Rapid Acquisition of Manufactured Parts (RAMP) program, initiated in 1986, is an integral part of this effort. The RAMP program's goal is to reduce the current average production lead times experienced by the Navy's inventory control points by a factor of 90 percent. The manufacturing engineering component of the RAMP architecture utilizes an intelligent processing technology built around a knowledge-based shell provided by ICAD, Inc. Rules and data bases in the software simulate an expert manufacturing planner's knowledge of shop processes and equipment. This expert system can use Product Data Exchange using STEP (PDES) data to determine what features the required part has, what material is required to manufacture it, what machines and tools are needed, and how the part should be held (fixtured) for machining, among other factors. The program's rule base then indicates, for example, how to make each feature, in what order to make it, and to which machines on the shop floor the part should be routed for processing. This information becomes part of the shop work order. The process planning function under RAMP greatly reduces the time and effort required to complete a process plan. Since the PDES file that drives the intelligent processing is 100 percent complete and accurate to start with, the potential for costly errors is greatly diminished.

Rentz, Richard E.↗
NASA NTRS1990

CCSDS - An approach to the definition of common standards for understanding space-related data

International cooperation for space data projects requires common data processing goals and unique data exchange mechanisms. The absence of standards has led to project unique interface definitions and special software on diverse systems. To address this problem, CCSDS Panel 2 is creating a set of standards to support self description of data using standard structures. The proposed standard data interchange mechanism - the Standard Formatted Data Unit (SFDU) - reduces information loss in data transfers, increases automated information exchange, and extends the lifetime of data. Data interchange structures, languages, and services are being developed with necessary control functions to provide these benefits. A set of recommendations for agency review has been issued this year to be the basis for future data exchange and to enhance access to older mission data sets. Space agencies and other space data handling facilities are being encouraged to implement the SFDU concept for ongoing and planned projects.

Drexler, Manfred↗
NASA NTRS1997

Querying Semi-Structured Data

The amount of data of all kinds available electronically has increased dramatically in recent years. The data resides in different forms, ranging from unstructured data in the systems to highly structured in relational database systems. Data is accessible through a variety of interfaces including Web browsers, database query languages, application-specic interfaces, or data exchange formats. Some of this data is raw data, e.g., images or sound. Some of it has structure even if the structure is often implicit, and not as rigid or regular as that found in standard database systems. Sometimes the structure exists but has to be extracted from the data. Sometimes also it exists but we prefer to ignore it for certain purposes such as browsing. We call here semi-structured data this data that is (from a particular viewpoint) neither raw data nor strictly typed, i.e., not table-oriented as in a relational model or sorted-graph as in object databases. As will seen later when the notion of semi-structured data is more precisely de ned, the need for semi-structured data arises naturally in the context of data integration, even when the data sources are themselves well-structured. Although data integration is an old topic, the need to integrate a wider variety of data- formats (e.g., SGML or ASN.1 data) and data found on the Web has brought the topic of semi-structured data to the forefront of research. The main purpose of the paper is to isolate the essential aspects of semi- structured data. We also survey some proposals of models and query languages for semi-structured data. In particular, we consider recent works at Stanford U. and U. Penn on semi-structured data. In both cases, the motivation is found in the integration of heterogeneous data.

DATA MANAGEMENT↗
DOE OSTI2015

Interoperability of Materials Database Systems in Support of Nuclear Energy Development and Potential Applications for Fuel Cell Material Selection

Materials database interoperability has been of great interest in recent years for information exchange in support of research and development (R&D). In response to data and knowledge sharing needs of the GenIV International Forum (GIF) for global collaboration in nuclear energy R&D, the European Commission JRC Institute for Energy and Transport (JRC-IET) and the Oak Ridge National Laboratory (ORNL) have established a materials database interoperability project that develops techniques for automated materials data exchange between systems hosted at the two institutes MatDB Online at JRC IET and the Gen IV Materials Handbook at ORNL, respectively. The work to enable automated exchange of data between the two systems leverages the XML data import and export functionalities of both systems in combination with recently developed standards for engineering materials data. The preliminary results of data communication between the two systems have demonstrated the feasibility and efficiency of materials database interoperability, which constructs an interoperation framework that can be seamlessly integrated into the high-throughput First Principles material databases and thus advance the discovery of novel materials in fuel cell applications.

36 MATERIALS SCIENCE↗
DOE OSTI2012

A COUPLED TH/NEUTRONICS/CRUD FRAMEWORK IN PREDICTION OF CIPS PHENOMENON

A coupled TH/Neutronics/CRUD framework, which is able to simulate the CRUD deposits impact on CIPS phenomenon, was described in this paper. This framework includes the coupling among three essential physics, thermal-hydraulics, CRUD and neutronics. The overall framework was implemented by using the CFD software STAR-CCM+, developing CRUD codes, and using the neutronics code DeCART. The coupling was implemented by exchanging data between softwares using intermediate exchange files. A typical 3 by 3 PWR fuel pin problem was solved under this framework. The problem was solved in a 12 months length period of time. Time-dependent solutions were provided, including CRUD deposits inventory and their distributions on fuels, boron hideout amount inside CRUD deposits, as well as power shape changing over time. The results clearly showed the power shape suppression in regions where CRUD deposits exist, which is a strong indication of CIPS phenomenon.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Solar Bankability Data to Advance Transactions and Access (SB-DATA) Final Technical Report

The DOE project “Solar Bankability Data to Advance Transactions and Access”, known in the solar industry and referred to in this report as “Orange Button”, had project objectives of convening key stakeholder participants in the solar project lifecycle to develop a data taxonomy that meets to the solar industry’s needs for standardized data types for use in solar projects. Additional Orange Button deliverables included the development of an online data exchange to act as a clearinghouse of data that conformed to the data standard and additional solar industry data, and a Solar Data Translation Platform that could transform legacy solar project data to conform to the Orange Button data standard. All of these objectives were achieved. The Smart Electric Power Alliance (SEPA) provided program management, industry participant recruiting and convening, worked with the solar industry to define the scope of the Orange Button data taxonomy, and documented and delivered the taxonomy data requirements to funded project partners to develop both the taxonomy and the data conversion tools. Further efforts included performing extensive industry outreach and awareness to encourage adoption of the Orange Button taxonomy by the solar industry by enumerating the cost saving benefits of using the taxonomy and the public benefit of the increased use of solar energy due to increased bankability and viability of solar development projects. Solar PV panel and component prices have fallen in the last decade, while soft costs have remained stubbornly high and can account for as much as 64% of the total cost of a new solar system. Part of the U.S. Department of Energy SunShot Initiative, Orange Button enabled the creation and adoption of industry-led open data standards for rapid and seamless information exchange across the solar value chain, from origination to decommissioning. Standardizing data will reduce soft costs by making it easier to share solar data and speeding up processes. The taxonomy is a product of solar industry participants convening to identify the pain points caused by incompatible data, and to agree on and standardize data types to be used by the actors in the solar project lifecycle. This report focuses on the SEPA led first phase of Orange Button project that included industry outreach and recruiting participants for five industry working groups, widespread industry outreach to increase awareness and encourage eventual adoption of the taxonomy, and the definition of the scope, use cases, and supporting data types of the Orange Button taxonomy. The solar industry has been receptive of the completed Orange Button taxonomy. In SEPA’s work, 401 solar industry participants facilitated by SEPA defined the scope, use cases, and requirements for the taxonomy leading to over 150 taxonomy early adopters incorporating the taxonomy into their software products serving the solar industry, and several large investors and banks have started projects to enable them to utilize the taxonomy to gain back-office efficiencies. Moving forward, as the solar industry more widely embraces the Orange Button taxonomy and begins realizing the tangible benefits of adoption at scale, we expect further pressure on solar soft costs and increases in the bankability of solar projects.

14 SOLAR ENERGY↗
DOE OSTI2024

Emulation and detection of physical faults and cyber-attacks on building energy systems through real-time hardware-in-the-loop experiments

The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physical faults (e.g., HVAC -heating, ventilation, and air-conditioning faults) may have similar adverse impacts as those from the cyber-attacks on building energy systems, such as occupant discomfort, energy wastage, and equipment downtime. However, current physical behavior-based anomaly detection methods fail to differentiate between cyber-attacks and physical faults in building energy systems. Moreover, the challenge in collecting real-world threat data with ground truth has led researchers to rely on numerical models with user-defined assumptions, which may not accurately reflect real-world conditions due to the lack of in-situ experimental datasets. To address these challenges and gaps, this paper presents a flexible hardware-in-the-loop (HIL) testbed for generating cyber-attack and physical fault datasets and demonstrating threat detection algorithms in a real building automation system (BAS) environment. This testbed combines hardware (i.e., real BAS with local HVAC controllers and a physical network) with software (i.e., high-fidelity models to represent behaviors of building envelope and HVAC energy systems), enabling emulations of realistic threats. Five HIL experiments, including one baseline without any threats, two with physical faults, and two with cyber-attacks, were conducted to generate datasets containing detailed network traffic and system states. A joint classification framework, incorporating a network analyzer and a physical HVAC fault detector, was proposed to automatically detect cyber-physical abnormalities on BAS at both the network and the physical HVAC levels. The network analyzer comprises a conditional random fields (CRF) based command validator and a statistics-based detection strategy. The fault detector employs a weather and schedule-based pattern matching and feature-based principal component analysis (WPM-FPCA) method. Evaluation of the classification using four metrics from the multi-class confusion matrix revealed an average accuracy of 90.2%, recall of 89.7%, precision of 88.5% and F1-score of 89.2%. Finally, these results demonstrate that the proposed joint classification framework can effectively differentiate between specific types of cyber-attacks (e.g., device reinitialization attack, network Denial-of-Service attack) and physical faults (e.g., air handling unit operational fault, cooling coil valve stuck) in real time for improved building energy management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Carbon Storage Open Database

The Carbon Storage Open Database is a collection of spatial data obtained from publicly available sources published by several NATCARB Partnerships and other organizations. The carbon storage open database was collected from open-source data on ArcREST servers and websites in 2018, 2019, 2021, and 2022. The original database was published on the former GeoCube, which is now EDX Spatial, in July 2020, and has since been updated with additional data resources from the Energy Data eXchange (EDX) and external public data resources. The shapefile geodatabase is available in total, and has also been split up into multiple databases based on the maps produced for EDX spatial. These are topical map categories that describe the type of data, and sometimes the region for which the data relates. The data is separated in case there is only a specific area or data type that is of interest for download. In addition to the geodatabases, this submission contains: 1. A ReadMe file describing the processing steps completed to collect and curate the data. 2. A data catalog of all feature layers within the database. Additional published resources are available that describe the work done to produce the geodatabase: Morkner, P., Bauer, J., Creason, C., Sabbatino, M., Wingo, P., Greenburg, R., Walker, S., Yeates, D., Rose, K. 2022. Distilling Data to Drive Carbon Storage Insights. Computers & Geosciences. https://doi.org/10.1016/j.cageo.2021.104945 Morkner, P., Bauer, J., Shay, J., Sabbatino, M., and Rose, K. An Updated Carbon Storage Open Database - Geospatial Data Aggregation to Support Scaling -Up Carbon Capture and Storage. United States: N. p., 2022. Web. https://www.osti.gov/biblio/1890730 Morkner, P., Rose, K., Bauer, J., Rowan, C., Barkhurst, A., Baker, D.V., Sabbatino, M., Bean, A., Creason, C.G., Wingo, P., and Greenburg, R. Tools for Data Collection, Curation, and Discovery to Support Carbon Sequestration Insights. United States: N. p., 2020. Web. https://www.osti.gov/biblio/1777195 Disclaimer: This project was funded by the United States Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

carbon storage↗
DOE OSTI2015

Design of an Open Smart Energy Gateway for Smart Meter Data Management

With the widespread deployment of electronic interval meters, commonly known as smart meters, came the promise of real-time data on electric energy consumption. Recognizing an opportunity to provide consumers access to their near real-time energy consumption data directly from their installed smart meter, we designed a mechanism for capturing those data for consumer use via an open smart energy gateway (OpenSEG). By design, OpenSEG provides a clearly defined boundary for equipment and data ownership. OpenSEG is an open-source data management platform to enable better data management of smart meter data. Effectively, it is an information architecture designed to work with the ZigBee Smart Energy Profile 1.x (SEP 1.x). It was specifically designed to reduce cyber-security risks and provide secure information directly from smart meters to consumers in near real time, using display devices already owned by the consumers. OpenSEG stores 48 hours of recent consumption data in a circular cache using a format consistent with commonly available archived (not real-time) consumption data such as Green Button, which is based on the Energy Services Provider Interface (ESPI) data standard. It consists of a common XML format for energy usage information and a data exchange protocol to facilitate automated data transfer upon utility customer authorization. Included in the design is an application program interface by which users can acquire data from OpenSEG for further post processing. A sample data display application is included in the initial software product. The data display application demonstrates that OpenSEG can help electricity use data to be retrieved from a smart meter and ported to a wide variety of user-owned devices such as cell phones or a user-selected database. This system can be used for homes, multi-family buildings, or small commercial buildings in California.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

WEC-SIM Support for an Innovative Zero Discharge Supercritical Water Based Wave Energy Desalination System (CRADA Final Report)

NREL will assist East Carolina University in its development of the foundational knowledge and proof of concept that are needed for future process scale up and commercialization of a sustainable wave-to-water (direct pressurization) desalination unit powered by a wave energy converter. NREL will provide guidance and support to East Carolina University on its: use of the NREL developed WEC-Sim based wave to water system, exploring the WEC-Sim/ASPEN Plus data exchange communication, and using experimental data to hopefully provide validation of analytical models.

16 TIDAL AND WAVE POWER↗
NASA NTRS2004

Development and Evaluation of an Airborne Separation Assurance System for Autonomous Aircraft Operations

NASA Langley Research Center is developing an Autonomous Operations Planner (AOP) that functions as an Airborne Separation Assurance System for autonomous flight operations. This development effort supports NASA s Distributed Air-Ground Traffic Management (DAG-TM) operational concept, designed to significantly increase capacity of the national airspace system, while maintaining safety. Autonomous aircraft pilots use the AOP to maintain traffic separation from other autonomous aircraft and managed aircraft flying under today's Instrument Flight Rules, while maintaining traffic flow management constraints assigned by Air Traffic Service Providers. AOP is designed to facilitate eventual implementation through careful modeling of its operational environment, interfaces with other aircraft systems and data links, and conformance with established flight deck conventions and human factors guidelines. AOP uses currently available or anticipated data exchanged over modeled Arinc 429 data buses and an Automatic Dependent Surveillance Broadcast 1090 MHz link. It provides pilots with conflict detection, prevention, and resolution functions and works with the Flight Management System to maintain assigned traffic flow management constraints. The AOP design has been enhanced over the course of several experiments conducted at NASA Langley and is being prepared for an upcoming Joint Air/Ground Simulation with NASA Ames Research Center.

Barhydt, Richard↗
DOE OSTI2012

A coupled TH/Neutronics/CRUD framework in prediction of CIPS phenomenon

A coupled TH/Neutronics/CRUD framework, which is able to simulate the CRUD deposits impact on CIPS phenomenon, was described in this paper. The coupling among three essential physics, thermal-hydraulics, CRUD and neutronics described in the framework was implemented by using CFD software STAR-CCM+, developing CRUD module, and using the neutronics code DeCART. The coupling among these codes was implemented by exchanging data between them using intermediate exchange files. A typical 3 by 3 PWR fuel pin problem was solved under this framework and the results were presented. Time-dependent solutions were provided for a 12-month simulation, including CRUD deposits inventory and their distributions on fuel rods, boron hideout amount inside CRUD deposits, as well as power shape changing over time. The results clearly showed the power shape suppression in regions where CRUD deposits exist, which is a clear indication of CIPS phenomenon. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Automating data analysis for hydrogen/deuterium exchange mass spectrometry using data-independent acquisition methodology

We present a hydrogen/deuterium exchange workflow coupled to tandem mass spectrometry (HX-MS 2 ) that supports the acquisition of peptide fragment ions alongside their peptide precursors. The approach enables true auto-curation of HX data by mining a rich set of deuterated fragments, generated by collisional-induced dissociation (CID), to simultaneously confirm the peptide ID and authenticate MS 1 -based deuteration calculations. The high redundancy provided by the fragments supports a confidence assessment of deuterium calculations using a combinatorial strategy. The approach requires data-independent acquisition (DIA) methods that are available on most MS platforms, making the switch to HX-MS 2 straightforward. Importantly, we find that HX-DIA enables a proteomics-grade approach and wide-spread applications. Considerable time is saved through auto-curation and complex samples can now be characterized and at higher throughput. We illustrate these advantages in a drug binding analysis of the ultra-large protein kinase DNA-PKcs, isolated directly from mammalian cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

yuwangcn/C4_dynamic_model

A dynamic systems model of C4 photosynthesis was developed based on the previous NADP-ME metabolic model for maize (Wang et al., 2014 ab). The NADP-ME metabolic model is an ordinary differential equation model including all individual steps in C4 photosynthetic carbon metabolism. Here, the model is extended to include posttranslational regulation and temperature response of enzyme activities, dynamic stomata conductance, and leaf energy balance. This model is written in Matlab (R2019a) Steady-state and dynamic gas exchange data for maize (B73), sugarcane (CP88-1762) and sorghum (Tx430) were measured in a greenhouse in Urbana, IL from July 25, 2019 through August 8, 2019. Data include: CO2 response curves Light response curves Photosynthetic induction curves measured in the transition from darkness to high light (1800 μmol m-2 s-1), data logged every 1 min. Photosynthetic induction in the transition from darkness to high light (1800 μmol m-2 s-1) to determine the kinetics of rubisco activation in these C4 crops (τ_Rubisco), data logged every 10 s. Gas exchange under fluctuating light. After dark adaptation, the leaves undergo three light change steps, light intensity was set as 1800 µmol m-2 s-1, 200 µmol m-2 s-1 and 1800 µmol m-2 s-1 for each 1800 s step.

Wang, Yu↗
DOE OSTI2015

Cooperative storage of shared files in a parallel computing system with dynamic block size

Improved techniques are provided for parallel writing of data to a shared object in a parallel computing system. A method is provided for storing data generated by a plurality of parallel processes to a shared object in a parallel computing system. The method is performed by at least one of the processes and comprises: dynamically determining a block size for storing the data; exchanging a determined amount of the data with at least one additional process to achieve a block of the data having the dynamically determined block size; and writing the block of the data having the dynamically determined block size to a file system. The determined block size comprises, e.g., a total amount of the data to be stored divided by the number of parallel processes. The file system comprises, for example, a log structured virtual parallel file system, such as a Parallel Log-Structured File System (PLFS).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Aligning NASA Earth Science Data Stewardship with FAIR Principles: Outcomes, Recommendations, and Future Directions

The FAIR Principles—Findable, Accessible, Interoperable, and Reusable—offer a widely accepted framework for improving the sharing and reuse of digital scientific data by both human and machine users. Following these principles is critical for effective scientific data stewardship, broader scientific collaboration, and compliance with federal and agency data policies. This paper, based on the work of NASA’s Open, Free, and FAIR Working Group (O’FAIR WG) under the Earth Science Data Systems Program, presents an overview of how FAIR is being applied within NASA’s Earth science data landscape. It highlights ongoing progress and challenges, identifies FAIR-enabling resources, and offers recommendations and strategic actions to enhance the FAIRness of NASA-funded open and free Earth science data products. The FAIR-enabling resources identified underscore the vital role of NASA's existing enterprise processes, standards, tools, and infrastructures in supporting FAIR implementation. Our findings show strong performance in making NASA Earth science data more findable and accessible. However, further work is needed—especially in enhancing interoperability, so that different systems and tools can better understand and exchange data. This is especially important for enabling machine-driven discovery and analysis. We emphasize the importance of a balanced strategy that combines a centralized, top-down approach—focused on building enterprise-level capabilities and processes—with a decentralized, bottom-up approach driven by discipline-specific needs and community practices. We advocate for coordinated efforts to enhance (meta)data interoperability to facilitate seamless data and information sharing and exchange of Earth science data both within NASA and across other agencies managing Earth science data.

Data Product↗