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

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At least 1,585 records · Page 88
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Knowledge Discovery for Early Failure Assessment of Complex Engineered Systems Using Natural Language Processing

Emerging complex engineered systems may have unexpected safety issues due to novel operational environments, increasing autonomy, human-machine interaction, and other factors. To prevent failures in operation or testing that necessitate costly redesign, it is desirable to predict likely failure modes early in the design process. Information about past engineering failures in natural language format presents one possible solution by enabling the retrieval of information that can inform new designs. However, identifying documents containing usable information and extracting the required information can be prohibitively time-consuming when implemented at scale. In this research, an automated natural language processing (NLP) framework is proposed to discover relevant knowledge from documents containing failure-related design information. The framework is applied to NASA’s Lessons Learned Information System (LLIS),which is publicly available. Documents containing usable information are filtered using two different NLP-based models. Next, from the identified usable documents, a failure taxonomy is extracted using a partitioned hierarchical topic modeling approach. Partitions of the document describe different sections of the failure taxonomy – i.e., failure, cause of failure, and recommendations – as indicated by the structure of the original document. The extracted failure taxonomy can be leveraged in early design failure assessment methods. Moreover, the framework can be used to identify documents containing usable failure-related design information from other databases and extract relevant information from these documents.

Documentation and Information Science↗
NASA NTRSDate not supplied

Knowledge Discovery for Early Failure Assessment of Complex Engineered Systems Using Natural Language Processing

Emerging complex engineered systems may have unexpected safety issues due to novel operational environments, increasing autonomy, human-machine interaction, and other factors. To prevent failures in operation or testing that necessitate costly redesign, it is desirable to predict likely failure modes early in the design process. Information about past engineering failures in natural language format presents one possible solution by enabling the retrieval of information that can inform new designs. However, identifying documents containing usable information and extracting the required information can be prohibitively time-consuming when implemented at scale. In this research, an automated natural language processing (NLP) framework is proposed to discover relevant knowledge from documents containing failure-related design information. The framework is applied to NASA’s Lessons Learned Information System (LLIS),which is publicly available. Documents containing usable information are filtered using two different NLP-based models. Next, from the identified usable documents, a failure taxonomy is extracted using a partitioned hierarchical topic modeling approach. Partitions of the document describe different sections of the failure taxonomy – i.e., failure, cause of failure, and recommendations – as indicated by the structure of the original document. The extracted failure taxonomy can be leveraged in early design failure assessment methods. Moreover, the framework can be used to identify documents containing usable failure-related design information from other databases and extract relevant information from these documents.

Documentation and Information Science↗
NASA NTRSDate not supplied

Using XR for Improving Scientific Discovery With Numerical Weather Models

Our work explores the use of extended reality (XR) to im- prove scientific discovery with numerical weather/climate models that inform Earth science digital twins, specifically the NASA Goddard Earth Observing System (GEOS) global atmospheric model. The overall project is named the Vi- sualization And Lagrangian dynamics Immersive eXtended Reality Toolkit (VALIXR), which has two main areas of focus: (1) enhancing the understanding of and interaction with model output data through advanced visualizations in the XR environment, and (2) the integration of Lagrangian dynamics into the GEOS model, which allows a natural, feature-specific analysis of Earth science phenomena as op- posed to traditional, fixed-point Eulerian dynamics. Here, we report initial work on these focus areas.

Thomas Grubb↗
NASA NTRS2024

Proceedings of the Quantum Sensing Workshop, September 2022

To understand the NASA needs for quantum-sensing technologies and the capabilities developed internal and external to NASA, a workshop was held on September 27 through 29, 2022, in Newport News, VA. This workshop brought together senior leadership within NASA, technical experts within the quantum sensing community, NASA scientists and potential end-users of quantum-sensing technologies, and external stakeholders. This document contains the papers and presentations given at the Workshop.

Quantum Sensing↗
NASA NTRSDate not supplied

Space-based Sensor for Extreme Fire Weather Events

Catastrophic bushfires are becoming increasingly prevalent as climate change advances. Impacts extend beyond national borders. Multinational efforts can inform new science and management practices. Space-based sensors and integrated data facilities will play an important role. This paper describes a collaborative project between a consortium of Australian universities and NASA Centers to develop and implement a small satellite platform comprising highly integrated thermal and lightning sensors coupled with AI-based edge computing to help predict, detect, and track bushfires, supporting mitigation activities. This will fill an important capability gap since Australia does not currently have any sovereign Earth observation satellites. This program is enabled by and builds on Australia-NASA collaboration and will also support fire science and management activities in the broader global context.

wildfire↗
DOE OSTI2016

Open Data and the Free Access to Law Movement in the United States

US President Barack Obama issued Executive Order 13642-Making Open and Machine Readable the New Default for Government - on May, 9 2013, mandating, wherever legally permissible and possible, that US Government information be made open to the public.[1] This edict accelerated the construction of and framework for data repositories, and data citation principles and practices, such as data.gov. As a corollary, researchers across the country's national laboratories found themselves creating data management plans, applying data set metadata standards, and ensuring the long-term access of data for federally funded scientific research.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2017

Girls in STEM Year 1

Explore the source record for details and available documents.

Information Science↗
DOE OSTI2017

Autoload: a pipeline for expanding the holdings of an Institutional Repository enabled by ResourceSync

Providing local access to locally produced content is a primary goal of the Institutional Repository (IR). Guidelines, requirements, and workflows are among the ways in which institutions attempt to ensure this content is deposited and preserved, but some content is always missed. At Los Alamos National Laboratory, the library implemented a service called LANL Research Online (LARO), to provide public access to a collection of publicly shareable LANL researcher publications authored between 2006 and 2016. LARO exposed the fact that we have full text for only about 10% of eligible publications for this time period, despite a review and release requirement that ought to have resulted in a much higher deposition rate. This discovery motivated a new effort to discover and add more full text content to LARO. Autoload attempts to locate and harvest items that were not deposited locally, but for which archivable copies exist. Here we describe the Autoload pipeline prototype and how it aggregates and utilizes Web services including Crossref, SHERPA/RoMEO, and oaDOI as it attempts to retrieve archivable copies of resources. Autoload employs a bootstrapping mechanism based on the ResourceSync standard, a NISO standard for resource replication and synchronization. We implemented support for ResourceSync atop the LARO Solr index, which exposes metadata contained in the local IR. This allowed us to utilize ResourceSync without modifying our IR. Here, we close with a brief discussion of other uses we envision for our ResourceSync-Solr implementation, and describe how a new effort called Signposting can replace cumbersome screen scraping with a robust autodiscovery path to content which leverages Web protocols.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗