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

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Requirement Discovery Using Embedded Knowledge Graph With ChatGPT

The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) con-cept focuses on providing air-taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT-4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user’s question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human-readable answers for the user. Our team implemented and tested both solutions to analyze require-ments within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches.

systems engineering↗
NASA NTRSDate not supplied

Requirement Discovery Using Embedded Knowledge Graph With ChatGPT - Poster

The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) con-cept focuses on providing air-taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT-4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user’s question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human-readable answers for the user. Our team implemented and tested both solutions to analyze require-ments within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches.

systems engineering↗
NASA NTRSDate not supplied

Requirement Discovery Using Embedded Knowledge Graph with ChatGPT

- NASA’s Air Traffic Management-Exploration (ATM-X) Urban Air Mobility (UAM) Airspace Subproject is conducting research that evolves UAM airspace towards a highly automated and operationally flexible system of the future. - (see https://www.nasa.gov/uam-overview/ for more information) - The complexity of UAM airspace, and its evolution through a series of transformative epochs, requires a planning tool to effectively organize, integrate, and communicate the research that will guide the evolution of UAM operations in the National Airspace System (NAS). - The planning tool, called the UAM airspace research roadmap (or just roadmap), is being developed as a new system engineering methodology leveraging model based system engineering (MBSE) and artificial intelligence capabilities. This presentation gives an overview of the Knowledge Graph and ChatGPT applications within this system engineering methodology and will describe how it is being used to meet the ATM-X UAM Airspace Subproject’s overarching research goals.

systems engineering↗
NASA NTRSDate not supplied

Developing a Vision for Heliophysics Infrastructure: The LIKED Resource and the DIARieS Ecosystem

Heliophysics data and computational infrastracture are not equipped for 21st science, suffering from holes in the know-how to build better systems. Without a clear vision, efforts to improve the infrastructure have been incremental and incoherent. This poster presents both the vision and the technology required: an online LIbrary KnowledgE and Discovery (LIKED) resource for discovering and implementing knowledge, data, and infrastructure resources; and an online analysis ecosystem to simplify Discovery, Implementation, Analysis, Reproducibility, and Sharing (DIARieS) of scientific results and environments. The LIKED and DIARieS solutions adopt FAIR data principles and the best practices from the budding field of open science. The proposed new infrastructure components will close many of the current gaps in heliophysics’ infrastructure, such as the ability to search for data and knowledge by phenomenon across domains, and to find software and examples relevant to the desired data set (including model data). Further, these components will enable community members to more efficiently use the resources already present and improve upon the content via a community-curated and trusted library. Combining these solutions lowers the barriers to heliophysics resources for all, increasing the return on our investments. Finally, the structure behind these ideas are topic-agnostic, so they are fully extensible to other fields, leading to invaluable connections to other disciplines. Just as with the development and construction of a long-term satellite mission, we must work together as a community to build a vision of the infrastructure that will most benefit the community, and then collaborate to construct, assemble, and test all the necessary pieces individually and as a unit. Our purpose in presenting this work is to not only describe the proposed vision, but also to gather feedback from the community on this topic.

infrastructure↗
DOE OSTI2025

Causal discovery from data assisted by large language models

Knowledge-driven discovery of novel materials necessitates the development of causal models for property emergence. While in the classical physical paradigm, the causal relationships are deduced based on physical principles or via experiment, the rapid accumulation of observational data necessitates learning causal relationships between dissimilar aspects of material structure and functionalities based on observations. For this, it is essential to integrate experimental data with prior domain knowledge. Here, we demonstrate this approach by combining high-resolution scanning transmission electron microscopy data with insights derived from large language models (LLMs). By applying ChatGPT to domain-specific literature, such as arXiv papers on ferroelectrics, and combining the obtained information with data-driven causal discovery, we construct adjacency matrices for directed acyclic graphs that map the causal relationships between structural, chemical, and polarization degrees of freedom in Sm-doped BiFeO 3 . This approach enables us to hypothesize how synthesis conditions influence material properties and guides experimental validation. Furthermore, the ultimate objective of this work is to develop a unified framework that integrates LLM-driven literature analysis with data-driven discovery, facilitating the precise engineering of ferroelectric materials by establishing clear connections between synthesis conditions and their resulting material properties.

Causal inference↗
NASA NTRSDate not supplied

Development of a Knowledge Graph for Dataset Discovery and Identification at a NASA Data Center

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) archives and distributes hundreds of Earth Science data collections to the public. These collections are used in research, resulting in the publication of thousands of scientific papers each year. As new users come to GES DISC for data, it is important for them to understand how prior research used the data. To help researchers, a knowledge graph (KG) was designed and implemented to connect publication citations with dataset metadata. The relationships created in the graph have the potential to allow the Web applications that utilize this information to directly connect the publication to the GES DISC datasets and services. These relationships are demonstrated using a web application prototype. In addition, the graph can also make connections between publications, datasets, and measurements based on the mentions of datasets and their attributes in the publications. To demonstrate this capability, a web application was created that takes the excerpt from the publication and returns a most likely dataset and measurement pairing, ranking the results based on how often these datasets and measurements were used in prior publications.

Nathaniel Crosby↗
DOE OSTI2021

AI Automated Discovery of New Climate Water System Knowledge from Models and Observations

This paper addresses focus area 3, “Insight gleaned from complex data.” Science Challenge: The climate-water system is highly complex, containing a multitude of positive and negative feedbacks, time scales stretching many orders-of-magnitudes, and non-linear, connected processes. Powerful artificial intelligence (AI) methods can revolutionize and automate the discovery of new knowledge and relationships in the climate-water system, which will improve understanding and predictability of extreme hydrological events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Interactive Web Application for Traffic Simulation Data Management and Visualization

As traffic simulation software becomes more effective for realistically simulating and analyzing traffic dynamics and vehicle interactions on the mesoscopic and microscopic level, the management, dissemination, and collaborative visualization of traffic simulation results produced by individual transportation planners presents a significant challenge. Existing online content management systems have a very limited capability in allowing users to query specific traffic simulation scenarios and geospatially visualize simulation results through shareable and interactive web interfaces. This paper presents a web-based application for promoting the archiving, sharing, and visualization of large-scale traffic simulation outputs. The application is developed to enhance cyber-physical controls, communications, and public education for collaborative transportation planning. Unique features of the web application include: (a) allowing users to upload their new traffic simulation scenarios (parameters and outputs), as well as search existing scenarios using easily accessible interfaces; (b) optimizing simulation output files with heterogeneous data formats and projected coordinate systems for web-based storage and management using a scalable and searchable data/metadata standard; (c) standardizing user-uploaded simulation outputs using web interfaces and data processing libraries with parallel computing capacity; and (d) providing shareable web visual interfaces for visualizing the traffic flow and signal information stored in simulation outputs (e.g., regional traffic patterns and individual vehicle interactions) and visually comparing multiple simulation outputs both spatially and temporally. Furthermore, the paper presents the conceptual design and implementation of this application, and demonstrates the application’s performance for sharing, comparing, and visualizing simulation outputs from VISSIM and SUMO, two commonly used traffic simulation software programs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

2021 Site Environmental Report (SER) for the Ernest Orlando Lawrence Berkeley National Laboratory (LBNL)

Lawrence Berkeley National Laboratory (LBNL, Berkeley Lab) is a multiprogram scientific facility operated by the University of California (UC) for the U.S. Department of Energy (DOE). Berkeley Lab’s research is focused on the physical, biological, environmental, and computational sciences, with the objective of delivering scientific knowledge and discoveries pertinent to DOE’s mission. This annual report describes environmental protection activities and potential impacts resulting from operations conducted in calendar year 2021, unless otherwise indicated. The format and content of this report satisfy the requirements of both DOE Order 231.1B, Administrative Change 1 (Environment, Safety, and Health Reporting) (DOE, 2012) and the operating contract between UC and DOE (DOE Contract No. DE-AC02-05CH11231, also known as Contract 31).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Transformative Impacts of Laser-Induced Breakdown Spectroscopy on Environmental and Biological Research at Oak Ridge National Laboratory

This manuscript will present an advancement of transformative research that has been conducted at Oak Ridge National Laboratory (ORNL) over a 25-year period (2000–2025) on a variety of environmental and biological matrices. These investigations derived a fundamental understanding of how elemental detection and analysis of these matrices led to the knowledge and discovery of natural processes in plants and the environment. Each project led to the initiation of a new research area which unearthed awesome and novel breakthroughs. Highlights are listed below: 1. The preliminary research at ORNL centered on the detection of aerosols utilizing Laser-induced Breakdown Spectroscopy (LIBS) technology. The Clean Air Act Amendment (CAAA) of 1990 highlighted the importance of identifying hazardous air pollutants (HAPs) due to their impact on environmental and human health, thereby underscoring the need to detect various toxic elements. Research in aerosol chemistry aimed to identify these harmful elements released by factories during periods of increased emissions in their manufacturing processes. LIBS emerged as the most effective method for real-time, in situ measurements of metal species in both gaseous and aerosol phases. 2. An understanding of the presence of total carbon in soils gives perspective on how to develop carbon sequestration strategies. The recognition that carbon sinks can evolve back to carbon sources to emit back to the atmosphere was an important consideration. Also, the concentration of carbon in soil indicates the health of land areas for growing crops successfully. 3. The direct detection of most of the elements in a wood sample in a single emission spectrum, without sample preparation, encouraged the research to use the LIBS technique for preservative treated wood coupled with use of multivariate statistical methodology. Additionally, it encouraged the researchers to try to differentiate natural woods from different parts of the country, and it was successfully demonstrated that LIBS coupled with MVA analysis could differentiate wood of different species from each other and of similar species grown in different environments based on their elemental spectra. This was a breakthrough since it revealed a systematic approach to connect elemental scarcity and abundance to either drought or typical rainfall conditions for the hardwood trees grown in specific areas. 4. Furthermore, the research progressed to reveal physiological and developmental processes contributing to biomass production such that the variation in leaf elemental composition increases our understanding of terrestrial nutrient cycles, as well as tracking the transfer of toxic elements from soils to living organisms. 5. Recently another breakthrough viz., ionomics initiated the correlation of elements to specific genes, uncovering the function that the element performed in the plant. More recently, this has been extended from plants to fungi as well as fungi growing in symbiotic relations with plants.

09 BIOMASS FUELS↗
NASA NTRS2005

Review of high-throughput techniques for detecting solid phase Transformation from material libraries produced by combinatorial methods

High-throughput measurement techniques are reviewed for solid phase transformation from materials produced by combinatorial methods, which are highly efficient concepts to fabricate large variety of material libraries with different compositional gradients on a single wafer. Combinatorial methods hold high potential for reducing the time and costs associated with the development of new materials, as compared to time-consuming and labor-intensive conventional methods that test large batches of material, one- composition at a time. These high-throughput techniques can be automated to rapidly capture and analyze data, using the entire material library on a single wafer, thereby accelerating the pace of materials discovery and knowledge generation for solid phase transformations. The review covers experimental techniques that are applicable to inorganic materials such as shape memory alloys, graded materials, metal hydrides, ferric materials, semiconductors and industrial alloys.

Lee, Jonathan A.↗
NASA NTRS1995

Fast Spatio-Temporal Data Mining from Large Geophysical Datasets

Use of the UCLA CONQUEST (CONtent-based Querying in Space and Time) is reviewed for performance of automatic cyclone extraction and detection of spatio-temporal blocking conditions on MPP. CONQUEST is a data analysis environment for knowledge and data mining to aid in high-resolution modeling of climate modeling.

knowledge discovery data mining climate modeling c↗
NASA NTRS2017

Marshall Space Flight Center Research and Technology Report 2016

Marshall Space Flight Center is essential to human space exploration and our work is a catalyst for ongoing technological development. As we address the challenges facing human deep space exploration, we advance new technologies and applications here on Earth, expand scientific knowledge and discovery, create new economic opportunities, and continue to lead global space exploration.

R&T Report 2016↗
NASA NTRS2020

Development of VBA Tool for Document Term Search

Employees throughout different agencies such as NASA, have identified that the search of determined terms/words through documents, consume substantial research time of such. These types of searches are substantially limited towards one word in a one document identification; forward one, these usual types of searches lack efficiency & optimization through research aspects of work. Consequently, this reflects in the decrease productivity during work hours etc. The application of VBA (Visual Basic for Applications) is the programming language of Excel, which was conducted for the development of optimized tool for document term search. The project enables the search of single & multiple word/term search through single format documents for paragraph data extraction.

Ssytems Development↗
DOE OSTI2022

Ontologies: The Gateway to Knowledge-Enabled Information Services at Los Alamos National Laboratory

At LANL, we use ontologies to capture and maintain essential organizational knowledge and support tools and frameworks for information discovery. Ontologies capture knowledge, creating meaningful structures for finding and interpreting information. Information in a human context that provides meaning and data that is organized and communicated. This report provides information about LANL's ontology efforts and challenges.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗