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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 577 records · Page 32
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

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) concept 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 requirements 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↗
DOE OSTI2010

Experimental Discovery of Topological Insulators and Related Superconductors

Most quantum states of condensed matter are categorized by the symmetries they break. The remarkable discovery of charge Quantum Hall effects (1980s) revealed that there exists an organizational principle of matter based only on the topological distinctions, but in the presence of time-reversal symmetry breaking. In the past few years, theoretical developments suggest that new classes of topological states of matter might exist that are purely topological in nature in the sense that they do not break time-reversal symmetry, and hence can be realized without any applied magnetic field: "Quantum Hall-like effects without Magnetic Fields." This talk describes our discovery of new topologically ordered states of matter (topological insulators) and discusses the unusual electro-magnetic, spin, and superconducting properties this novel phase of quantum matter might exhibit and their potential applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2010

Continuous Time Group Discovery in Dynamic Graphs

With the rise in availability and importance of graphs and networks, it has become increasingly important to have good models to describe their behavior. While much work has focused on modeling static graphs, we focus on group discovery in dynamic graphs. We adapt a dynamic extension of Latent Dirichlet Allocation to this task and demonstrate good performance on two datasets. Modeling relational data has become increasingly important in recent years. Much work has focused on static graphs - that is fixed graphs at a single point in time. Here we focus on the problem of modeling dynamic (i.e. time-evolving) graphs. We propose a scalable Bayesian approach for community discovery in dynamic graphs. Our approach is based on extensions of Latent Dirichlet Allocation (LDA). LDA is a latent variable model for topic modeling in text corpora. It was extended to deal with topic changes in discrete time and later in continuous time. These models were referred to as the discrete Dynamic Topic Model (dDTM) and the continuous Dynamic Topic Model (cDTM), respectively. When adapting these models to graphs, we take our inspiration from LDA-G and SSN-LDA, applications of LDA to static graphs that have been shown to effectively factor out community structure to explain link patterns in graphs. In this paper, we demonstrate how to adapt and apply the cDTM to the task of finding communities in dynamic networks. We use link prediction to measure the quality of the discovered community structure and apply it to two different relational datasets - DBLP author-keyword and CAIDA autonomous systems relationships. We also discuss a parallel implementation of this approach using Hadoop. In Section 2, we review LDA and LDA-G. In Section 3, we review the cDTM and introduce cDTMG, its adaptation to modeling dynamic graphs. We discuss inference for the cDTM-G and details of our parallel implementation in Section 4 and present its performance on two datasets in Section 5 before concluding in Section 6.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

A discovery! The Higgs? Why is this important? How it was done.

Data collected during 2011 and 2012 at the Large Hadron Collider (LHC) at CERN in Switzerland, the world's highest-energy proton collider, has culminated in the discovery of a new particle that is about 135 times heavier than a proton. But is it really the Higgs particle predicted by the theory that explains the origin of the mass of most elementary particles in the universe? The discovery and its possible identity is discussed by two Brookhaven Lab physicists, Sally Dawson and Howard Gordon, with deep roots in the hunt for the Higgs.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2013

Domain-Specific Languages For Developing and Deploying Signature Discovery Workflows

Domain-agnostic Signature Discovery entails scientific investigation across multiple domains through the re-use of existing algorithms into workflows. The existing algorithms may be written in any programming language for various hardware architectures (e.g., desktops, commodity clusters, and specialized parallel hardware platforms). This raises an engineering issue in generating Web services for heterogeneous algorithms so that they can be composed into a scientific workflow environment (e.g., Taverna). In this paper, we present our software tool that defines two simple Domain-Specific Languages (DSLs) to automate these processes: SDL and WDL. Our Service Description Language (SDL) describes key elements of a signature discovery algorithm and generates the service code. The Workflow Description Language (WDL) describes the pipeline of services and generates deployable artifacts for the Taverna workflow management system. We demonstrate our tool with a landscape classification example that is represented by BLAST workflows composed of services that wrap original scripts.

Jacob, Ferosh↗
DOE OSTI2013

The Discovery of the Higgs Boson: America's Role

The discovery of the Higgs boson was an international endeavor, involving thousands of physicists from across the world. While the accelerator at which the experimental work was done is located on Europe, the US supplied more physicists than any other single country. America had a very large role in the discovery of the Higgs particle and continues to have a leading role in the ongoing studies of the boson's properties. This video describes some of the contributions of U.S. universities and laboratories.

43 PARTICLE ACCELERATORS↗
DOE OSTI2014

IMG-ABC: An Atlas of Biosynthetic Gene Clusters to Fuel the Discovery of Novel Secondary Metabolites

In the discovery of secondary metabolites (SMs), large-scale analysis of sequence data is a promising exploration path that remains largely underutilized due to the lack of relevant computational resources. We present IMG-ABC (https://img.jgi.doe.gov/abc/) -- An Atlas of Biosynthetic gene Clusters within the Integrated Microbial Genomes (IMG) system1. IMG-ABC is a rich repository of both validated and predicted biosynthetic clusters (BCs) in cultured isolates, single-cells and metagenomes linked with the SM chemicals they produce and enhanced with focused analysis tools within IMG. The underlying scalable framework enables traversal of phylogenetic dark matter and chemical structure space -- serving as a doorway to a new era in the discovery of novel molecules.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

Plenario: An Open Data Discovery and Exploration Platform for Urban Science

The past decade has seen the widespread release of open data concerning city services, conditions, and activities by government bodies and public institutions of all sizes. Hundreds of open data portals now host thousands of datasets of many different types. These new data sources represent enormous po- tential for improved understanding of urban dynamics and processes—and, ultimately, for more livable, efficient, and prosperous communities. However, those who seek to realize this potential quickly discover that discovering and applying those data relevant to any particular question can be extraordinarily dif- ficult, due to decentralized storage, heterogeneous formats, and poor documentation. In this context, we introduce Plenario, a platform designed to automating time-consuming tasks associated with the discovery, exploration, and application of open city data—and, in so doing, reduce barriers to data use for researchers, policymakers, service providers, journalists, and members of the general public. Key innovations include a geospatial data warehouse that allows data from many sources to be registered into a common spatial and temporal frame; simple and intuitive interfaces that permit rapid discovery and exploration of data subsets pertaining to a particular area and time, regardless of type and source; easy export of such data subsets for further analysis; a user-configurable data ingest framework for automated importing and periodic updating of new datasets into the data warehouse; cloud hosting for elastic scaling and rapid creation of new Plenario instances; and an open source implementation to enable community contributions. We describe here the architecture and implementation of the Plenario platform, discuss lessons learned from its use by several communities, and outline plans for future work.

Catlett, Charlie↗
DOE OSTI2017

Sensitivity and Discovery Potential of CUORE to Neutrinoless Double-Beta Decay

We present a study of the sensitivity and discovery potential of CUORE, a bolometric double-beta decay experiment under construction at the Laboratori Nazionali del Gran Sasso in Italy. Two approaches to the computation of experimental sensitivity for various background scenarios are presented, and an extension of the sensitivity formulation to the discovery potential case is also discussed. Assuming a background rate of 10 -2 cts/(keV kg y), we find that, after 5 years of live time, CUORE has a 1 sigma sensitivity to the neutrinoless double-beta decay half-life of T$0v\atop{1/2}$(1θ) = 1.6 \times 10 26 y and thus a potential to probe the effective Majorana neutrino mass down to 40-100 meV; the sensitivity at 1.64 sigma, which corresponds to 90% C.L., will be T$0v\atop{1/2}$(1.64θ) = 9.5 \times 10 25 y. This range is compared with the claim of observation of neutrinoless double-beta decay in 76 Ge and the preferred range of the neutrino mass parameter space from oscillation results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Uranium Accumulation Discoveries at Y-12 (Paper)

In June 2017, reduction operations at the Y-12 National Security Complex (Y-12) experienced a uranium accumulation event that compromised the nuclear criticality safety (NCS) of reduction operations. This event led to the execution of an extensive Extent-of-Condition (EOC) review aimed at identifying the location of other significant uranium masses in unintended locations. This paper describes the reduction process, the equipment in which unexpected accumulation was discovered, and the timeline of events leading to the discovery. It highlights the multi-disciplinary effort taken by production to recover from the event and resume operations. Lastly, it includes a description of the inconsistencies between operational conditions and Criticality Safety Evaluation (CSE) assumptions and how the EOC led to the discovery of four more accumulations within Y-12 processes.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Uranium Accumulation Discoveries at Y-12 (Presentation)

In June 2017, reduction operations at the Y-12 National Security Complex (Y-12) experienced a uranium accumulation event that compromised the nuclear criticality safety (NCS) of reduction operations. This event led to the execution of an extensive Extent-of-Condition (EOC) review aimed at identifying the location of other significant uranium masses in unintended locations. This paper describes the reduction process, the equipment in which unexpected accumulation was discovered, and the timeline of events leading to the discovery. It highlights the multi-disciplinary effort taken by production to recover from the event and resume operations. Lastly, it includes a description of the inconsistencies between operational conditions and Criticality Safety Evaluation (CSE) assumptions and how the EOC led to the discovery of four more accumulations within Y-12 processes.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Uranium Accumulation Discoveries at Y-12

Since discovering an unexpected accumulation of uranium in the Reduction Area of Building 9212 last June, CNS has been executing an extent-of-condition review that has led to additional discoveries of uranium accumulation. In all instances of accumulation discoveries, CNS has paused operations while executing corrective actions to address identified issues. CNS is aggressively addressing the underlying causes of the accumulations across its facilities and programs, to include expanding and accelerating the scope of extent-of-condition walk-downs and improvement actions.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Preserving the Mound Laboratory Legacy-The Mound Cold War Discovery Center - 19550

The Mound Cold War Discovery Center (MCWDC) opened at the former DOE Mound Laboratory in Miamisburg, Ohio, on April 23, 2018, approximately three years after the US Department of Energy (DOE) Office of Legacy Management, Dayton History, the Mound Science and EnergyMuseum Association (MSEMA), and the Mound Development Corporation signed the Mound Educational Center Memorandum of Agreement (MOA). This MOA designated each party's responsibilities for the facility and laid the groundwork for DOE to enter into a Cooperative Agreement with Dayton History to design, build, and perform initial operations of the educational center. Dayton History is a nonprofit organization that operates several historical facilities in Montgomery County, Ohio. The Mound Development Corporation is the nonprofit organization that operates the Mound Business Park, privately owned property previously associated with the former DOE Mound Laboratory. The US Atomic Energy Commission began operations at the Mound Laboratory in 1948 as an integrated research, development, and production facility that supported the nation's weapons and energy programs. In October 2015, DOE entered into a five-year Cooperative Agreement with Dayton History to preserve, protect, and make accessible legacy records and information about the history of the Mound site through the development of an educational center. Years One and Two were for design and construction, respectively. Years Three through Five were for operation of the new center. The new center, located in a building owned by the Mound Development Corporation, would carry on the mission started by the MSEMA, a nonprofit organization started by former Mound site employees more than 15 years ago after DOE mission work ended at the site. As part of the cooperative agreement, Dayton History is responsible for creating interactive displays and exhibits to draw, entertain, and educate the public on the Mound site's significant contributions to the protection of the nation; developing educational programs; and initiating marketing strategies for the long-term sustainability of the educational center. In 2016, parties to the MOA officially named the facility the Mound Cold War Discovery Center. The MCWDC was designed to preserve the history of the Mound site through effective, entertaining, and efficient communication of the history of the Mound Laboratory; the site's legacy to the US weapons production complex and space exploration program; and promote understanding of what has happened at the site since DOE mission work ended, including environmental cleanup under the Comprehensive Environmental Response, Compensation, and Liability Act and the regulator-approved end state of industrial/commercial land use with associated institutional controls. The cooperative agreement requires Dayton History to develop and implement marketing strategies to support the long-term sustainability of the MCWDC at the Mound site. Dayton History will submit a report after the first year of operation and every year thereafter during the period of the cooperative agreement. These reports will describe and status efforts to obtain sustainability and offer for consideration any operational changes, corrections, or suggestions that may improve the potential for sustainability. Prior to the end of the cooperative agreement, a sustainability assessment decision will be made by DOE to determine whether the MCWDC has enough community interest to financially sustain itself at the Mound site. The status of the MCWDC Legacy Fund established by Dayton History will be considered in DoE's assessment determination. If DOE determines that the MCWDC is not sustainable at the Mound site, the exhibits will be relocated to Carillon Historical Park in Dayton, with the five key exhibits that were predetermined in the MOA to be placed on permanent display, thereby maintaining the legacy of the Mound site. Dayton History is required to submit a final closeout report at the end of Year Five of the cooperative agreement. The report will describe all activities completed under the agreement, provide an assessment of how project goals were achieved or not achieved, and describe how the legacy of the site will be preserved. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Discovery and Spectroscopic Characterization of a Distant, Compact Milky Way Satellite in Gemini

We present the discovery of a compact Milky Way satellite in the constellation of Gemini. This system was discovered by cross-matching detections from two independent search algorithms applied to Blanco/DECam data from the third data release of the DECam Local Volume Exploration survey (DELVE DR3), and confirmed with deeper imaging from Gemini/GMOS-N. Based on these data, we determine that the system is an ultra-faint ($M_V = -2.1^{+0.4}_{-0.6}$), compact ($r_{1/2} = 8.6^{+1.4}_{-1.2}$ pc) system located at a heliocentric distance of $120^{+7}_{-6}$ kpc. These physical properties place the system in the regime of ambiguous, ultra-faint compact Milky Way halo satellites that cannot be confidently classified as dwarf galaxies or star clusters from morphology alone; we therefore name the system DELVE 8/Gemini I. From medium-resolution Keck/DEIMOS spectroscopy, we securely identify four members including two blue horizontal branch stars, confirming the system as a bound satellite moving at a mean radial velocity of $v_{\rm hel} = -82.7^{+3.7}_{-3.9} {\rm km\,s}^{-1}$. We also use these spectra to place an upper limit of $\rm [Fe/H] \lesssim -2.5$ on the metallicity of DELVE 8/Gemini I's brightest star, supporting the classification of the system as either an ancient star cluster or ultra-faint dwarf galaxy. The discovery of faint, distant systems similar to DELVE 8/Gemini I is expected to become more common with upcoming surveys.

Overdeck, K. [Chicago U., Astron. Astrophys. Ctr.;↗
DOE OSTI2010

Coupling Visualization and Data Analysis for Knowledge Discovery from Multi-dimensional Scientific Data

Knowledge discovery from large and complex scientific data is a challenging task. With the ability to measure and simulate more processes at increasingly finer spatial and temporal scales, the growing number of data dimensions and data objects presents tremendous challenges for effective data analysis and data exploration methods and tools. The combination and close integration of methods from scientific visualization, information visualization, automated data analysis, and other enabling technologies"such as efficient data management" supports knowledge discovery from multi-dimensional scientific data. This paper surveys two distinct applications in developmental biology and accelerator physics, illustrating the effectiveness of the described approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Constrained or unconstrained? Neural-network-based equation discovery from data

Throughout many fields, practitioners often rely on differential equations to model systems. Yet, for many applications, the theoretical derivation of such equations and/or the accurate resolution of their solutions may be intractable. Instead, recently developed methods, including those based on parameter estimation, operator subset selection, and neural networks, allow for the data-driven discovery of both ordinary and partial differential equations (PDEs), on a spectrum of interpretability. The success of these strategies is often contingent upon the correct identification of representative equations from noisy observations of state variables and, as importantly and intertwined with that, the mathematical strategies utilized to enforce those equations. Specifically, the latter has been commonly addressed via unconstrained optimization strategies. Representing the PDE as a neural network, we propose to discover the PDE (or the associated operator) by solving a constrained optimization problem and using an intermediate state representation similar to a physics-informed neural network (PINN). The objective function of this constrained optimization problem promotes matching the data, while the constraints require that the discovered PDE is satisfied at a number of spatial collocation points. We present a penalty method and a widely used trust-region barrier method to solve this constrained optimization problem, and we compare these methods on numerical examples. Our results on several example problems demonstrate that the latter constrained method outperforms the penalty method, particularly for higher noise levels or fewer collocation points. This work motivates further exploration into using sophisticated constrained optimization methods in scientific machine learning, as opposed to their commonly used, penalty-method or unconstrained counterparts. For both of these methods, we solve these discovered neural network PDEs with classical methods, such as finite difference methods, as opposed to PINNs-type methods relying on automatic differentiation. Here, we briefly highlight how simultaneously fitting the data while discovering the PDE improves the robustness to noise and other small, yet crucial, implementation details.

Data-driven discovery↗