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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

An agentic artificially intelligent X-ray scientist

Executing experimental tasks in both normal research laboratories and large-scale scientific facilities often requires extensive human supervision and remains a key challenge on the path to fully autonomous, artificial intelligence (AI)-driven science. Here we demonstrate a large language model-driven agent that autonomously performs X-ray sample alignment on a synchrotron beamline by planning actions, executing instrumental commands, interpreting observations and iterating towards experimental goals. Based on existing large language models with structured tool-use via the model context protocol, our AI X-ray scientist was guided and tested using an in-house-built virtual experimental setup that mirrors a six-circle diffractometer at an operational synchrotron beamline. The agentic workflow developed in the virtual environment was directly deployed on a real beamline, where it correctly identified reference reflections and determined the orientation matrix, an essential first step in any type of single-crystal scattering experiment. Our AI X-ray scientist responded effectively to unexpected experimental conditions, demonstrating adaptive problem-solving and readiness for addressing practical experimental situations. Our study provides a step towards autonomous operation across diverse experimental environments at large-scale scattering facilities.

Chen, Zhantao (ORCID:0000000319543868)↗
NASA NTRS1973

Ground Operations Aerospace Language (GOAL). Volume 3: Data bank

The GOAL (Ground Operations Aerospace Language) test programming language was developed for use in ground checkout operations in a space vehicle launch environment. To insure compatibility with a maximum number of applications, a systematic and error-free method of referencing command/response (analog and digital) hardware measurements is a principle feature of the language. Central to the concept of requiring the test language to be independent of launch complex equipment and terminology is that of addressing measurements via symbolic names that have meaning directly in the hardware units being tested. To form the link from test program through test system interfaces to the units being tested the concept of a data bank has been introduced. The data bank is actually a large cross-reference table that provides pertinent hardware data such as interface unit addresses, data bus routings, or any other system values required to locate and access measurements.

Source record↗
NASA NTRS2014

Monitoring with Data Automata

We present a form of automaton, referred to as data automata, suited for monitoring sequences of data-carrying events, for example emitted by an executing software system. This form of automata allows states to be parameterized with data, forming named records, which are stored in an efficiently indexed data structure, a form of database. This very explicit approach differs from other automaton-based monitoring approaches. Data automata are also characterized by allowing transition conditions to refer to other parameterized states, and by allowing transitions sequences. The presented automaton concept is inspired by rule-based systems, especially the Rete algorithm, which is one of the well-established algorithms for executing rule-based systems. We present an optimized external DSL for data automata, as well as a comparable unoptimized internal DSL (API) in the Scala programming language, in order to compare the two solutions. An evaluation compares these two solutions to several other monitoring systems.

log analysis↗
DOE OSTI2025

RAG for FLAG: AI Assistance for a Physics Code

Artificial intelligence (AI) has quickly become an important tool in scientific research, where significant efforts are underway to develop tools that will expedite the research process. One area of particular impact is scientific software, which can be particularly complex, and therefore time consuming to learn and use effectively. AI assistants are increasingly helping to streamline the process by performing tasks such as interactively answering user questions or suggesting solutions. Los Alamos National Laboratory (LANL) develops several advanced scientific codes, such as FLAG, which can be used to run multiphysics simulations. With this study, our goal was to develop an AI assistant for FLAG that could help make the process of understanding the software and running physics simulations more efficient. To develop an AI assistant for FLAG, we used a method called retrieval-augmented generation (RAG), which is a technique that uses information from relevant data sources to enhance the accuracy of large language models (LLMs). We used the FLAG user manual and other FLAG documentation as the knowledge base for the RAG system. When a user provides a query, RAG retrieves relevant sections from the knowledge base in response, then uses those excerpts to generate grounded and contextually rich answers. We found that our AI assistant was able to provide context aware answers and source references to user queries. To evaluate performance, we developed a set of 40 benchmark questions and compared the accuracy of the responses to those of two standard LLMs without retrieval. Our AI assistant significantly outperformed the standard LLMs at answering FLAG-related questions, with an 82.5% accuracy rate, compared to 47.5% for both of the standard LLMs. This has the potential to make the process of learning and using FLAG much easier, especially for new users. Ultimately, it supports LANL’s broader mission by empowering scientists and engineers to focus more on discovery and analysis rather than on navigating complex software systems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1988

VAPEPS user's reference manual, version 5.0

This is the reference manual for the VibroAcoustic Payload Environment Prediction System (VAPEPS). The system consists of a computer program and a vibroacoustic database. The purpose of the system is to collect measurements of vibroacoustic data taken from flight events and ground tests, and to retrieve this data and provide a means of using the data to predict future payload environments. This manual describes the operating language of the program. Topics covered include database commands, Statistical Energy Analysis (SEA) prediction commands, stress prediction command, and general computational commands.

Park, D. M.↗
NASA NTRS2007

Advanced Reference Counting Pointers for Better Performance

A computer program implements reference counting pointers (RCPs) that are lock-free, thread-safe, async-safe, and operational on a multiprocessor computer. RCPs are powerful and convenient means of managing heap memory in C++ software. Most prior RCP programs use locks to ensure thread safety and manage concurrency. The present program was developed in a continuing effort to explore ways of using the C++ programming language to develop safety-critical and mission- critical software. This effort includes exploration of lock-free algorithms because they offer potential to avoid some costly and difficult verification problems. Unlike previously published RCP software, the present program does not use locks (meaning that no thread can block progress on another thread): Instead, this program implements algorithms that exploit capabilities of central-processing- unit hardware so as to avoid locks. Once locks are eliminated, it becomes possible to realize the other attributes mentioned in the first sentence. In addition to the abovementioned attributes, this program offers several advantages over other RCP programs that use locks: It is smaller (and, hence, is faster and uses less memory), it is immune to priority inversion, and there is no way for it to cause a C++ exception.

Reinholtz, William↗
DOE OSTI2025

System Engineers and Decisions: It?s All about Knowledge

In order to guarantee that a system meets adequate levels of reliability and availability, system performances are continuously monitored and analyzed thanks to the technological advancements driving the Industry 4.0 revolution. An Industry 4.0 approach is typically based on advanced statistical, big data mining, machine learning, and internet-of-things methods designed to detect anomalies in the behavior of system, detect the most likely failure modes, and provide indications to system engineers on when maintenance activities should be performed before system performance are deemed unacceptable (which can be generated by diagnostic and prognostic methods). However, these analyses, which are designed to automatize and increase the efficacy of the system maintenance program, require large amount of data which can come in various forms: numeric, textual, images, sounds etc. Such data constitutes the historic knowledge benchmark to track system performances and support system engineer decisions. Here we claim that data is not sufficient to support this kind of analyses when applied to systems characterized by complex architectures and behaviors. Robust system engineer decisions require the ability to understand the system operational context that lies behind the observed data elements. In this respect, system models are in fact necessary to “put data in context” and capture relationships between data elements. Industry 4.0 methods require in fact contextual knowledge as a basis upon which hypotheses can be generated and assumptions tested. In our view, for complex systems, model-based system engineering (MBSE) models can afford this contextual knowledge, as they are typically used to describe systems architecture and dynamic behaviors. System knowledge is here intended as the blending of collected data and system architecture which takes the form of a “knowledge graph”. A knowledge graph is a database which consists of a large set of nodes (in our case an entity can be either a data or an MBSE element) which are linked to each other. The types of nodes and links follow a pre-defined topology, sometimes also refers as an ontology, that is designed to fit the actual decisions that needs to be performed. We show here how a knowledge graph can be defined to support system engineer maintenance decisions and how the same graph can be built based on system MBSE models and pre-processed data from numeric (through anomaly detections and diagnostic methods) and textual elements (through technical language processing TLP).

97 - MATHEMATICS AND COMPUTING↗
NASA NTRS1999

Design of a Formation of Earth-Orbiting Satellites: The Auroral Lites Mission

The National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) has proposed a set of spacecraft flying in close formation around the Earth in order to measure the behavior of the auroras. The mission, named Auroral Lites, consists of four spacecraft configured to start at the vertices of a tetrahedron, flying over three mission phases. During the first phase, the distance between any two spacecraft in the formation is targeted at 10 kilometers (km). The second mission phase is much tighter, requiring satellite interrange spacing targeted at 500 meters. During the final phase of the mission, the formation opens to a nominal 100-km interrange spacing. In this paper, we present the strategy employed to initialize and model such a close formation during each of these phases. The analysis performed to date provides the design and characteristics of the reference orbit, the evolution of the formation during Phases I and II, and an estimate of the total mission delta-V budget. AI Solutions' mission design tool, FreeFlyer, was used to generate each of these analysis elements. The tool contains full force models, including both impulsive and finite duration maneuvers. Orbital maintenance can be fully modeled in the system using a flexible, natural scripting language built into the system. In addition, AI Solutions is in the process of adding formation extensions to the system facilitating mission analysis for formations like Auroral Lites. We will discuss how FreeFlyer is used for these analyses.

Hametz, Mark E.↗
NASA NTRS1999

Design of a Formation of Earth Orbiting Satellites: The Auroral Lites Mission

The National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) has proposed a set of spacecraft flying in close formation around the Earth in order to measure the behavior of the auroras. The mission, named Auroral Lites, consists of four spacecraft configured to start at the vertices of a tetrahedron, flying over three mission phases. During the first phase, the distance between any two spacecraft in the formation is targeted at 10 kilometers (km). The second mission phase is much tighter, requiring satellite interrange spacing targeted at 500 meters. During the final phase of the mission, the formation opens to a nominal 100-km interrange spacing. In this paper, we present the strategy employed to initialize and model such a close formation during each of these phases. The analysis performed to date provides the design and characteristics of the reference orbit, the evolution of the formation during Phases I and II, and an estimate of the total mission delta-V budget. AI Solutions' mission design tool, FreeFlyer(R), was used to generate each of these analysis elements. The tool contains full force models, including both impulsive and finite duration maneuvers. Orbital maintenance can be fully modeled in the system using a flexible, natural scripting language built into the system. In addition, AI Solutions is in the process of adding formation extensions to the system facilitating mission analysis for formations like Auroral Lites. We will discuss how FreeFlyer(R) is used for these analyses.

Hametz, Mark E.↗
DOE OSTI2024

Automating the Analysis of Large Language Models Responses through Zero-Shot Question Answering

Recent advancements in Large Language Models (LLMs) have shown significant potential in various applications, yet their evaluation, particularly in zero-shot question answering scenarios, remains a challenging task. In this study, our objective was to explore precision metrics for Large Language Models (LLM) and design and implement a software pipeline to automatically evaluate LLMs' outputs under zero-shot question answering. Zero-shot question answering involves a model providing answers to questions about topics it hasn't seen during training. It leverages the principles of zero-shot learning by relying on semantic understanding and generalization from related knowledge. The data used was metadata from medical databases on congenital heart disease. We explored eleven LLM metrics and selected three for our evaluation: BLEU, BERTScore, and MoverScore. BLEU calculates a score based on the overlap of n-grams (contiguous sequences of n items, typically words) between the machine-generated translation and the reference translations. Higher BLEU scores indicate better correspondence between the machine-generated and human-generated translations. BERTScore is a metric used to evaluate the quality of machine-generated text by measuring the similarity of token embeddings produced by BERT (Bidirectional Encoder Representations from Transformers) between the generated text and reference text. MoverScore is a metric that quantifies the dissimilarity between the distributions of word embeddings from machine-generated text and reference text, emphasizing semantic similarity over exact token overlap. We also introduced HBKI, a composite metric summarizing these approaches. We tested five models —GPT-3, Llama-2, Gemini 1.5 Pro, Solar 10.7B, and Mixtral-8x7b. Our software pipeline, designed and implemented using Object-Oriented Programming principles, allows users to customize the selection and extraction of features for topics of interest in their own research. Our results show that MoverScore delivered the most precise evaluation of the LLM's outputs, while Mixtral-8x7b achieved the best overall performance in extracting metadata from the databases.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Compass Concurrent Engineering Lessons Learned in Remote and Hybrid Environments

The Compass Team at NASA’s Glenn Research Center (GRC) is a concurrent engineering team which specializes in conceptual spacecraft mission designs. Detailed descriptions of the team, its history, and its operating model can be found in [1] and [2]. During the COVID-19 pandemic, the team was required to move to remote (virtual) operation from their in-person model for approximately 22 months. As the restrictions began to lift and team members were able to return in-person to the Compass Lab, the team moved into a hybrid mode of operation, with some participants still tying in remotely some or all of the time. This paper discusses many of the lessons learned from these experiences, highlighting improvements, outstanding challenges and the tools and methodologies used to address both. Throughout this discussion the terms “in-person”, “hybrid”, “virtual” and “remote” will be used. For the purposes of this paper, “in-person” will be understood to mean when team members are interacting simultaneously, physically within the Compass Lab. “Remote” or “virtual” will refer to when interactions are happening between people who are not co-located using only technology to interface. “Hybrid” will refer to when two or more participants are physically located in the Compass Lab and one or more participant(s) is participating remotely. Media richness is described as “a medium’s ability to communicate effectively based of four factors. They are the capacity for immediate feedback, the number of cues and channels it utilizes, the degree of personalization it affords, and its ability to communicate using natural language” [3]. This theory will be referenced and discussed in multiple of the following sections due to its relevance when choosing how to operate in remote and hybrid modes, as well as in making tool selections. The key to selecting the best mode of communication lays in how complicated the discussion is and the level of ambiguity involved. Not all conversations or interactions require media rich mediums. For example, providing information about which there is little to no ambiguity can easily be done in less rich methods - such as email. A conversation including high levels of ambiguity and/or complex information is better suited to a richer medium, such as in-person or a video call with shared screens.

Concurrent Engineering↗
DOE OSTI2021

Models and Processes to Extract Drug-like Molecules From Natural Language Text

Researchers worldwide are seeking to repurpose existing drugs or discover new drugs to counter the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A promising source of candidates for such studies is molecules that have been reported in the scientific literature to be drug-like in the context of viral research. However, this literature is too large for human review and features unusual vocabularies for which existing named entity recognition (NER) models are ineffective. We report here on a project that leverages both human and artificial intelligence to detect references to such molecules in free text. We present 1) a iterative model-in-the-loop method that makes judicious use of scarce human expertise in generating training data for a NER model, and 2) the application and evaluation of this method to the problem of identifying drug-like molecules in the COVID-19 Open Research Dataset Challenge (CORD-19) corpus of 198,875 papers. We show that by repeatedly presenting human labelers only with samples for which an evolving NER model is uncertain, our human-machine hybrid pipeline requires only modest amounts of non-expert human labeling time (tens of hours to label 1778 samples) to generate an NER model with an F-1 score of 80.5%—on par with that of non-expert humans—and when applied to CORD’19, identifies 10,912 putative drug-like molecules. This enriched the computational screening team’s targets by 3,591 molecules, of which 18 ranked in the top 0.1% of all 6.6 million molecules screened for docking against the 3CLPro protein.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Publication and Retrieval of Computational Chemical-Physical Data Via the Semantic Web. Final Technical Report

This research showed the feasibility of applying the concepts of the Semantic Web to Computation Chemistry. We have created the first web portal (www.chemsem.com) that allows data created in the calculations of quantum chemistry, and other such chemistry calculations to be placed on the web in a way that makes the data accessible to scientists in a semantic form never before possible. The semantic web nature of the portal allows data to be searched, found, and used as an advance over the usual approach of a relational database. The semantic data on our portal has the nature of a Giant Global Graph (GGG) that can be easily merged with related data and searched globally via a SPARQL Protocol and RDF Query Language (SPARQL) that makes global searches for data easier than with traditional methods. Our Semantic Web Portal requires that the data be understood by a computer and hence defined by an ontology (vocabulary). This ontology is used by the computer in understanding the data. We have created such an ontology for computational chemistry (purl.org/gc) that encapsulates a broad knowledge of the field of computational chemistry. We refer to this ontology as the Gainesville Core. While it is perhaps the first ontology for computational chemistry and is used by our portal, it is only a start of what must be a long multi-partner effort to define computational chemistry. In conjunction with the above efforts we have defined a new potential file standard (Common Standard for eXchange – CSX for computational chemistry data). This CSX file is the precursor of data in the Resource Description Framework (RDF) form that the semantic web requires. Our portal translates CSX files (as well as other computational chemistry data files) into RDF files that are part of the graph database that the semantic web employs. We propose a CSX file as a convenient way to encapsulate computational chemistry data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1984

Fundamental Equations of the LIF Hydroxyl Measurements, Appendix a

The equation relating OH concentration to the observed detector count rate for a given laser flux is defined. That equation, which will be referred to henceforth as the central equation for absolute sensitivity, will be cast in terms of standard spectroscopic quantities such as oscillator strengths, Hoenl-London factors, rotational and vibrational quantum numbers, etc., such that it may be tested directly under laboratory conditions. The working expressions used by each of the OH laser induced fluorescence (LIF) groups will be related to this central equation, thereby establishing a common language for the comparison of experimental results and the definition of LIF system performance. The relevant structural details of the OH radical via a spectroscopic description of the energy levels and transitions employed in the LIF method are detailed. The central equation relating observed count rate to absolute concentration will be derived, and that equation will be related to the observed quantities cited by each of the experimental groups engaged in the measurement of OH in the Earth's atmosphere.

Source record↗
DOE OSTI2025

An Exploratory Data Mining Investigation for Constructing a Publicly Sourced Dataset of Foreign Hypersonic Tests

This document details a data mining exercise that resulted in an exploratory dataset of publicly reported foreign (non-US) hypersonic vehicle test events. Using a combination of targeted English language searches and country-specific queries, the study aggregates information from digital news media, official press releases, and social media posts. The resulting list of events captures the publicly available accounts of foreign hypersonic tests, although it does not represent an exhaustive record. Limitations such as inconsistent reporting, translation challenges, and the inherently provisional nature of open-source data are acknowledged. This dataset serves as an initial reference point for further inquiries into high-speed atmospheric phenomena and may facilitate future efforts to correlate these events with geophysical measurements.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Collection And Analysis Of Telemetry For The Cyote Heuristic

CATCH CLI focuses on gathering telemetry data, storing it in the Neo4j database, querying for Mitre ATT&CK patterns, and creating STIX 2.1 reports. Key Components: Analysis Modules: Analyze data to detect attack patterns. GoSTOTS Collection Engines: Collect telemetry data. These tools can be used together or individually. Analysis modules rely on data from specific engines to identify attack patterns. Source Code Organization: Engines: CATCH/catch/cmd/collection Modules: CATCH/catch/cmd/analysis CGUI Overview CATCH Graphical User Interface (CGUI) offers a graphical shell to execute CATCH CLI, allowing easy editing of: Analysis Modules Database configurations Profiles (collection and device settings) Neo4j Overview Neo4j is a graph database using the Cypher query language, storing data in JSON. It seamlessly integrates with STIX 2.1 data for: Data Submission: CATCH Collection Engines Data Querying: Analysis Modules CATCH modifies STIX 2.1 data for Neo4j submission and reverts it back during querying. STIG Overview Structured Threat Intelligence Graph (STIG) is a tool for creating, editing, querying, analyzing, and visualizing threat intelligence using STIX 2.1 and storing data in Neo4j. Usage Tools can be run: Manually (CLI): Refer to CATCH documentation User Interface: Run ./cgui/CGUI or go run ./cgui/ Additional Information Logging System: Detailed in the config documentation Further Documentation: Available for CATCH and CGUI

Madsen, MichaelJ. [Idaho National Laboratory (INL)↗
NASA NTRS1987

User's guide to programming fault injection and data acquisition in the SIFT environment

Described are the features, command language, and functional design of the SIFT (Software Implemented Fault Tolerance) fault injection and data acquisition interface software. The document is also intended to assist and guide the SIFT user in defining, developing, and executing SIFT fault injection experiments and the subsequent collection and reduction of that fault injection data. It is also intended to be used in conjunction with the SIFT User's Guide (NASA Technical Memorandum 86289) for reference to SIFT system commands, procedures and functions, and overall guidance in SIFT system programming.

Elks, Carl R.↗
NASA NTRS2010

Time-Tag Generation Script

Time-Tag Generation Script (TTaGS) is an application program, written in the AWK scripting language, for generating commands for aiming one Ku-band antenna and two S-band antennas for communicating with spacecraft. TTaGS saves between 2 and 4 person-hours per every 24 hours by automating the repetitious process of building between 150 and 180 antenna-control commands. TTaGS reads a text database of communication satellite schedules and a text database of satellite rise and set times and cross-references items in the two databases. It then compares the scheduled start and stop with the geometric rise and set to compute the times to execute antenna control commands. While so doing, TTaGS determines whether to generate commands for guidance, navigation, and control computers to tell them which satellites to track. To help prevent Ku-band irradiation of the Earth, TTaGS accepts input from the user about horizon tolerance and accordingly restricts activation and effects deactivation of the transmitter. TTaGS can be modified easily to enable tracking of additional satellites and for such other tasks as reading Sun-rise/set tables to generate commands to point the solar photovoltaic arrays of the International Space Station at the Sun.

Jackson, Dan E.↗