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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Ontological Modeling for Integrated Spacecraft Analysis

Current spacecraft work as a cooperative group of a number of subsystems. Each of these requiresmodeling software for development, testing, and prediction. It is the goal of my team to create anoverarching software architecture called the Integrated Spacecraft Analysis (ISCA) to aid in deploying the discrete subsystems' models. Such a plan has been attempted in the past, and has failed due to the excessive scope of the project. Our goal in this version of ISCA is to use new resources to reduce the scope of the project, including using ontological models to help link the internal interfaces of subsystems' models with the ISCA architecture.I have created an ontology of functions specific to the modeling system of the navigation system of a spacecraft. The resulting ontology not only links, at an architectural level, language specificinstantiations of the modeling system's code, but also is web-viewable and can act as a documentation standard. This ontology is proof of the concept that ontological modeling can aid in the integration necessary for ISCA to work, and can act as the prototype for future ISCA ontologies.

ontological modeling↗
NASA NTRSDate not supplied

Developing and Testing a Common Space Systems Ontology using the Ontological Modeling Language

This paper describes the development and testing of the initial version of a common space systems ontology (CoSSO) for use by the Advanced Concepts Office (ACO) at NASA's Marshall Space Flight Center. The ontology provides a shared conceptualization of concepts of interest to the ACO for modeling aerospace systems concepts in a pre-phase A context to aid with the transition to a more model-based paradigm. The ontological concepts and relations, as well as the anticipated use cases, were developed through interactions with the relevant subject matter experts at the ACO and implemented in the Ontological Modeling Language (OML). The ontology builds on the Basic Formal Ontology (BFO) and the Common Core Ontologies (CCO). While most of the ontology is still in the initial stages, an Environmental Control and Life Support System (ECLSS) ontology is being built on top of the main CoSSO and heavily developed as a proof of concept. The ECLSS ontology is designed with different use cases in mind, namely predicting and diagnosing errors in ECLS systems on long-duration missions, with a focus on the Four-Bed CO$_2$ carbon dioxide scrubber currently on board the ISS. The ECLSS ontology is being developed in a similar manner to the CoSSO, and designed to be compatible with it. The current state of both ontologies is presented and discussed, along with plans for future development and testing.

Conceptual Design↗
NASA NTRS2015

Ontology and modeling patterns for state-based behavior representation

This paper provides an approach to capture state-based behavior of elements, that is, the specification of their state evolution in time, and the interactions amongst them. Elements can be components (e.g., sensors, actuators) or environments, and are characterized by state variables that vary with time. The behaviors of these elements, as well as interactions among them are represented through constraints on state variables. This paper discusses the concepts and relationships introduced in this behavior ontology, and the modeling patterns associated with it. Two example cases are provided to illustrate their usage, as well as to demonstrate the flexibility and scalability of the behavior ontology: a simple flashlight electrical model and a more complex spacecraft model involving instruments, power and data behaviors. Finally, an implementation in a SysML profile is provided.

state variable↗
NASA NTRS2008

An Ontology-Based Archive Information Model for the Planetary Science Community

The Planetary Data System (PDS) information model is a mature but complex model that has been used to capture over 30 years of planetary science data for the PDS archive. As the de-facto information model for the planetary science data archive, it is being adopted by the International Planetary Data Alliance (IPDA) as their archive data standard. However, after seventeen years of evolutionary change the model needs refinement. First a formal specification is needed to explicitly capture the model in a commonly accepted data engineering notation. Second, the core and essential elements of the model need to be identified to help simplify the overall archive process. A team of PDS technical staff members have captured the PDS information model in an ontology modeling tool. Using the resulting knowledge-base, work continues to identify the core elements, identify problems and issues, and then test proposed modifications to the model. The final deliverables of this work will include specifications for the next generation PDS information model and the initial set of IPDA archive data standards. Having the information model captured in an ontology modeling tool also makes the model suitable for use by Semantic Web applications.

Information Model↗
DOE OSTI2025

Floating Wind Array Ontology and Modeling Framework

While there are many tools for designing and modeling a single floating turbine, array level design and modeling has much more to consider. Designing floating wind arrays requires a coupled approach considering many variables, from bathymetry to installation and maintenance to failure and risk analysis. With all of these considerations, an array-level modeling tool is needed to quickly evaluate array designs. The Floating Array Model (FAModel) tool developed at the National Renewable Energy Laboratory was created to fill this gap in low-fidelity array modeling. FAModel is a python framework created to streamline holistic low-fidelity floating wind modeling for array-level analysis. FAModel integrates site data and models with a variety of open-source modeling tools developed by NREL, including FLORIS, RAFT, MoorPy, and anchor capacity models. The integration of these tools allows users to quickly and holistically design an array by considering forces, area analysis, visualization, annual energy production, failure modeling, and component costs.

17 WIND ENERGY↗
NASA NTRS2018

Definition of Modeling vs. Programming Languages

Modeling languages (like UML and SysML) are those used in modelbased specification of software-intensive systems. Like programming languages, they are defined using their syntax and semantics. However, both kinds of languages are defined by different communities, and in response to different requirements, which makes their methodologies and tools different. In this paper, we highlight the main differences between the definition methodologies of modeling and programming languages. We also discuss the impact of these differences on language tool support. We illustrate our ideas using examples from known programming and modeling languages. We also present a case study, where we analyze the definition of a new modeling language called the Ontology Modeling Language (OML). We highlight the requirements that have driven OML definition and explain how they are different from those driving typical programming languages. Finally, we discuss how these differences are being abstracted away using new language definition tools.

Elaasar, Maged↗
NASA NTRS2009

Scientific Digital Libraries, Interoperability, and Ontologies

Scientific digital libraries serve complex and evolving research communities. Justifications for the development of scientific digital libraries include the desire to preserve science data and the promises of information interconnectedness, correlative science, and system interoperability. Shared ontologies are fundamental to fulfilling these promises. We present a tool framework, some informal principles, and several case studies where shared ontologies are used to guide the implementation of scientific digital libraries. The tool framework, based on an ontology modeling tool, was configured to develop, manage, and keep shared ontologies relevant within changing domains and to promote the interoperability, interconnectedness, and correlation desired by scientists.

Information Model↗
DOE OSTI2025

Chemical classification program synthesis using generative artificial intelligence

Accurately classifying chemical structures is essential for cheminformatics and bioinformatics, including tasks such as identifying bioactive compounds of interest, screening molecules for toxicity to humans, finding non-organic compounds with desirable material properties, or organizing large chemical libraries for drug discovery or environmental monitoring. However, manual classification is labor-intensive and difficult to scale to large chemical databases. Existing automated approaches either rely on manually constructed classification rules, or are deep learning methods that lack explainability. This work presents an approach that uses generative artificial intelligence to automatically write chemical classifier programs for classes in the Chemical Entities of Biological Interest (ChEBI) database. These programs can be used for efficient deterministic run-time classification of SMILES structures, with natural language explanations. The programs themselves constitute an explainable computable ontological model of chemical class nomenclature, which we call the ChEBI Chemical Class Program Ontology (C3PO). We validated our approach against the ChEBI database, and compared our results against deep learning models and a naive SMARTS pattern based classifier. C3PO outperforms the naive classifier, but does not reach the performance of state of the art deep learning methods. However, C3PO has a number of strengths that complement deep learning methods, including explainability and reduced data dependence. C3PO can be used alongside deep learning classifiers to provide an explanation of the classification, where both methods agree. The programs can be used as part of the ontology development process, and iteratively refined by expert human curators.

Artificial Intelligence↗
NASA NTRS2007

Defining the Core Archive Data Standards of the International Planetary Data Alliance (IPDA)

A goal of the International Planetary Data Alliance (lPDA) is to develop a set of archive data standards that enable the sharing of scientific data across international agencies and missions. To help achieve this goal, the IPDA steering committee initiated a six month proj ect to write requirements for and draft an information model based on the Planetary Data System (PDS) archive data standards. The project had a special emphasis on data formats. A set of use case scenarios were first developed from which a set of requirements were derived for the IPDA archive data standards. The special emphasis on data formats was addressed by identifying data formats that have been used by PDS nodes and other agencies in the creation of successful data sets for the Planetary Data System (PDS). The dependency of the IPDA information model on the PDS archive standards required the compilation of a formal specification of the archive standards currently in use by the PDS. An ontology modelling tool was chosen to capture the information model from various sources including the Planetary Science Data Dictionary [I] and the PDS Standards Reference [2]. Exports of the modelling information from the tool database were used to produce the information model document using an object-oriented notation for presenting the model. The tool exports can also be used for software development and are directly accessible by semantic web applications.

ontology↗
NASA NTRS1990

Qualitative models for space system engineering

The objectives of this project were: (1) to investigate the implications of qualitative modeling techniques for problems arising in the monitoring, diagnosis, and design of Space Station subsystems and procedures; (2) to identify the issues involved in using qualitative models to enhance and automate engineering functions. These issues include representing operational criteria, fault models, alternate ontologies, and modeling continuous signals at a functional level of description; and (3) to develop a prototype collection of qualitative models for fluid and thermal systems commonly found in Space Station subsystems. Potential applications of qualitative modeling to space-systems engineering, including the notion of intelligent computer-aided engineering are summarized. Emphasis is given to determining which systems of the proposed Space Station provide the most leverage for study, given the current state of the art. Progress on using qualitative models, including development of the molecular collection ontology for reasoning about fluids, the interaction of qualitative and quantitative knowledge in analyzing thermodynamic cycles, and an experiment on building a natural language interface to qualitative reasoning is reported. Finally, some recommendations are made for future research.

Forbus, Kenneth D.↗
NASA NTRS2021

An Ontology-Based Virtual Orrery

Tutorials in this technical memorandum explain how to use the Protégé ontology editor to import data from Excel and export data from Protégé in the JavaScript Object Notation Linked Data (JSON-LD) format then how to transform the JSON-LD file into variables for web apps. These tutorials apply JavaScript and R programming languages and 3D graphics libraries. Ontologies enable standardization, data sharing, and semantic interoperability. An ontology models a domain of knowledge as taxonomies of annotated classes, data properties, relational object properties, and inference rules. This technical memorandum includes a data dictionary for a subset of the Space Situational Awareness Ontology (SSAO).

Ontologies↗
DOE OSTI2025

Extraction and Analysis of Time Series Data from Building Automation Systems Using Large Language Models

Semantic schemas like Haystack 4, Brick and ASHRAE standard 223 enable the structured, standardized, and machine-readable representation of building data, facilitating interoperability, data integration, and advanced analytics. However, extracting information from these models requires specialized expertise in SPARQL and other programming languages, skills that are not commonly found among building professionals. Recent advancements in Large Language Models (LLMs), such as ChatGPT, enable the construction of queries using natural language, making it easier for individuals to interact with these systems in a manner that resembles everyday speech. However, these methods have not yet been tested on building semantic ontologies. This paper introduces a novel workflow and tool for enabling users to ask questions about a specific building's data, using natural language and receive answers automatically generated by GPT-4o. Our approach integrates semantic ontologies with advanced LLM capabilities to automate three critical steps: (1) generating SPARQL queries to retrieve time series references from ontological models, (2) extracting the corresponding time series data from the Building Automation System, and (3) performing computations and visualizations tailored to the user's query. The proposed method simplifies access to BAS data, allowing both domain experts and non-specialists to conduct sophisticated analyses without needing extensive technical knowledge of semantic web technologies. By demonstrating this pipeline, we facilitate more accessible and scalable data-driven decision-making in building operations and management.

Mulayim, Ozan Baris↗
DOE OSTI2025

Extraction and Analysis of Time Series Data from Building Automation Systems Using Large Language Models

Semantic schemas like Haystack 4, Brick and ASHRAE standard 223 enable the structured, standardized, and machine-readable representation of building data, facilitating interoperability, data integration, and advanced analytics. However, extracting information from these models requires specialized expertise in SPARQL and other programming languages, skills that are not commonly found among building professionals. Recent advancements in Large Language Models (LLMs), such as ChatGPT, enable the construction of queries using natural language, making it easier for individuals to interact with these systems in a manner that resembles everyday speech. However, these methods have not yet been tested on building semantic ontologies. This paper introduces a novel workflow and tool for enabling users to ask questions about a specific building's data, using natural language and receive answers automatically generated by GPT-4o. Our approach integrates semantic ontologies with advanced LLM capabilities to automate three critical steps: (1) generating SPARQL queries to retrieve time series references from ontological models, (2) extracting the corresponding time series data from the Building Automation System, and (3) performing computations and visualizations tailored to the user's query. The proposed method simplifies access to BAS data, allowing both domain experts and non-specialists to conduct sophisticated analyses without needing extensive technical knowledge of semantic web technologies. By demonstrating this pipeline, we facilitate more accessible and scalable data-driven decision-making in building operations and management.

Mulayim, Ozan Baris↗
NASA NTRS2005

Modeling and Simulation Verification, Validation and Accreditation (VV&A): A New Undertaking for the Exploration Systems Mission Directorate

and Accreditation (VV&A) session audience, a snapshot review of the Exploration Space Mission Directorate s (ESMD) investigation into implementation of a modeling and simulation (M&S) VV&A program. The presentation provides some legacy ESMD reference material, including information on the then-current organizational structure, and M&S (Simulation Based Acquisition (SBA)) focus contained therein, to provide a context for the proposed M&S VV&A approach. This reference material briefly highlights the SBA goals and objectives, and outlines FY05 M&S development and implementation consistent with the Subjective Assessment, Constructive Assessment, Operator-in-the-Loop Assessment, Hardware-in-the-Loop Assessment, and In Service Operations Assessment M&S construct, the NASA Exploration Information Ontology Model (NExIOM) data model, and integration with the Windchill-based Integrated Collaborative Environment (ICE). The presentation then addresses the ESMD team s initial conclusions regarding an M&S VV&A program, summarizes the general VV&A implementation approach anticipated, and outlines some of the recognized VV&A program challenges, all within a broader context of the overarching Integrated Modeling and Simulation (IM&S) environment at both the ESMD and Agency (NASA) levels. The presentation concludes with a status on the current M&S organization s progress to date relative to the recommended IM&S implementation activity. The overall presentation was focused to provide, for the Verification, Validation,

Prill, Mark E.↗