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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Temporal and contextual knowledge in model-based expert systems

A basic paradigm that allows representation of physical systems with a focus on context and time is presented. Paragon provides the capability to quickly capture an expert's knowledge in a cognitively resonant manner. From that description, Paragon creates a simulation model in LISP, which when executed, verifies that the domain expert did not make any mistakes. The Achille's heel of rule-based systems has been the lack of a systematic methodology for testing, and Paragon's developers are certain that the model-based approach overcomes that problem. The reason this testing is now possible is that software, which is very difficult to test, has in essence been transformed into hardware.

Toth-Fejel, Tihamer↗
DOE OSTI2025

Large Language Model Integration for Knowledge Retrieval and Interaction for the DUNE Experiment

The Deep Underground Neutrino Experiment (DUNE) is a next-generation neutrino experiment that will generate an unprecedented volume of heterogeneous information-from documentation and technical notes to experimental data and reconstruction pipelines. Efficient knowledge retrieval and contextual understanding are increasingly critical for collaboration-wide productivity and onboarding. In this work, we present DUNE-GPT, a prototype framework that leverages large language models (LLMs) and retrieval-augmented generation (RAG) to enable natural-language querying of DUNE's internal documentation and technical resources. The system provides an intelligent interface for DUNE collaborators to interact with experiment-specific knowledge while maintaining data privacy and infrastructure compliance within Fermilab computing resources.

Rafique, A. [Argonne (main)]↗
NASA NTRS1992

Constraint-based component-modeling for knowledge-based design

The paper describes the application of various advanced programming techniques derived from artificial intelligence research to the development of flexible design tools for conceptual design. Special attention is given to two techniques which appear to be readily applicable to such design tools: the constraint propagation technique and the object-oriented programming. The implementation of these techniques in a prototype computer tool, Rubber Airplane, is described.

Kolb, Mark A.↗
NASA NTRS2013

The Impact of Back-Sputtered Carbon on the Accelerator Grid Wear Rates of the NEXT and NSTAR Ion Thrusters

A study was conducted to quantify the impact of back-sputtered carbon on the downstream accelerator grid erosion rates of the NEXT (NASA's Evolutionary Xenon Thruster) Long Duration Test (LDT1). A similar analysis that was conducted for the NSTAR (NASA's Solar Electric Propulsion Technology Applications Readiness Program) Life Demonstration Test (LDT2) was used as a foundation for the analysis developed herein. A new carbon surface coverage model was developed that accounted for multiple carbon adlayers before complete surface coverage is achieved. The resulting model requires knowledge of more model inputs, so they were conservatively estimated using the results of past thin film sputtering studies and particle reflection predictions. In addition, accelerator current densities across the grid were rigorously determined using an ion optics code to determine accelerator current distributions and an algorithm to determine beam current densities along a grid using downstream measurements. The improved analysis was applied to the NSTAR test results for evaluation. The improved analysis demonstrated that the impact of back-sputtered carbon on pit and groove wear rate for the NSTAR LDT2 was negligible throughout most of eroded grid radius. The improved analysis also predicted the accelerator current density for transition from net erosion to net deposition considerably more accurately than the original analysis. The improved analysis was used to estimate the impact of back-sputtered carbon on the accelerator grid pit and groove wear rate of the NEXT Long Duration Test (LDT1). Unlike the NSTAR analysis, the NEXT analysis was more challenging because the thruster was operated for extended durations at various operating conditions and was unavailable for measurements because the test is ongoing. As a result, the NEXT LDT1 estimates presented herein are considered preliminary until the results of future posttest analyses are incorporated. The worst-case impact of carbon back-sputtering was determined to be the full power operating condition, but the maximum impact of back-sputtered carbon was only a four percent reduction in wear rate. As a result, back-sputtered carbon is estimated to have an insignificant impact on the first failure mode of the NEXT LDT at all operating conditions.

Soulas, George C.↗
NASA NTRS2013

The Impact of Back-Sputtered Carbon on the Accelerator Grid Wear Rates of the NEXT and NSTAR Ion Thrusters

A study was conducted to quantify the impact of back-sputtered carbon on the downstream accelerator grid erosion rates of the NASA's Evolutionary Xenon Thruster (NEXT) Long Duration Test (LDT1). A similar analysis that was conducted for the NASA's Solar Electric Propulsion Technology Applications Readiness Program (NSTAR) Life Demonstration Test (LDT2) was used as a foundation for the analysis developed herein. A new carbon surface coverage model was developed that accounted for multiple carbon adlayers before complete surface coverage is achieved. The resulting model requires knowledge of more model inputs, so they were conservatively estimated using the results of past thin film sputtering studies and particle reflection predictions. In addition, accelerator current densities across the grid were rigorously determined using an ion optics code to determine accelerator current distributions and an algorithm to determine beam current densities along a grid using downstream measurements. The improved analysis was applied to the NSTAR test results for evaluation. The improved analysis demonstrated that the impact of back-sputtered carbon on pit and groove wear rate for the NSTAR LDT2 was negligible throughout most of eroded grid radius. The improved analysis also predicted the accelerator current density for transition from net erosion to net deposition considerably more accurately than the original analysis. The improved analysis was used to estimate the impact of back-sputtered carbon on the accelerator grid pit and groove wear rate of the NEXT Long Duration Test (LDT1). Unlike the NSTAR analysis, the NEXT analysis was more challenging because the thruster was operated for extended durations at various operating conditions and was unavailable for measurements because the test is ongoing. As a result, the NEXT LDT1 estimates presented herein are considered preliminary until the results of future post-test analyses are incorporated. The worst-case impact of carbon back-sputtering was determined to be the full power operating condition, but the maximum impact of back-sputtered carbon was only a 4 percent reduction in wear rate. As a result, back-sputtered carbon is estimated to have an insignificant impact on the first failure mode of the NEXT LDT1 at all operating conditions.

Soulas, George C.↗
NASA NTRSDate not supplied

Modeling Atmospheric Science Knowledge from Research Publications

NASA Earth Science Data Centers contain enormous amounts of remote sensing digital data. It is often a significant challenge for users to find data suitable for their research topic in these vast archives. One of the approaches is the usage-driven dataset discovery, where users seek publications on projects similar to their intended study. For this approach to be effective, users need a clear connection between the underlying data in the publications and the study objectives; this is not often apparent to non-expert users. Tools and methodologies that can help facilitate and organize these connections are therefore valuable for creating improved knowledge mappings, which can be further used by search engines to suggest data or publications best tailored to a user’s specific research goal. As an illustration of these challenges, in this work we focus on the atmospheric chemistry processes related to Earth environmental impacts such as ozone depletion, aerosols, smog formation, acid rain, and radiative forcing. We further limit our study to publications that use data from the Microwave Limb Sounder (MLS) instrument flown on the Aura Earth Observing System. To create knowledge representations of science carried out in these publications, we use existing ontologies such as the Global Change Master Directory (GCMD) and Semantic Web for Earth and Environmental Terminology (SWEET). These ontologies together encompass term dictionaries that include measured variables, names of molecules or radicals, mission and instrument names, locations, action words, among many others. Based on these terms acknowledge graph database was populated with the terms retrieved from scientific publications that study atmospheric chemistry. These databases can be used to further enhance the automation of knowledge discovery and facilitate machine learning and artificial intelligence algorithms or applications. These tools and methods can also be extended to apply to content from other related Earth science domains.

Irina Gerasimov↗
NASA NTRSDate not supplied

Online Small-Sat Knowledge Repositories and Modeling Tools for Risk Reduction and Enhanced Mission Success

Developers of small satellites face the challenges of a short development schedule, incomplete information about parts from vendors, and limited budgets. The situation is complicated by the need to use commercial electronic parts rather than radiation hardened parts to meet performance and budget constraints. Fortunately, a number of organizations are standing up new modeling and data resources that are available for free online. Motivation for sharing models, part data, and lessons learned include shorter development times, increased mission success likelihood, and training the next generation of space professionals. The purpose of this paper is to make these resources and their complementary capabilities known to the small satellite community.

Small Satellites↗
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↗
DOE OSTI2023

KEBLM: Knowledge-Enhanced Biomedical Language Models

Pretrained language models (PLMs) have demonstrated strong performance on many natural language processing (NLP) tasks. Despite their great success, these PLMs are typically pretrained only on unstructured free texts without leveraging existing structured knowledge bases that are readily available for many domains, especially scientific domains. As a result, these PLMs may not achieve satisfactory performance on knowledge-intensive tasks such as biomedical NLP. Comprehending a complex biomedical document without domain-specific knowledge is challenging, even for humans. Inspired by this observation, we propose a general framework for incorporating various types of domain knowledge from multiple sources into biomedical PLMs. We encode domain knowledge using lightweight adapter modules, bottleneck feed-forward networks that are inserted into different locations of a backbone PLM. For each knowledge source of interest, we pretrain an adapter module to capture the knowledge in a self-supervised way. We design a wide range of self-supervised objectives to accommodate diverse types of knowledge, ranging from entity relations to description sentences. Once a set of pretrained adapters is available, we employ fusion layers to combine the knowledge encoded within these adapters for downstream tasks. Each fusion layer is a parameterized mixer of the available trained adapters that can identify and activate the most useful adapters for a given input. Our method diverges from prior work by including a knowledge consolidation phase, during which we teach the fusion layers to effectively combine knowledge from both the original PLM and newly-acquired external knowledge using a large collection of unannotated texts. After the consolidation phase, the complete knowledge-enhanced model can be fine-tuned for any downstream task of interest to achieve optimal performance. Extensive experiments on many biomedical NLP datasets show that our proposed framework consistently improves the performance of the underlying PLMs on various downstream tasks such as natural language inference, question answering, and entity linking. These results demonstrate the benefits of using multiple sources of external knowledge to enhance PLMs and the effectiveness of the framework for incorporating knowledge into PLMs. Finally, while primarily focused on the biomedical domain in this work, our framework is highly adaptable and can be easily applied to other domains, such as the bioenergy sector.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS2014

Aviation Safety Risk Modeling: Lessons Learned From Multiple Knowledge Elicitation Sessions

Aviation safety risk modeling has elements of both art and science. In a complex domain, such as the National Airspace System (NAS), it is essential that knowledge elicitation (KE) sessions with domain experts be performed to facilitate the making of plausible inferences about the possible impacts of future technologies and procedures. This study discusses lessons learned throughout the multiple KE sessions held with domain experts to construct probabilistic safety risk models for a Loss of Control Accident Framework (LOCAF), FLightdeck Automation Problems (FLAP), and Runway Incursion (RI) mishap scenarios. The intent of these safety risk models is to support a portfolio analysis of NASA's Aviation Safety Program (AvSP). These models use the flexible, probabilistic approach of Bayesian Belief Networks (BBNs) and influence diagrams to model the complex interactions of aviation system risk factors. Each KE session had a different set of experts with diverse expertise, such as pilot, air traffic controller, certification, and/or human factors knowledge that was elicited to construct a composite, systems-level risk model. There were numerous "lessons learned" from these KE sessions that deal with behavioral aggregation, conditional probability modeling, object-oriented construction, interpretation of the safety risk results, and model verification/validation that are presented in this paper.

Luxhoj, J. T.↗
NASA NTRS1989

A development framework for distributed artificial intelligence

The authors describe distributed artificial intelligence (DAI) applications in which multiple organizations of agents solve multiple domain problems. They then describe work in progress on a DAI system development environment, called SOCIAL, which consists of three primary language-based components. The Knowledge Object Language defines models of knowledge representation and reasoning. The metaCourier language supplies the underlying functionality for interprocess communication and control access across heterogeneous computing environments. The metaAgents language defines models for agent organization coordination, control, and resource management. Application agents and agent organizations will be constructed by combining metaAgents and metaCourier building blocks with task-specific functionality such as diagnostic or planning reasoning. This architecture hides implementation details of communications, control, and integration in distributed processing environments, enabling application developers to concentrate on the design and functionality of the intelligent agents and agent networks themselves.

Adler, Richard M.↗
DOE OSTI2022

KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N<sub>2</sub>O emission using data from mesocosm experiments

Abstract. Agricultural nitrous oxide (N2O) emission accounts for a non-trivial fraction of global greenhouse gas (GHG) budget. To date, estimating N2O fluxes from cropland remains a challenging task because the related microbial processes (e.g., nitrification and denitrification) are controlled by complex interactions among climate, soil, plant and human activities. Existing approaches such as process-based (PB) models have well-known limitations due to insufficient representations of the processes or uncertainties of model parameters, and due to leverage recent advances in machine learning (ML) a new method is needed to unlock the “black box” to overcome its limitations such as low interpretability, out-of-sample failure and massive data demand. In this study, we developed a first-of-its-kind knowledge-guided machine learning model for agroecosystems (KGML-ag) by incorporating biogeophysical and chemical domain knowledge from an advanced PB model, ecosys, and tested it by comparing simulating daily N2O fluxes with real observed data from mesocosm experiments. The gated recurrent unit (GRU) was used as the basis to build the model structure. To optimize the model performance, we have investigated a range of ideas, including (1) using initial values of intermediate variables (IMVs) instead of time series as model input to reduce data demand; (2) building hierarchical structures to explicitly estimate IMVs for further N2O prediction; (3) using multi-task learning to balance the simultaneous training on multiple variables; and (4) pre-training with millions of synthetic data generated from ecosys and fine-tuning with mesocosm observations. Six other pure ML models were developed using the same mesocosm data to serve as the benchmark for the KGML-ag model. Results show that KGML-ag did an excellent job in reproducing the mesocosm N2O fluxes (overall r2=0.81, and RMSE=3.6 mgNm-2d-1 from cross validation). Importantly, KGML-ag always outperforms the PB model and ML models in predicting N2O fluxes, especially for complex temporal dynamics and emission peaks. Besides, KGML-ag goes beyond the pure ML models by providing more interpretable predictions as well as pinpointing desired new knowledge and data to further empower the current KGML-ag. We believe the KGML-ag development in this study will stimulate a new body of research on interpretable ML for biogeochemistry and other related geoscience processes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

A knowledge base architecture for distributed knowledge agents

A tuple space based object oriented model for knowledge base representation and interpretation is presented. An architecture for managing distributed knowledge agents is then implemented within the model. The general model is based upon a database implementation of a tuple space. Objects are then defined as an additional layer upon the database. The tuple space may or may not be distributed depending upon the database implementation. A language for representing knowledge and inference strategy is defined whose implementation takes advantage of the tuple space. The general model may then be instantiated in many different forms, each of which may be a distinct knowledge agent. Knowledge agents may communicate using tuple space mechanisms as in the LINDA model as well as using more well known message passing mechanisms. An implementation of the model is presented describing strategies used to keep inference tractable without giving up expressivity. An example applied to a power management and distribution network for Space Station Freedom is given.

Riedesel, Joel↗
DOE OSTI2022

PemNet: A Transfer Learning-Based Modeling Approach of High-Temperature Polymer Electrolyte Membrane Electrochemical Systems

Widespread adoption of high-temperature electrochemical systems such as polymer electrolyte membrane fuel cells (HT-PEMFCs) requires models and computational tools for accurate optimization and guiding new materials for enhancing fuel cell performance and durability. Furthermore, while robust and better suited for extrapolation, knowledge-based modeling has limitations as it is time-consuming and requires information about the system that is not always available (e.g., material properties and interfacial behavior between different materials). Data-driven modeling, on the other hand, is easier to implement but often necessitates large datasets that could be difficult to obtain. In this contribution, knowledge-based modeling and data-driven modeling are combined by implementing a few-shot learning (FSL) approach. A knowledge-based model originally developed for a HT-PEMFCs was used to generate simulated data (887,735 points) and used to pretrain a neural network source model tuned via a genetic algorithm-based AutoML. Then, experimental datasets from HT-PEMFCs with different materials and operating conditions (~50 points each) were used to train six target models via FSL. Models for the unseen data reached high accuracies in all cases (rRMSE < 10%).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2006

Microwave Signatures of Snow on Sea Ice: Modeling

Accurate knowledge of snow-depth distribution over sea ice is critical for polar climate studies. Current snow-depth-over-sea-ice retrieval algorithms do not sufficiently account for variations in snow and ice physical properties that can affect the accuracy of retrievals. For this reason, airborne microwave observations were coordinated with ground-based measurements of snow depth and snow properties in the vicinity of Barrow, AK, in March 2003. In this paper, the effects of snowpack properties and ice conditions on microwave signatures are examined using detailed surface-based measurements and airborne observations in conjunction with a thermal microwave-emission model. A comparison of the Microwave Emission Model of Layered Snowpacks (MEMLS) simulations with detailed snowpack and ice data from stakes along the Elson Lagoon and the Beaufort Sea and ra- 'diometer data taken from low-level flights using a Polarimetric Scanning Radiometer (PSR-A) shows that MEMLS can be used to simulate snow on sea ice and is a useful tool for understanding the limitations of the snow-depth algorithm. Analysis of radiance data taken over the Elson Lagoon and the Beaufort Sea using MEMLS suggests that the radiometric differences between the two locations are due to the differences in sea-ice emissivity. Furthermore, measured brightness temperatures suggest that the current snow-depth retrieval algorithm is sufficient for areas of smooth first-year sea ice, whereas new algorithm coefficients are needed for rough first-year sea ice. Snowpack grain size and density remain an unresolved issue for snow-depth retrievals using passive-microwave radiances.

Powell, D. C.↗
DOE OSTI2022

Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science

Abstract Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph‐based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these “knowledge graphs” (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open‐access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open‐source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object‐oriented classification and graph‐oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1990

A model for a knowledge-based system's life cycle

The American Institute of Aeronautics and Astronautics has initiated a Committee on Standards for Artificial Intelligence. Presented here are the initial efforts of one of the working groups of that committee. The purpose here is to present a candidate model for the development life cycle of Knowledge Based Systems (KBS). The intent is for the model to be used by the Aerospace Community and eventually be evolved into a standard. The model is rooted in the evolutionary model, borrows from the spiral model, and is embedded in the standard Waterfall model for software development. Its intent is to satisfy the development of both stand-alone and embedded KBSs. The phases of the life cycle are detailed as are and the review points that constitute the key milestones throughout the development process. The applicability and strengths of the model are discussed along with areas needing further development and refinement by the aerospace community.

Kiss, Peter A.↗