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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Performance and stability of telemanipulators using bilateral impedance control

A new method of control for telemanipulators called bilateral impedance control is investigated. This new method differs from previous approaches in that interaction forces are used as the communication signals between the master and slave robots. The new control architecture has several advantages: (1) It allows the master robot and the slave robot to be stabilized independently without becoming involved in the overall system dynamics; (2) It permits the system designers to arbitrarily specify desired performance characteristics such as the force and position ratios between the master and slave; (3) The impedance at both ends of the telerobotic system can be modulated to suit the requirements of the task. The main goals of the research are to characterize the performance and stability of the new control architecture. The dynamics of the telerobotic system are described by a bond graph model that illustrates how energy is transformed, stored, and dissipated. Performance can be completely described by a set of three independent parameters. These parameters are fundamentally related to the structure of the H matrix that regulates the communication of force signals within the system. Stability is analyzed with two mathematical techniques: the Small Gain Theorem and the Multivariable Nyquist Criterion. The theoretical predictions for performance and stability are experimentally verified by implementing the new control architecture on a multidegree of freedom telemanipulator.

Moore, Christopher Lane↗
DOE OSTI2024

A graph neural network-state predictive information bottleneck (GNN-SPIB) approach for learning molecular thermodynamics and kinetics

Molecular dynamics simulations offer detailed insights into atomic motions but face timescale limitations. Enhanced sampling methods have addressed these challenges but even with machine learning, they often rely on pre-selected expert-based features. Here, in this work, we present a Graph Neural Network-State Predictive Information Bottleneck (GNN-SPIB) framework, which combines graph neural networks and the state predictive information bottleneck to automatically learn low-dimensional representations directly from atomic coordinates. Tested on three benchmark systems, our approach predicts essential structural, thermodynamic and kinetic information for slow processes, demonstrating robustness across diverse systems. The method shows promise for complex systems, enabling effective enhanced sampling without requiring pre-defined reaction coordinates or input features.

Zou, Ziyue↗
NASA NTRS2021

TLife-LSTM: Forecasting Future COVID-19 Progression with Topological Signatures of Atmospheric Conditions

Understanding the impact of atmospheric conditions on SARS-CoV2 is critical to model COVID-19 dynamics and sheds a light on the future spread around the world. Furthermore, geographic distri- butions of expected clinical severity of COVID-19 may be closely linked to prior history of respiratory diseases and changes in humidity, tem- perature, and air quality. In this context, we postulate that by tracking topological features of atmospheric conditions over time, we can provide a quanti?able structural distribution of atmospheric changes that are likely to be related to COVID-19 dynamics. As such, we apply the machinery of persistence homology on time series of graphs to extract topological signatures and to follow geographical changes in relative humidity and temperature. We develop an integrative machine learning framework named Topological Lifespan LSTM (TLife-LSTM) and test its predictive capabilities on forecasting the dynamics of SARS-CoV2 cases. We validate our framework using the number of con?rmed cases and hospitalization rates recorded in the states of Washington and California in the USA. Our results demonstrate the predictive potential of TLife-LSTM in forecasting the dynamics of COVID-19 and modeling its complex spatio-temporal spread dynamics.

Gel, Yulia R.↗
NASA NTRSDate not supplied

Multi-Domain Routing in Delay Tolerant Networks

The goal of Delay Tolerant Networking (DTN) is to provide the missing ingredient for the ever-growing collection of communicating nodes in our solar system to become a Solar System Internet (SSI). Great strides have been made in modeling particular types of DTNs, such as schedule- or discovery-based. Now, analogously to the Internet, these smaller DTNs can be considered routing domains which must be stitched together to form the overall SSI. In this paper, we propose a framework for cross-domain routing in DTNs as well as methodologies for detecting these sub-domains. Example time-varying networks are given to demonstrate the techniques proposed. A basic component is the mathematical theory of sheaves, which unifies the underlying model of DTN routing algorithms, by giving rise to routing sheaves – these can be defined for the dynamic and scheduled networks as noted above, and can also be used to define the interfaces between these domains in order to route across them. An immediate application would be routing across discovery-based networks connected by scheduled networks. These DTN subdomains remain elusive, however, and need to become well-defined and properly sized for tractable computability. In particular, a balance must be determined between areas that are too large (i.e. large matrix computations) versus areas that are too small (i.e. “many” single-noded domains). Moreover, the connections between the domains should, at least locally, be chosen to optimize data flow and connectivity: we address this in three ways. First, tools from persistent homology are given to understand underlying structures, reminiscent of hierarchies in the Internet Protocol (IP) addressing. Second, we construct a notion of temporal graph curvature based on network geometry to analyze flows induced by dynamical processes on these networks. Finally, Schrodinger Bridges, a tool arising from statistical physics, are proposed as a method of constructing flows on time-evolving networks with desirable properties such as speed, robustness, and load sensitivity. We construct an approach to temporal hypergraphs to simultaneously model unicast, multicast, and broadcast, using the language of scheme theory, and then consider DTN network coding as a way to achieve network-level computation and organization. The paper concludes with a discussion and ideas for future work.

Alan Hylton↗
DOE OSTI2025

Novel Results Visualization for Dynamic PSA and New Modeling Features in EMRALD

The Event Modeling Risk Assessment Linked Diagram (EMRALD) tool, developed at the Idaho National Laboratory (INL), was designed to simplify the creation of dynamic models and support various research projects. One of the primary goals of EMRALD was to provide visual methods for modeling. EMRALD consists of two main components: a web-based user interface for model development and a solve engine for running model simulations. Over time, it has evolved to meet the diverse needs of its users. Initially, EMRALD's results were simple text outputs with final key state percentages and uncertainty bounds. However, because EMRALD utilizes a three-phase discrete event simulation and tracks the paths of each simulation run leading to a key state, there is significant potential to analyze large sets of path results data, including state paths, events, and timing. Visualizing this data meaningfully posed a challenge. To address this, a novel time-based Sankey diagram was developed. EMRALD exports results data in a format that can be opened in this Sankey viewer, allowing users to visualize paths, occurrences, events, and probability data for the entire simulation run in a single diagram. Moreover, when EMRALD was first created, there were limited tools capable of meeting its graphical requirements, many of which are no longer supported. In 2024, a new web-based interface was developed using modern graphing tools, enabling additional modeling features. This paper discusses the new dynamic PSA results visualization capability and the enhanced modeling tools available in EMRALD.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Molecular architecture and functional dynamics of the pre-incision complex in nucleotide excision repair

Nucleotide excision repair (NER) is vital for genome integrity. Yet, our understanding of the complex NER protein machinery remains incomplete. Combining cryo-EM and XL-MS data with AlphaFold2 predictions, we build an integrative model of the NER pre-incision complex(PInC). Here TFIIH serves as a molecular ruler, defining the DNA bubble size and precisely positioning the XPG and XPF nucleases for incision. Using simulations and graph theoretical analyses, we unveil PInC’s assembly, global motions, and partitioning into dynamic communities. Remarkably, XPG caps XPD’s DNA-binding groove and bridges both junctions of the DNA bubble, suggesting a novel coordination mechanism of PInC’s dual incision. XPA rigging interlaces XPF/ERCC1 with RPA, XPD, XPB, and 5' ssDNA, exposing XPA’s crucial role in licensing the XPF/ERCC1 incision. Mapping disease mutations onto our models reveals clustering into distinct mechanistic classes, elucidating xeroderma pigmentosum and Cockayne syndrome disease etiology.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1987

The importance of aerodynamics on dynamics

The effects of aerodynamic phenomena on helicopter structural dynamics are examined, reviewing the results of recent theoretical and experimental investigations. A schematic diagram of a dynamic analysis is presented and discussed, noting the numerous aerodynamic inputs, and numerical data are presented in graphs. Consideration is given to forcing functions, aerodynamic damping, aeroelastic coupling, negative damping, apparent coriolis coupling, periodic coefficients, inverse damping, additional degrees of freedom, flap-lag stability, and dynamic inflow. The need for theoretical models which account for the dynamic loop between blade lift and induced flow (permitting blade motion to dynamically alter the flowfield during transients) is indicated.

Peters, David A.↗
DOE OSTI2026

Knowledge Graph for End-to-End Traceability of an Integrated Human-Earth System Model

Integrated human-Earth system models inform energy-water-land system dynamics and policies, yet their results are difficult to trace through input-data, model structure, scenario configurations, and solved outputs. Because this information is siloed across disconnected artifacts, process-based IAMs have historically lacked a unified, queryable representation. Such lack of traceability prevents researchers from systematically isolating the multi-sector drivers of complex outcomes (such as tracing water-scarcity results back to distant energy-system dynamics) or conducting holistic uncertainty attribution across hundreds of interacting parameters. To address this concern, our work documents the software engineering process of a knowledge graph that unifies these four layers for the Global Change Analysis Model (GCAM-USA_Reference scenario, GCAM v9.1). The graph was built as a relational property graph in DuckDB from the run’s own artifacts: the input-preparation dependency map (gcamdata chunk map), the model’s XML input files, the run configuration, and the results database (BaseX), successfully mapping the model’s declared structure. The resulting graph comprises 204,321 nodes and 1,687,814 edges across 16 node types and 15 edge types, with approximately 16.3 million time-series values stored separately to maintain structural efficiency. To ensure representation fidelity, every edge carries an epistemic-status annotation recording the warrant for the relationship (structural, provenance, dependency, or model-derived), and a machine-readable provenance ledger classifying the origin of every schema element. Evaluation against a fixed five-benchmark suite with locked baselines reports zero structural orphans, zero dangling edge endpoints, and 100% of output-producing technologies traceable to raw input files. Two interactive interfaces present the graph, including a serverless browser application built on DuckDB-Wasm. By establishing the first end-to-end provenance framework for an IAM, this work enables researchers and scientists to systematically audit complex policy scenarios, debug model structures, and trace policy-relevant outputs to their data origins in real time.

Artifical Intelligence↗
DOE OSTI2025

A Dynamic Hierarchical Attention Framework for Multimodal Malware Detection

The increasing use of Android in the worldwide mobile ecosystem has come along with a significant increase in advanced malware, highlighting the critical necessity for efficient, scalable, and adaptable detection systems. Despite recent advancements in machine learning improving malware detection, the majority of current solutions are limited to one, two, or three data modalities, hence neglecting the comprehensive behavioral spectrum of contemporary multi-vector threats. This thesis presents the first comprehensive multimodal framework for Android malware detection, which combines textual, time-series (temporal), graph-based (structural), and visual information using an innovative hierarchical attention mechanism and Dynamic Fusion Controller (DFC). Our methodology consistently classifies and processes modalities as either sequential or structural, facilitating content-adaptive weighting and resilient cross-modal representation learning. We advance the implementation of cutting-edge time series techniques, such as MiniRocket, for malware detection, hence creating new opportunities for temporal analysis in cybersecurity. Comprehensive experimental assessment shows that our framework performs exceptionally well, with 99.46% classification accuracy and 97.15% detection accuracy, significantly outperforming existing approaches through effective multimodal integration and hierarchical attention mechanisms.

Nazmin, Tamanna↗
DOE OSTI2025

Quantum Hamiltonian algorithms for maximum independent sets

ABSTRACT We compare two quantum Hamiltonian algorithms that address the maximum independent set problem: one based on the emergent non-Abelian gauge matrix in adiabatic evolution of an energetically isolated manifold of states; the other based on designed application of single-qubit operations. We demonstrate that they are mathematically equivalent in the sense that one is the other’s interaction picture. Despite their mathematical equivalence, our numerical simulations show significant differences between them in performance, which is explained analytically. Intriguingly, this equivalence unveils that the PXP model, recently prominent in quantum dynamics research, can be viewed as quantum diffusion over the median graph of all independent sets governed by the non-Abelian gauge matrix.

Science & Technology - Other Topics↗
NASA NTRS1989

Ring dynamics

Theoretical models of planetary-ring dynamics are discussed in a detailed analytical review and illustrated with graphs and diagrams. The streamline concept is introduced, and the phenomena associated with the transport of angular momentum are described. Particular attention is then given to (1) broad rings like those of Saturn (shepherding, density-wave excitation, gaps, bending-wave excitation, multiringlet structures, inner-edge shepherding, and the possibility of polar rings around Neptune), (2) narrow rings like those of Uranus (shepherding, ring shapes, and a self-gravity model of rigid precession), and (3) ring arcs like those seen in stellar-occultation observations of Neptune.

Borderies, Nicole↗
NASA NTRS1994

Dynamical observer for a flexible beam via finite element approximations

The purpose of this view-graph presentation is a computational investigation of the closed-loop output feedback control of a Euler-Bernoulli beam based on finite element approximation. The observer is part of the classical observer plus state feedback control, but it is finite-dimensional. In the theoretical work on the subject it is assumed (and sometimes proved) that increasing the number of finite elements will improve accuracy of the control. In applications, this may be difficult to achieve because of numerical problems. The main difficulty in computing the observer and simulating its work is the presence of high frequency eigenvalues in the finite-element model and poor numerical conditioning of some of the system matrices (e.g. poor observability properties) when the dimension of the approximating system increases. This work dealt with some of these difficulties.

Manitius, Andre↗
DOE OSTI2026

SENTRA: A Modular Computational Graph Framework for Critical Mineral and Materials Supply Chains: Part I: Network Construction Latent-Quantity Estimation, and Temporal Graph Forecasting

Global supply chains for critical minerals and materials are complex, evolving networks of countries, products, production stages, and trade relationships. Existing analytical approaches are limited by fragmented data and static network representations that do not capture the dynamic production dependencies linking raw materials, intermediate products, and final goods across multiple countries. Trade and production statistics provide only a partial view of domestic production, inventories, and material flows, making it difficult to identify indirect sourcing pathways, hidden dependencies, and embedded foreign exposures. This paper introduces the Supply Chain Exposure Network Tracking and Risk Assessment (SENTRA) framework, a modular graph-based computational framework for constructing, analyzing, and forecasting dynamic supply chain networks. As the first paper in a three-part methodological series, it establishes the computational foundation of SENTRA by constructing a temporal attributed multi-relational graph whose nodes represent product–country pairs and whose edges encode observed trade and within-country value-chain relationships. Statistical estimation and constrained optimization recover latent production, final demand, and product input dependency coefficients while enforcing economic accounting constraints. Graph-derived exposure measures quantify direct, transshipment, value-chain, and multi-hop supply chain dependencies independently of the forecasting model. A temporal graph forecasting architecture based on a relational graph neural network then forecasts the evolution of the graph under mass-balance constraints with distribution-free conformal uncertainty quantification. Validation on the global aluminum supply chain shows that the learned graph representations recover economically meaningful supply chain structure, accurately forecast out-of-sample trade relationships, and produce well-calibrated prediction intervals. Subsequent papers apply this computational foundation to exposure assessment, disruption analysis, and scenario-based policy analysis, and extend the framework to multimaterial supply chain modeling and decision support.

36 MATERIALS SCIENCE↗
NASA NTRS1991

A nonrecursive 'Order N' preconditioned conjugate gradient/range space formulation of MDOF dynamics

This paper addresses the requirements of present-day mechanical system simulations of algorithms that induce parallelism on a fine scale and of transient simulation methods which must be automatically load balancing for a wide collection of system topologies and hardware configurations. To this end, a combination range space/preconditioned conjugage gradient formulation of multidegree-of-freedon dynamics is developed, which, by employing regular ordering of the system connectivity graph, makes it possible to derive an extremely efficient preconditioner from the range space metric (as opposed to the system coefficient matrix). Because of the effectiveness of the preconditioner, the method can achieve performance rates that depend linearly on the number of substructures. The method, termed 'Order N' does not require the assembly of system mass or stiffness matrices, and is therefore amenable to implementation on work stations. Using this method, a 13-substructure model of the Space Station was constructed.

Kurdila, A. J.↗
DOE OSTI2025

Model-based Hierarchical Reinforcement Learning for Improved Physical Security Design: A Prototype

Prior work in FY24 developed an adversarial AI agent aid in path analysis of physical protection systems. This agent, trained using a model-based reinforcement learning algorithm, was able to successfully learn the most vulnerable path in facilities. It was able to extend the current state of practice for physical protection design by exhibiting dynamic behavior based on current environmental conditions. Whereas PathTrace largely performs a static, graph-based analysis, the AI agent was able to make decisions based on relative position in the facility, current conditions (was the adversarial agnet discovered?), and proximity to secondary targets. The agent demonstrated some novel capabilities, but had limitations that need to be resolved before it can be used for production purposes. For example, the adversarial agent generalizes poorly and takes a relatively long time to train. Nonetheless, there is still considerable promise for developing the adversarial agent further in order to explore even richer, more dynamic behaviors (e.g., adversary motivations, environmental debris, and more). This work considers a complementary idea; development of a planning agent. The planning agent is envisioned as an auto-complete-like tool that can help accelerate security system design by human experts. The agent would respect existing barriers and sensors placed by a human expert while offering cost-effective suggestions (i.e., implicitly balancing effectiveness with cost) to improve the design. The goal is for this agent to be part of an expert’s toolbox, not to totally upend the current state-of-practice, or to displace human experts. The ultimate goal would be concurrent training of both the adversarial and planning agent together, to learn entirely through self-play. This would represent an entirely new way of performing system deign. We selected a hierarchical, model-based reinforcement learning algorithm to serve as the planning agent. This is an extension of concepts used in the prior FY24 adversarial agent work. There, we had a single agent acting an environment. Here, we have two different sub-agents (policies), working together, to form a complete agent. There is a manager policy, which can select abstract goals on slower time scales, and a worker, which performs primitive actions to reach goals selected by the manager. It is worth noting that this class of algorithm is challenging to work with. From our understanding, our work is one of the first successful uses of model-based reinforcement learning (MBRL) in nuclear energy1 , and likely the first hierarchical model-based reinforcement learning application in nuclear energy. Further, this work is one of the first known attempts to apply AI to perform a design tasks in nuclear energy. Consequently, there were significant implementation challenges and the bulk of the work was focused on successful implementation and algorithm design. The results presented here are very low technology readiness level as a consequence of the lack of related literature, but still represent a significant step forward in the pursuit of applied AI for design.

42 ENGINEERING↗
DOE OSTI2024

Retrieval Augmented Generation for Robust Cyber Defense

In cybersecurity, the ability to efficiently analyze and respond to vulnerabilities, weaknesses, attack patterns, and threat tactics is critical for effective defense strategies. With the increasing complexity and volume of cybersecurity data, traditional methods of querying and retrieving information are often inadequate. To address this challenge, we implemented Retrieval-Augmented Generation (RAG) systems—CyRAG and GraphCyRAG—that integrate large language models (LLMs) with both structured data from relational databases and knowledge graphs such as Neo4j. CyRAG is designed to handle structured data, focusing on CVE (Common Vulnerabilities and Exposures) and CWE (Common Weakness Enumeration) entities to generate accurate and context-rich responses. In contrast, GraphCyRAG leverages Neo4j knowledge graphs to retrieve interconnected information from CVE, CWE, CAPEC (Common Attack Pattern Enumeration and Classification), and ATT&CK (Adversarial Tactics, Techniques, and Common Knowledge) datasets. By utilizing Neo4j’s graph-based framework, GraphCyRAG enables deeper traversal of relationships between vulnerabilities and attack patterns, providing cybersecurity analysts with more comprehensive insights into potential attack vectors and mitigation strategies. Our preliminary results demonstrate that integrating knowledge graphs with RAG significantly enhances both the accuracy and depth of threat analysis, allowing for the retrieval of dynamic, real-time data and the generation of contextually aware responses. This approach helps analysts uncover hidden relationships between cyber entities, predict exploit paths, and prioritize mitigation efforts effectively. The integration of RAG with cybersecurity knowledge graphs represents a significant advancement in cybersecurity threat intelligence, enabling more informed decision-making and stronger defense strategies.

97 MATHEMATICS AND COMPUTING↗