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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Strym: A Python Package for Real-time CAN Data Logging, Analysis and Visualization to Work with USB-CAN Interface

In this report, we describe a data analysis tool developed for decoding and analyzing vehicle data obtained from a passenger vehicle’s onboard controller area network (CAN) bus. The tool developed in this paper provides a timeseries framework to perform domain-specific analysis at scale when interpreting data from a vehicle or a collection of vehicles in light of how to design intelligent vehicle applications. The tool, called Strym, exploits the CAN bus mechanism of modern vehicles to capture data using commercially available CAN-to-USB hardware Comma.ai Panda devices, managed through open-source software Libpanda. Strym permits the decoding of vendor-specific CAN messages in a vehicle-agnostic manner. Through this, a researcher can characterize data throughput, assess data quality, and perform analyses. Such analyses are useful in a number of research such as studying human driving behavior in mixed-autonomy, new driver models, rare-event detection, traffic flow estimation, and custom control of vehicles.

Performance evaluation, Smart cities, Intelligent ↗
DOE OSTI2022

Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science Applications

Physics-constrained machine learning is emerging as an important topic in the field of machine learning for physics. One of the most significant advantages of incorporating physics constraints into machine learning methods is that the resulting model requires significantly less data to train. By incorporating physical rules into the machine learning formulation itself, the predictions are expected to be physically plausible. Gaussian process (GP) is perhaps one of the most common methods in machine learning for small datasets. In this paper, we investigate the possibility of constraining a GP formulation with monotonicity on three different material datasets, where one experimental and two computational datasets are used. The monotonic GP is compared against the regular GP, where a significant reduction in the posterior variance is observed. The monotonic GP is strictly monotonic in the interpolation regime, but in the extrapolation regime, the monotonic effect starts fading away as one goes beyond the training dataset. Imposing monotonicity on the GP comes at a small accuracy cost, compared to the regular GP. The monotonic GP is perhaps most useful in applications where data are scarce and noisy, and monotonicity is supported by strong physical evidence.

36 MATERIALS SCIENCE↗
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)↗
DOE OSTI2025

Deploying Adversarial Attacks in Super-Resolution Models

Reliable super-resolution methods are crucial for applications like remote sensing, grid resilience and disaster impact analysis, and standoff biometrics. These methods infuse additional high-frequency information into reconstructions, allowing for better contextualization and image intelligence. However, super-resolution models can also introduce hallucinations or other unseen vulnerabilities that could be exploited by an adversary. This is further compounded by the prominence of deep learning in these models, as models are often blindly applied on out-of-distribution images. In this work, we implement adversarial attacks in common open-source super-resolution models and examine their impact on reconstructions and downstream classification tasks. We find that an adversarially trained super-resolution model can produce high-quality reconstructions that degrade downstream classifications. Moreover, these attacks do not require access to low-resolution imagery or class labels at inference time. These results demonstrate the vulnerability of super-resolution methods to malicious actors and motivates the development of a detector for super-resolution adversarial attacks. Further exploration of adversarial attacks in this domain is required to ensure trustworthiness and robustness of super-resolution models for national security applications.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRS2014

Vision Based Autonomous Robotic Control for Advanced Inspection and Repair

The advanced inspection system is an autonomous control and analysis system that improves the inspection and remediation operations for ground and surface systems. It uses optical imaging technology with intelligent computer vision algorithms to analyze physical features of the real-world environment to make decisions and learn from experience. The advanced inspection system plans to control a robotic manipulator arm, an unmanned ground vehicle and cameras remotely, automatically and autonomously. There are many computer vision, image processing and machine learning techniques available as open source for using vision as a sensory feedback in decision-making and autonomous robotic movement. My responsibilities for the advanced inspection system are to create a software architecture that integrates and provides a framework for all the different subsystem components; identify open-source algorithms and techniques; and integrate robot hardware.

sensory feedback↗
NASA NTRS1996

On-board Neural Processor Design for an Intelligent Multi-sensor Microspacecraft

A compact VLSI neural processor based on the Optimization Cellular Neural Network (OCNN)has been under development to provide a wide range of support for an intelligent remote sensing microspacecraft which requires both high bandwidth communication and high-performance computing for on-board data analysis, thematic data reduction, synergy of multiple types of sensors, and other smart-sensor functions. The OCNN architecture is a programmable multi-dimensional array of neurons which are locally connected with their local neurons. The OCNN operation theory, architecture, design and implementation, prototype chip, and system applications have been investigated in detail and presented in this paper.

array↗
NASA NTRS1993

Performance analysis of a robotic system for space-based assembly

A dual arm system for space-based assembly has been under development and experimentation at the Center of Intelligent Robotic Systems for Space Exploration. This paper will describe the components of this system and will then focus on the specific problem of predicting response time of the integrated system. This hierarchical system consists of a task representation and planning level, a coordination level, and an execution level to carry out the desired assembly task. A common modeling methodology based on Petri nets was used to model the various subtasks throughout the levels of the hierarchy. The Petri net model can then be analyzed for liveness and boundedness that, in turn, are used to detect the existence of deadlock or conflict in the system. The model can also be used to calculate the time it will take to complete an assembly task once an operator issues a command. These modeling results are compared with the actual experimental results obtained from carrying out the assembly task.

Desrochers, Alan↗
DOE OSTI2026

Plant Bioengineering Atlas: A Knowledge Graph of Genes, DNA Constructs, and Plant Traits.

Plant bioengineering has generated tens of thousands of genotype-to-phenotype relationships, but this knowledge remains fragmented across narrative literature and difficult to use computationally. Inconsistent descriptions of DNA constructs, host species, and traits, including variable species names, omitted regulatory elements, and inconsistent gene symbols, impede data reuse, comparative analysis, and design-build-test-learn cycles. Here, we present the Plant Bioengineering Atlas, a literature-mined, ontology-grounded knowledge base assembled using an artificial intelligence (AI)-aided extraction pipeline. A large language model parsed open-access primary research articles to generate structured, provenance-anchored records of engineered genes, modification types, promoter-gene-terminator constructs, host species, target traits, and reported phenotypes, with every record traceable to its source. The current release contains 14,358 curated records encompassing 6,998 distinct genes across 436 plant species from 6,452 papers published between 2000 and 2026. Corpus analysis reveals that experiments are concentrated in a small group of model and crop species, disease and pathogen resistance is the most frequently engineered trait class, and constitutive regulatory parts (particularly the CaMV 35S promoter and NOS terminator) remain pervasive. Two in five records omit one or both flanking regulatory elements (i.e., promoter and terminator), while only 23.4% describe cassettes in which both elements resolve to named part classes, exposing a systematic reproducibility gap. We organize these data into a knowledge graph linking genes, constructs, species, and traits; provide access through an interactive web portal; and propose an AI-compatible documentation standard for AI-ready reporting. The Plant Bioengineering Atlas provides a foundation for data-driven hypothesis generation and AI-aided plant biodesign.

, Genes, DNA Constructs↗
NASA NTRS1992

Advanced satellite workstation: An integrated workstation environment for operational support of satellite system planning and analysis

A prototype integrated environment, the Advanced Satellite Workstation (ASW), is described that has been developed and delivered for evaluation and operator feedback in an operational satellite control center. The current ASW hardware consists of a Sun Workstation and Macintosh II Workstation connected via an ethernet Network Hardware and Software, Laser Disk System, Optical Storage System, and Telemetry Data File Interface. The central mission of ASW is to provide an intelligent decision support and training environment for operator/analysts of complex systems such as satellites. There have been many workstation implementations recently which incorporate graphical telemetry displays and expert systems. ASW is a considerably broader look at intelligent, integrated environments for decision support, based upon the premise that the central features of such an environment are intelligent data access and integrated toolsets. A variety of tools have been constructed in support of this prototype environment including: an automated pass planner for scheduling vehicle support activities, architectural modeler for hierarchical simulation and analysis of satellite vehicle subsystems, multimedia-based information systems that provide an intuitive and easily accessible interface to Orbit Operations Handbook and other relevant support documentation, and a data analysis architecture that integrates user modifiable telemetry display systems, expert systems for background data analysis, and interfaces to the multimedia system via inter-process communication.

Sutton, Stewart A.↗
NASA NTRS2018

Introduction to NASA Goddard Workshop on Artificial Intelligence

Artificial Intelligence (AI) is a collection of advanced technologies that allows machines to think and act, both humanly and rationally, through sensing, comprehending, acting and learning. AI's foundations lie at the intersection of several traditional fields Philosophy, Mathematics, Economics, Neuroscience, Psychology and Computer Science. Although the inception of AI started in the 1950's, it has recently made a strong comeback in all aspects of society and all over the world; this is mainly due to the timely combination of increased data volumes, advanced and mature algorithms, and improvements in computing power and storage. Current AI applications include big data analytics, robotics, intelligent sensing, assisted decision making, and speech recognition just to name a few.This workshop will be investigating how AI technologies can be adapted or developed to address the following challenges: Discover events of interest and correlations in large amounts of science data; improve the outcomes of science modeling and data assimilation using improved data processing, integration, and analysis. Design advisors for mission planning and operations, including anomaly detection and spacecraft health monitoring. Develop tools for engineering support, including advanced manufacturing, orbit determination, new component design and system engineering. Customize intelligent user interfaces, including visual analytics and natural language processing.

Le Moigne, Jacqueline↗
NASA NTRS1985

Applications of Some Artificial Intelligence Methods to Satellite Soundings

Hard clustering of temperature profiles and regression temperature retrievals were used to refine the method using the probabilities of membership of each pattern vector in each of the clusters derived with discriminant analysis. In hard clustering the maximum probability is taken and the corresponding cluster as the correct cluster are considered discarding the rest of the probabilities. In fuzzy partitioned clustering these probabilities are kept and the final regression retrieval is a weighted regression retrieval of several clusters. This method was used in the clustering of brightness temperatures where the purpose was to predict tropopause height. A further refinement is the division of temperature profiles into three major regions for classification purposes. The results are summarized in the tables total r.m.s. errors are displayed. An approach based on fuzzy logic which is intimately related to artificial intelligence methods is recommended.

Munteanu, M. J.↗
DOE OSTI2024

Gains in operational flexibility, safety margins, and cost efficiencies via integrated Plant Reload Optimization platform

The U.S. Department of Energy Light Water Reactor Sustainability Program Risk-Informed Systems Analysis Pathway Plant Reload Optimization Project aims to develop an integrated, comprehensive framework offering an all-in-one solution for reload evaluations with a special focus on optimizing core design. Optimizing the fuel loading pattern is one of the most important considerations in reducing the amount of new fuel used in the core. Due to thousands of possible core configuration options, finding optimal solutions is an unachievable task for a human. The Plant ReLoad Optimization platform, which supports artificial-intelligence-based reactor core designing, is now fully capable of handling realistic problems. The Plant ReLoad Optimization platform development project aims to build a reactor core design tool that includes reactor safety and fuel performance analyses and uses artificial intelligence to support the optimization of core design solutions. The NSGA-II (Non-dominated Sorting Genetic Algorithm II) optimizer was developed and tested within RAVEN (Risk Analysis and Virtual ENvironment) to handle many constraints by using an augmented objectives methodology. The demonstration was performed with constrained multiobjective optimization of a 17 × 17 pressurized-water reactor core loading patterns to minimize fuel cost and maximize fuel cycle length.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Device Feasibility Analysis of Multi-level FeFETs for Neuromorphic Computing

As an emerging non-volatile memory device technology, Ferroelectric Field-Effect Transistors (FeFETs) can enable low-power, adaptive intelligent system design. However, device dimension and operating voltage dependent reliability issues of scaled FeFETs can ultimately lead to degraded performance in solving machine learning tasks. In this article, detailed experimental characterization of FeFET devices of different dimensions have been carried out to explicitly evaluate the non-ideal behavior in device conductance programming properties like number of programming states, cycle-to-cycle (C2C) variations, device-to-device (D2D) variations, and state retention. A hardware-aware software simulation approach has been adopted to capture the adversarial effects of the non-idealities on recognition accuracy through algorithm-level performance assessment by including them in NeuroSim, a popular neural network hardware simulator, to execute a neural network model considering all other hardware constraints. With the added non-idealities, significant accuracy degradation has been observed compared to the ideal scenarios where D2D variations play the most critical role. Thereafter, feasibility of a variation-aware training method has been evaluated to tackle the accuracy drop.

42 ENGINEERING↗
DOE OSTI2022

Understanding the Physics Representation of Deep Learning Models in Environmental Applications

Deep learning (DL) models have been popular in earth and environmental modeling and analysis, which exhibit huge potential in capturing and reconstructing the non-linearity of relevant environmental processes. They are extensively used as analytical tools or emulators for multiple domains (atmosphere, land surface, ocean, and biogeochemistry). Despite their success, their internal working mechanism remains largely unknown. Such a lack of knowledge hinders the identification of physically consistent models that are fully adaptive to non-stationary climate, as well as the development of physics-informed machine learning such as physics-informed neural network (PINN). To establish preliminary knowledge and framework of such physics representation evaluation, this project focuses on an improved understanding of DL models in the environmental applications. DL models are increasingly applied to environmental modeling and prediction. However, they have been evaluated mostly from a performance perspective, and there is a gap in understanding how they represent the known physics internally. Such knowledge is especially critical when applying DL models under climate change conditions, where new inputs are likely outside the ranges of the training datasets. In this project, we reveal how the known physical processes are represented within DL models from both statistical and mechanistic perspectives. Leveraging the traditional model evaluations that focus more on the accuracies of predictions, we establish a framework that examines both the accuracy and physics representation of DL models. This analysis framework can identify DL models that make the correct predictions based on correct physics, thus enhancing the existing explainable artificial intelligence (explainable-AI) portfolio. It lays a foundation for developing novel metrics to evaluate the emerging DL models in environmental applications. This knowledge also informs the development of physics-informed DL models by revealing the direct connections between the known physical processes and specific model components or structures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Bridging the Gap for Powering Data Centers

The rapid expansion of data centers, primarily driven by artificial intelligence, is outpacing the adaptability of the U.S. electric grid. This report, developed by Idaho National Laboratory (INL) , presents a gap analysis of some of the infrastructure challenges associated with large-scale data center deployment. Drawing from a national workshop hosted by INL in October of 2025, the report synthesizes stakeholder insights, survey data, and technical discussions to identify critical barriers and research needs. Key findings highlight the growing preference for behind-the-meter generation, the perceived inadequacy of legacy interconnection processes, and the urgent need for improved coordination between utilities, regulators, and data center developers. Environmental concerns such as water use and noise pollution, as well as economic constraints like equipment lead times and cost allocation, are also explored. The report outlines national lab capabilities in modeling, simulation, and technical assistance, and proposes targeted R&D priorities to support resilient, scalable, and efficient integration of data centers into the grid.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2008

Visualization Component of Vehicle Health Decision Support System

The visualization front-end of a Decision Support System (DSS) also includes an analysis engine linked to vehicle telemetry, and a database of learned models for known behaviors. Because the display is graphical rather than text-based, the summarization it provides has a greater information density on one screen for evaluation by a flight controller.This tool provides a system-level visualization of the state of a vehicle, and drill-down capability for more details and interfaces to separate analysis algorithms and sensor data streams. The system-level view is a 3D rendering of the vehicle, with sensors represented as icons, tied to appropriate positions within the vehicle body and colored to indicate sensor state (e.g., normal, warning, anomalous state, etc.). The sensor data is received via an Information Sharing Protocol (ISP) client that connects to an external server for real-time telemetry. Users can interactively pan, zoom, and rotate this 3D view, as well as select sensors for a detail plot of the associated time series data. Subsets of the plotted data can be selected and sent to an external analysis engine to either search for a similar time series in an historical database, or to detect anomalous events. The system overview and plotting capabilities are completely general in that they can be applied to any vehicle instrumented with a collection of sensors. This visualization component can interface with the ISP for data streams used by NASA s Mission Control Center at Johnson Space Center. In addition, it can connect to, and display results from, separate analysis engine components that identify anomalies or that search for past instances of similar behavior. This software supports NASA's Software, Intelligent Systems, and Modeling element in the Exploration Systems Research and Technology Program by augmenting the capability of human flight controllers to make correct decisions, thus increasing safety and reliability. It was designed specifically as a tool for NASA's flight controllers to monitor the International Space Station and a future Crew Exploration Vehicle.

Jacob, Joseph↗
DOE OSTI2018

ADAPTIVE SURROGATES WITHIN THE RAVEN FRAMEWORK FOR DYNAMIC PROBABILISTIC RISK ASSESSMENT ANALYSIS

The RAVEN code has been under development at the Idaho National Laboratory since 2012. Its main goal is to create a multi-purpose platform for the deploying of all the capabilities needed for Probabilistic Risk Assessment (PRA), Uncertainty Quantification (UQ), data mining analysis and optimization studies. RAVEN has demonstrated a good level of maturity in terms of state-of-art and advanced analysis methodologies. In the past year the RAVEN code has been released as open-source project and is available to download (raven.inl.gov) free of charge. The main subject of this paper is to present a new system within the RAVEN framework to drastically accelerate PRA and UQ analysis using acceleration schemes based on surrogate modeling. This system is based on the heavy usage of surrogate modeling and artificial intelligence algorithms, able to emulate the response of a physical system in closed domains (domains where the surrogate models have been constructed from). The aim of this paper is to show to the community how the employment of surrogate modeling techniques can be a viable solution for the current and future challenges in terms of PRA, UQ and Safety Margins characterizations. In addition, in this manuscript it is presented the construction of a RAVEN new entity named “HybridModel” that is aimed to switch between a high-fidelity code and its linked surrogate, accelerating the PRA and UQ processes without largely reducing the accuracy of the results of the analyses. All these new capabilities will be shown with an application example about a Station Black Out scenario for a prototypical Pressurized Water Reactor (modeled with RELAP5-3D).

97 MATHEMATICS AND COMPUTING↗