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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Flexible visualization of a 3rd party Intrusion Prevention (Security) tool: A use case with the ELK stack

A difficult aspect of cyber security is the ability to achieve automated real time intrusion prevention across various sets of systems. To this extent, several companies are offering comprehensive solutions that leverage an "accuracy of scale" and moving much of the intelligence and detection on the Cloud, relying on an ever-growing set of data and analytics to increase decision accuracy. Often, they provide tools to visualize the decision workflows in attack prevention (as well as tune the algorithm) but those solutions are not always practical as companies see the problem as "global" that is, from a unified Cyber-security standpoint. However, a key to a successful Cyber-security program is transparency and trust: from an experimental team viewpoint, this specifically means having the ability to immediately see what and from where, who has been blocked and being able to inform the community in case of a revoked access without the need for filing a "ticket" (that may eventually be answered) – in other words, rapid response to their user-base is essential but solutions targeting "sub-groups" in an organization are not often available. We have come up with a versatile solution leveraging the ELK stack (Elasticsearch, Logstash, & Kibana) and an IPS (Intrusion Prevention System) based WAF (Web Application Firewall) from Signal Sciences. Signal Science allows the streaming of detailed logs in a Logstash format suitable for custom solutions for visualization. By combining these two tools, we have strengthened our security posture and enabled individual experiments to monitor their own traffic. Specifically, the IPS WAF provides unique data such as country of origin, protocol, response code, source IP, and paths accessed. In this contribution, we will show how we engineered a visualization solution so experiment groups could access a dashboard with predefined graphs but also, where they can create individual customizable dashboards used to display blocked traffic and troubleshoot latency issues. We will discuss the details and procedures for developing and configuring these tools and how it benefits cyber security postures across our scientific based environment.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Energy Delivery Systems with Verifiable Trustworthiness (Final Report)

Energy Delivery Systems (EDS) must be verified to be free from intrusive and malicious software. One way of verifying this software is to perform device scans to detect malicious code. Because it is possible to have “fileless” malware that exists only in device (volatile) memory, offline scanning and even many forms of online scanning is insufficient for detection. This project (“Verify”) addresses this need by performing direct sampling of memory during device operation to detect unexpected or modified software while not interfering with device operation. The Verify project provides a proof-of-concept of detection by random sampling combined with remote software- and timing-based attestation methods for robust detection of in-memory threats. An external review of Verify was performed by our partner, General Electric (GE), and a summary of their findings is provided.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2017

Thermal Inspection of a Composite Fuselage Section Using a Fixed Eigenvector Principal Component Analysis Method

A composite fuselage aircraft forward section was inspected with flash thermography. The fuselage section is 24 feet long and approximately 8 feet in diameter. The structure is primarily configured with a composite sandwich structure of carbon fiber face sheets with a Nomex(Trademark) honeycomb core. The outer surface area was inspected. The thermal data consisted of 477 data sets totaling in size of over 227 Gigabytes. Principal component analysis (PCA) was used to process the data sets for substructure and defect detection. A fixed eigenvector approach using a global covariance matrix was used and compared to a varying eigenvector approach. The fixed eigenvector approach was demonstrated to be a practical analysis method for the detection and interpretation of various defects such as paint thickness variation, possible water intrusion damage, and delamination damage. In addition, inspection considerations are discussed including coordinate system layout, manipulation of the fuselage section, and the manual scanning technique used for full coverage.

Zalameda, Joseph N.↗
DOE OSTI2023

Harmonic Signals Dataset

The Harmonic Signals Dataset (HSD) consists of one-dimensional time series data collected from current sensors with a goal of providing data for research and development of Non-Intrusive Load Monitoring (NILM) with high sample rate (800kHz) sensors. NILM seeks to detect and characterize electrical equipment operating within a facility by sensing changes in electrical current on the building power system associated with the equipment. To provide data with a known ground truth, but with the realism of a signal introduced within a building facility, a series of known ground truth signals were injected as voltage into the power system of a building and measurements of the signals at multiple locations across the building power system were made with electrical current sensors.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Advancing Industry 4.0: Multimodal Sensor Fusion for AI-Based Fault Detection in 3D Printing

Additive manufacturing, particularly fused deposition modeling, is transforming modern production by enabling rapid prototyping and complex part fabrication. However, its layer-by-layer process remains vulnerable to faults such as nozzle clogging, filament runout, and layer misalignment, which compromise print quality and reliability. Traditional inspection methods are costly, time-intensive, and often limited to post-process analysis, making them unsuitable for real-time intervention. In this current study, the authors developed a novel, low-cost, and portable faultdetection system that leverages multimodal sensor fusion and artificial intelligence for real-time monitoring in FDM-based 3D printing. The system integrates acoustic, vibration, and thermal sensing into a non-intrusive architecture, capturing complementary data streams that reflect both mechanical and process-related anomalies. Acoustic and thermal sensors operate in a fully contactless manner, while the vibration sensor requires minimal attachment such that it will not interfere with printer hardware, thereby preserving portability and ease of deployment. The multimodal signals are processed into spectrograms and time-frequency features, which are classified using convolutional neural networks for intelligent fault detection. The proposed system advances Industry 4.0 objectives by offering an affordable, scalable, and practical monitoring solution that improves faultdetection accuracy, reduces waste, and supports sustainable, adaptive manufacturing.

42 ENGINEERING↗
DOE OSTI2026

Ensemble Federated Machine Learning‐Based Cybersecurity Situational Awareness in Microgrid Network

Cyber-physical microgrids are vulnerable to stealthy cybersecurity threats that disguise their actions through the exploitation of system knowledge. Such actions can severely impacts microgrids deployed in defense bases, slowing the response time of military forces during national emergencies. Several machine-learning algorithms have been proposed to detect intrusions in the grid networks; however, these traditional machine-learning algorithms lack data privacy and are subject to several adversarial machine-learning threats. This paper proposes a novel federated machine learning (FML)-based three-model framework to detect and identify stealthy data-integrity attacks while ensuring data privacy in microgrid networks. The proposed architecture uses a variational mode decomposition technique to extract derived features from incoming measurement and control datasets. The extraction of these derived features allows FML models to learn minute variations in data patterns that allow them to perform significantly better than the models trained with generic datasets consisting of raw features. Our experimental results show the efficient performance of the proposed methodology against different types of data integrity attacks while considering primary and secondary controllers in microgrids. Further, the applied FML-integrated random forest ensemble algorithm outperforms the existing generic FML algorithms during noisy and noise-free datasets with prediction latencies of only 91–134 µs per sample within the 0.1 s sampling interval and requires communication bandwidth of around ∼8.25 KB/s at the control center and ∼2.7 KB/s per edge client for communication.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2005

Technology That's Ready and Able to Inspect Those Cables

Attempting to locate a malfunctioning wire in a complex bundle of wires or in a cable that is concealed behind a wall is as difficult as trying to find a needle in a haystack. The result of such an effort can also be costly, time-consuming, and frustrating, whether it is the tedious process of examining cable connections for the Space Shuttle or troubleshooting a cable television hookup. Furthermore, other maintenance restrictions can compound the effort required to locate and repair a particular wiring problem. For example, on the Space Shuttle, once a repair is completed, all systems that have a wire passing through any of the connectors that were disconnected during troubleshooting are affected and, therefore, must undergo retesting, an arduous task that is completely unrelated to the original problem. In an effort to streamline wire inspection and maintenance, two contractors supporting NASA's Kennedy Space Center invented the Standing Wave Reflectometer (SWR) in 1999. In doing so, they leveraged technology that was first developed to detect problems that could lead to aircraft accidents, such as the one that resulted in the catastrophic failure of TWA flight 800 in 1996. The SWR performs a non-intrusive inspection that verifies the condition of electrical power and signal-distribution systems inside the Space Shuttle orbiters. Such testing reduces processing delays and ensures safe operation of these systems.

Source record↗
NASA NTRS2012

NASA Tech Briefs, May 2012

Topics covered include: An "Inefficient Fin" Non-Dimensional Parameter to Measure Gas Temperatures Efficiently; On-Wafer Measurement of a Multi-Stage MMIC Amplifier with 10 dB of Gain at 475 GHz; Software to Control and Monitor Gas Streams; Miniaturized Laser Heterodyne Radiometer (LHR) for Measurements of Greenhouse Gases in the Atmospheric Column; Anomaly Detection in Test Equipment via Sliding Mode Observers; Absolute Position of Targets Measured Through a Chamber Window Using Lidar Metrology Systems; Goldstone Solar System Radar Waveform Generator; Fast and Adaptive Lossless Onboard Hyperspectral Data Compression System; Iridium Interfacial Stack - IrIS; Downsampling Photodetector Array with Windowing; Optical Phase Recovery and Locking in a PPM Laser Communication Link; High-Speed Edge-Detecting Line Scan Smart Camera; Optical Communications Channel Combiner; Development of Thermal Infrared Sensor to Supplement Operational Land Imager; Amplitude-Stabilized Oscillator for a Capacitance-Probe Electrometer; Automated Performance Characterization of DSN System Frequency Stability Using Spacecraft Tracking Data; Histogrammatic Method for Determining Relative Abundance of Input Gas Pulse; Predictive Sea State Estimation for Automated Ride Control and Handling - PSSEARCH; LEGION: Lightweight Expandable Group of Independently Operating Nodes; Real-Time Projection to Verify Plan Success During Execution; Automated Performance Characterization of DSN System Frequency Stability Using Spacecraft Tracking Data; Web-Based Customizable Viewer for Mars Network Overflight Opportunities; Fabrication of a Cryogenic Terahertz Emitter for Bolometer Focal Plane Calibrations; Fabrication of an Absorber-Coupled MKID Detector; Graphene Transparent Conductive Electrodes for Next- Generation Microshutter Arrays; Method of Bonding Optical Elements with Near-Zero Displacement; Free-Mass and Interface Configurations of Hammering Mechanisms; Wavefront Compensation Segmented Mirror Sensing and Control; Long-Life, Lightweight, Multi-Roller Traction Drives for Planetary Vehicle Surface Exploration; Reliable Optical Pump Architecture for Highly Coherent Lasers Used in Space Metrology Applications; Electrochemical Ultracapacitors Using Graphitic Nanostacks; Improved Whole-Blood-Staining Device; Monitoring Location and Angular Orientation of a Pill; Molecular Technique to Reduce PCR Bias for Deeper Understanding of Microbial Diversity; Laser Ablation Electrodynamic Ion Funnel for In Situ Mass Spectrometry on Mars; High-Altitude MMIC Sounding Radiometer for the Global Hawk Unmanned Aerial Vehicle; PRTs and Their Bonding for Long-Duration, Extreme-Temperature Environments; Mid- and Long-IR Broadband Quantum Well Photodetector; 3D Display Using Conjugated Multiband Bandpass Filters; Real-Time, Non-Intrusive Detection of Liquid Nitrogen in Liquid Oxygen at High Pressure and High Flow; Method to Enhance the Operation of an Optical Inspection Instrument Using Spatial Light Modulators; Dual-Compartment Inflatable Suitlock; Large-Strain Transparent Magnetoactive Polymer Nanocomposites; Thermodynamic Vent System for an On-Orbit Cryogenic Reaction Control Engine; Time Distribution Using SpaceWire in the SCaN Testbed on ISS; and Techniques for Solution- Assisted Optical Contacting.

Source record↗
DOE OSTI2025

Cognitive IoT and Edge Computing for Intrusion Detection with Federated TinyML

Internet of Things (IoT) and Edge Computing (EC) are rapidly becoming an integral part of the modern society. By 2030, there is estimated to be over 40 billion active and connected IoT devices [1]. This rapid progress also comes with a significant implication on cybersecurity. Back-end infrastructure and systems have a much broader attack than they did previously due to vulnerable IoT/EC devices being connected to wireless networks. This expanding attack surface is a growing concern because IoT/EC are increasingly being used in critical systems such as power grids, health care, and smart homes. To effectively address a problem of this scale, cognitive cyber methods—which can autonomously detect and react to cyber attacks as they develop—are needed. To address this, we bring Artificial Intelligence (AI) and Machine Learning (ML) to IoT/EC devices, using tinyML to monitor voluminous IoT data against cyber threats, and using Federated Learning (FL) to share local detection knowledge across the system while preserving privacy. We propose a novel three-layer architecture: (1) an IoT layer for tinyML-based inference, (2) an edge layer for ML model training, and (3) a cloud layer for FL operations. Using the publicly available 11-class N-BaIoT dataset [2], we demonstrate that this architecture mitigates resource constraints at the IoT layer while improving detection accuracy over standard two-layer designs. An outlier-resistant scaler, feature reduction, and quantization enable the tinyML model to maintain detection accuracy with a reduced model size. Additionally, federated learning that only utilizes the intersection (across heterogenous devices) of the reduced feature set achieves superior detection accuracy compared to locally trained models.

Li, Mingyan [ORNL] (ORCID:0009000569532640)↗
NASA NTRS2017

Dust and Debris Tolerant Retractable Cover Connector

A debris exclusion and removal apparatus for connectors which have retractable cover configurations which include internal wafers that clean the connectors prior to mating. XXXX connectors. More particularly, embodiments relate to dust tolerant connectors. Some embodiments also relate to an intelligent connector system capable of detecting damage to or faults within a conductor and then rerouting the energy to a non-damaged spare conductor. Discussion Connectors of the present invention may be used to transfer electrical current, fluid, and gas in a wide variety of environments containing dust and other debris, wherein that debris may present substantial challenges. For example, lunar/Martian dust intrusion and/or accumulation in connectors used to transfer oxygen, hydrogen, nitrogen, etc., may lead to larger system failures as well as loss of life in extraterrestrial human exploration endeavors. Additionally, embodiments of the present invention may also be suitable for use where connectors must resist water intrusion, such as terrestrial deep water operations.

Lewis, Mark E.↗
DOE OSTI2023

Digital holography for real-time non-invasive monitoring of larval fish at power plant intakes

Effective evaluation of technological and operational approaches to reduce entrainment of marine organisms at cooling water intake structures (CWIS) requires accurate organism-sensing systems. Current detection methods lead to large temporal data gaps, require tedious manual analysis, and are fatal to organisms. In this report we describe integrating deep learning with a non-lethal, non-intrusive imaging method—digital holography—to rapidly detect fish larvae. Laboratory experiments demonstrated that the instrument could successfully image fish larvae at flow rates exceeding ranges seen in CWIS. Holograms of two fish larvae species, in the presence of bubbles and detritus, were collected to build a large database for training a lightweight convolutional neural network. The model achieves 97% extraction accuracy in quantifying larvae, and distinguishing them from other particles, including detritus and bubbles, when applied to a dataset of manually classified images, exceeding previous metrics for non-lethal, accurate, and real-time detection. These results demonstrate the potential of in situ holographic imaging for monitoring endangered larval fish species at power plant intake structures, and for high-fidelity, real-time applications in monitoring aquatic ichthyoplankton.

endangered species↗
NASA NTRS1999

Methods of Viscosity Measurements in Sealed Ampoules

Viscosity of semiconductor and metallic melts is usually measured by oscillating cup method. This method utilizes the melts contained in vacuum sealed silica ampoules, thus the problems related to volatility, contamination, and high temperature and pressure can be alleviated. In a typical design, the time required for a single measurement is of the order of one hour. In order to reduce this time to a minute range, a high resolution (0.05 arc.sec) angular detection system is implemented in our design of the viscometer. Furthermore, an electromagnet generating a rotational magnetic field (RMF) is incorporated into the apparatus. This magnetic field can be used to remotely and non intrusively measure the electrical conductivity of the melt. It can also be used to induce a well controlled rotational flow in the system. The transient behavior of this flow can potentially yield the viscosity of the fluid. Based on RMF implementation, two novel viscometry methods are proposed in this work: a) the transient torque method, b) the resonance method. A unified theoretical approach to the three methods (oscillating cup, transient torque, and resonance) is presented along with the initial test results of the constructed apparatus. Advantages of each of the method are discussed.

Mazuruk, Konstantin↗
DOE OSTI2020

Distributed fiber sensor and machine learning data analytics for pipeline protection against extrinsic intrusions and intrinsic corrosions

This paper presents an integrated technical framework to protect pipelines against both malicious intrusions and piping degradation using a distributed fiber sensing technology and artificial intelligence. A distributed acoustic sensing (DAS) system based on phase-sensitive optical time-domain reflectometry (φ-OTDR) was used to detect acoustic wave propagation and scattering along pipeline structures consisting of straight piping and sharp bend elbow. Signal to noise ratio of the DAS system was enhanced by femtosecond induced artificial Rayleigh scattering centers. Data harnessed by the DAS system were analyzed by neural network-based machine learning algorithms. The system identified with over 85% accuracy in various external impact events, and over 94% accuracy for defect identification through supervised learning and 71% accuracy through unsupervised learning.

Peng, Zhaoqiang↗
DOE OSTI2022

Infrared-Fused Vision-Based Thermoregulation Performance Estimation for Personal Thermal Comfort-Driven HVAC System Controls

Thermal comfort is one of the primary factors influencing occupant health, well-being, and productivity in buildings. Existing thermal comfort systems require occupants to frequently communicate their comfort vote via a survey which is impractical as a long-term solution. Here, we present a novel thermal infrared-fused computer vision sensing method to capture thermoregulation performance in a non-intrusive and non-invasive manner. In this method, we align thermal and visible images, detect facial segments (i.e., nose, eyes, face boundary), and accordingly read the temperatures from the appropriate coordinates in the thermal image. We focus on the human face since it is often clearly visible to cameras and is not merged into a hot background (unlike hands). We use a regularized Gaussian Mixture model to track the thermoregulation changes over time and apply a heuristic algorithm to extract hot and cold indices. We present a personalized and a generalized comfort modeling method, selected based on the availability of the occupant historical indices measurements in a neutral environment, and use the time-series of the hot and cold indices to define corrections to HVAC system operations in the form of setpoint constraints. To evaluate the efficacy of our proposed approach in responding to thermal stimuli, we designed a series of controlled experiments to simulate exposure to cold and hot environments. While applying personalized modeling showed an acceptable average accuracy of 91.3%, the generalized model’s average accuracy was only 65.2%. This shows the importance of having access to physiological records in modeling and assessing comfort. We also found that individual differences should be considered in selecting the cooling and heating rates when some knowledge of the occupant’s overall thermal preference is available.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1997

Real Time Quantitative 3-D Imaging of Diffusion Flame Species

A low-gravity environment, in space or ground-based facilities such as drop towers, provides a unique setting for study of combustion mechanisms. Understanding the physical phenomena controlling the ignition and spread of flames in microgravity has importance for space safety as well as better characterization of dynamical and chemical combustion processes which are normally masked by buoyancy and other gravity-related effects. Even the use of so-called 'limiting cases' or the construction of 1-D or 2-D models and experiments fail to make the analysis of combustion simultaneously simple and accurate. Ideally, to bridge the gap between chemistry and fluid mechanics in microgravity combustion, species concentrations and temperature profiles are needed throughout the flame. However, restrictions associated with performing measurements in reduced gravity, especially size and weight considerations, have generally limited microgravity combustion studies to the capture of flame emissions on film or video laser Schlieren imaging and (intrusive) temperature measurements using thermocouples. Given the development of detailed theoretical models, more sophisticated studies are needed to provide the kind of quantitative data necessary to characterize the properties of microgravity combustion processes as well as provide accurate feedback to improve the predictive capabilities of the computational models. While there have been a myriad of fluid mechanical visualization studies in microgravity combustion, little experimental work has been completed to obtain reactant and product concentrations within a microgravity flame. This is largely due to the fact that traditional sampling methods (quenching microprobes using GC and/or mass spec analysis) are too heavy, slow, and cumbersome for microgravity experiments. Non-intrusive optical spectroscopic techniques have - up until now - also required excessively bulky, power hungry equipment. However, with the advent of near-IR diode lasers, the possibility now exists to obtain reactant and product concentrations and temperatures non-intrusively in microgravity combustion studies. Over the past ten years, Southwest Sciences has focused its research on the high sensitivity, quantitative detection of gas phase species using diode lasers. Our research approach combines three innovations in an experimental system resulting in a new capability for nonintrusive measurement of major combustion species. FM spectroscopy or high frequency Wavelength Modulation Spectroscopy (WMS) have recently been applied to sensitive absorption measurements at Southwest Sciences and in other laboratories using GaAlAs or InGaAsP diode lasers in the visible or near-infrared as well as lead-salt lasers in the mid-infrared spectral region. Because these lasers exhibit essentially no source noise at the high detection frequencies employed with this technique, the achievement of sensitivity approaching the detector shot noise limit is possible.

Kane, Daniel J.↗
DOE OSTI2022

Cyber-Physical Resiliency for Wind Power Generation

During the next three years, wind power generation is expected to add more generation capacity to the national electrical grid than any other energy sector. With new wind turbine designs rated power over 10 MW and wind farms reaching over 1 GW capacities, the consequences of cyber-attacks on wind power generation are becoming increasingly more critical. Moreover, the known vulnerabilities of wind turbine control systems and the potential damaging effects of intrusions, motivate an urgent protection improvement for the wind power generation sector. This program has developed a variety of new adaptive defense technologies that enable wind power generation systems to survive sophisticated cyberattacks by enhancing the control systems capabilities of detection, localization, and accommodation. The introduction of these technologies in the on-shore and the emerging off-shore market will result in a significantly more reliable and secure wind power infrastructure.

17 WIND ENERGY↗
NASA NTRS1975

Variational optimization analysis of temperature and moisture advection in a severe storm environment

Horizontal wind components, potential temperature, and mixing ratio fields associated with a severe storm environment in the south central U.S. were analyzed from synoptic upper air observations with a nonhomogeneous, anisotropic weighting function. Each data field was filtered with variational optimization analysis techniques. Variational optimization analysis was also performed on the vertical motion field and was used to produce advective forecasts of the potential temperature and mixing ratio fields. Results show that the dry intrusion is characterized by warm air, the advection of which produces a well-defined upward motion pattern. A corresponding downward motion pattern comprising a deep vertical circulation in the warm air sector of the low pressure system was detected. The axes alignment of maximum dry and warm advection with the axis of the tornado-producing squall line also resulted.

Mcfarland, M. J.↗