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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 1,567 records · Page 87

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NASA NTRS2009

Modeling, Detection, and Disambiguation of Sensor Faults for Aerospace Applications

Sensor faults continue to be a major hurdle for systems health management to reach its full potential. At the same time, few recorded instances of sensor faults exist. It is equally difficult to seed particular sensor faults. Therefore, research is underway to better understand the different fault modes seen in sensors and to model the faults. The fault models can then be used in simulated sensor fault scenarios to ensure that algorithms can distinguish between sensor faults and system faults. The paper illustrates the work with data collected from an electro-mechanical actuator in an aerospace setting, equipped with temperature, vibration, current, and position sensors. The most common sensor faults, such as bias, drift, scaling, and dropout were simulated and injected into the experimental data, with the goal of making these simulations as realistic as feasible. A neural network based classifier was then created and tested on both experimental data and the more challenging randomized data sequences. Additional studies were also conducted to determine sensitivity of detection and disambiguation efficacy to severity of fault conditions.

Balaban, Edward↗

NASA NTRS2014

Open Source Initiative Powers Real-Time Data Streams

Under an SBIR contract with Dryden Flight Research Center, Creare Inc. developed a data collection tool called the Ring Buffered Network Bus. The technology has now been released under an open source license and is hosted by the Open Source DataTurbine Initiative. DataTurbine allows anyone to stream live data from sensors, labs, cameras, ocean buoys, cell phones, and more.

Source record↗

NASA NTRS2017

Static Electric Fields and Lightning Over Land and Ocean in Florida Thunderstorms

Natural cloud-to-ground (CG) lightning and the charge structure of the associated clouds behave differently over land and ocean. Existing literature has raised questions over the years on the behavior of thunderstorms and lightning over oceans, and there are still open scientific questions. We expand on the observational datasets by obtaining identical electric field observations over coastal land, near-shore, and deep ocean regions during both clear air and thunderstorm periods. Oceanic observations were obtained using two 3-meter NOAA buoys that were instrumented with Campbell Scientific electric field mills to measure the static electric fields. These data were compared to selected electric field records from the existing on-shore electric field mill suite of 31 sensors at Kennedy Space Center (KSC). CG lightning occurrence times, locations and peak current values for both on-shore and ocean were provided by the U.S. National Lightning Detection Network. The buoy instruments were first evaluated on-shore at the Florida coast, to calibrate field enhancements and to confirm proper behavior of the system in elevated-field environments. The buoys were then moored 20NM and 120NM off the coast of KSC in February (20NM) and August (120NM) 2014. Statistically larger CG peak currents were reported over the deep ocean for first strokes and for subsequent strokes with new contacts points. Storm-related static fields were significantly larger at both oceanic sites, likely due to decreased screening by nearby space charge. Time-evolution of the static field during storm development and propagation indicated weak or missing lower positive charge regions in most storms that initiated over the deep ocean, supporting one mechanism for the observed high peak currents in negative first strokes over the deep ocean. This project also demonstrated the practicality of off-shore electric field measurements for safety-related decision making at KSC.

Lightning↗

NASA NTRS2017

Static Electric Fields and Lightning Over Land and Ocean in Florida Thunderstorms

Electric Fields↗

NASA NTRS2020

How Long Is Too Long? Variogram Analysis of AERONET Data to Aid Aerosol Validation and Intercomparison Studies

Geophysical data sets derived from satellite sensors, ground/airborne instrumentation, and computational models are often compared against each other. A common example is the validation of satellite aerosol optical depth (AOD) retrievals against measurements from Aerosol Robotic Network (AERONET) Sun photometers. Spatiotemporal mismatch between data set sampling means that uncaptured variation in the underlying geophysical field introduces apparent disagreement into such comparisons, known as representation or collocation matchup uncertainty. This study uses variogram analysis of AERONET data to estimate temporal mismatch uncertainties and decorrelation time scales for the global AERONET record. As well as total AOD, the fine‐ and coarse‐mode AODs, Ångström Exponent (AE), and fine‐mode fraction (FMF) of AOD are analyzed. Globally, a time difference of 30 min typically induces from 0.011–0.035 variation in AOD. For total, fine, and coarse AODs the typical time to decorrelation is around 2–10 days. For AE and FMF it is 3–33 days; that is, aerosol systems often persist significantly longer than individual events in them. Biomass burning regions tend to show the largest and fastest subdaily AOD variability and also longest times to decorrelation. Some sites show significant season‐to‐season variations in behavior. These results can be used to inform site‐specific time collocation thresholds for aerosol validation analyses and account for temporal variation when estimating data set uncertainty. They also have implications for comparisons between different satellite products or models, data aggregation, and time series analyses. Results are provided on a site‐by‐site basis to facilitate use by other researchers.

satellite↗

NASA NTRSDate not supplied

Perturbations in Brain Functional Connectivity Patterns After Waking From Slow Wave Sleep Under Different Cognitive States

Sleep inertia refers to the state of transition between sleep and wake characterized by impaired alertness, confusion, and reduced cognitive and behavioral performance. While the behavioral symptoms of sleep inertia are well described, the neurological changes that lead to this state remain elusive. Here, to understand the state of sleep inertia and the reorganization that the brain undergoes, we took a graph theoretical approach and compared the EEG derived brain connectivity patterns before sleep and after waking up while participants (n = 10) performed multiple tasks that differed in cognitive complexities. We focused on how the degree and the clustering coefficient of brain regions (EEG sensors) change immediately after participants wake up from slow wave sleep. During a psychomotor vigilance task (PVT), designed to assess vigilant attention, we find that the brain regions with strong network connectivity (degree) before sleep show a reduction in connectivity after waking. In contrast, those with low connectivity before sleep have greater connectivity after waking. The regions that undergo these changes are specific to each participant and these findings are unique to the beta frequency range, which plays a key role in sensorimotor functioning and preserving the current state of the brain. Moreover, in tasks that required inhibitory control and arithmetic reasoning, we found that only regions with weak connectivity before sleep exhibited more connections after waking, but regions with high connectivity prior to sleeping remained unchanged, highlighting task specific effects. Furthermore, we find that during the PVT, the clustering coefficient within low frequency oscillations of the brain is reduced upon waking while it remains unchanged during other tasks. These results suggest that the connections between regions that are lost after abrupt awakening can be reallocated to other regions in order to renormalize the brain. However, this response may only be evident during specific cognitive states and may be more nuanced during complex task performance.

sleep inertia↗

NASA NTRS2023

Pacific Northwest Health & Air Quality: Monitoring Trends in Air Quality During a Drought Case Study to Improve Public Health Response to Drought Threats

Recent studies have documented a correlation between air quality and drought in the United States, which has been linked with increased aerosols including airborne particulate matter (PM) during drought conditions. This study partnered with local health departments to evaluate trends in air quality in the Pacific Northwest during the evolution of drought conditions using aerosol optical depth (AOD) observations collected by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra and Aqua satellites. These satellite data were analyzed in conjunction with ground-based PM2.5 and PM10 data sourced from the Environmental Protection Agency (EPA)’s network of ground-based monitors and the Standardized Precipitation Evapotranspiration Index (SPEI) drought index. Based on recommendations by local health departments, this study examined air quality trends between 2015 and 2022 in 12 counties within Oregon and Washington that reflected diversity in population density, drought exposure, rural and urban status, and data availability from EPA monitors. Overall, results indicated variation in relationships among drought, satellite, and ground-based air quality data across the study area. This study did not control for the impact of wildfire events on air quality and also did not investigate shorter SPEI aggregation periods, both of which are avenues for future research. This project supplemented research into links between drought and human health and provided health departments with an objective foundation from which they can communicate public health risks to local communities.

Abby Sgan↗

DOE OSTI2019

Ubiquitous Traffic Volume Estimation through Machine Learning Procedure

High-quality traffic volume data is critical for transportation planning, operations, and travel-energy calculations. However, vehicle count data from roadside sensors is very sparse owing to high capital cost of installing, and on-going maintenance of electronic sensor equipment. This research effort aims to bring to market a first of its kind traffic volume data product that provides accurate estimates of traffic volumes across the entire road network for all times (24x7) and all locations (100% coverage). This product greatly improves the coverage and quality of traffic volume information by combining commercial probe traffic data with traditional traffic measurement using state-of-the-art machine learning. Commercial probe traffic data are derived from vehicles that self-report their position and speed as well as crowd sourced data from smartphone applications.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2025

Fiber-Embedded Wireless Sensors

Explore the source record for details and available documents.

1½-D Printing↗

NASA NTRS2019

"Sensor Web Evolution - Webs of Webs for NASA Science - Focus on small Uninhabited Aerial Systems (sUAS)"

This paper will describe the evolution of information collection, derivation and delivery mechanisms in webs of NASA sensor webs, with a focus on recent advancements in small Uninhabited Aerial Systems (sUAS). I will discuss the movement to "Fog Computing", also known as Edge Computing. Fog Computing facilitates the distribution of common operations and networking between edge devices and cloud computing facilities, optimizing the production of actionable intelligence. Initially, sUASs utilized onboard data collection as standard, with minimal data downloaded directly. Information products were derived in conventional computational environments, generally desk top computers, and information products made available to the Science Community in weeks or months. With the increased availability, and increasingly lower costs, of beyond line of sight (BLOS) satellite based communication, transmission rates and data volumes increased, and processing migrated to Cloud based services. Contemporary sUASs are moving some of that information product derivation to on vehicle services, and are creating a distributed Cloud/Fog environment. I will describe the technological advances that have made this possible, including low power multi-core Central Processing Units (CPU), and, more recently, the availability of high end Graphical Processing Units (GPU) that consume only a few watts. Intelligent system software, leveraging these hardware advances, finally allows for information product generation on-board, rather than simple data collection. Additionally, intelligent flight control systems now support mutual vehicle to vehicle collaboration, allowing sUASs to create ad-hoc sensor webs on demand, as required. Also discussed will be the lessons learned by the Authors' development of data systems for NASA's large High Altitude Long Endurance (HALE) UASs like Predator and Global Hawk, and how those lessons are being applied to sUAS development. This paper will focus on application, rather a deep dive into the technology, and will highlight improving data management through these new technologies.

Sensor Web↗

DOE OSTI2021

Leading/lagging cable referencing platform for monitoring the health of underground cable networks

A system for detecting water trees in branching underground electrical cables includes a pulse generator configured to inject a pulse into a first underground cable that branches into a second underground cable and a third underground cable. The system includes a first sensor associated with the first cable, a second sensor associated with the second cable, and a third sensor associated with the third cable. The system includes a control device configured to obtain a first, second, and third signal associated with the first, second and third sensors, respectively. The control device determines a lead-lag relationship between the second and third signals and determines presence of a water tree within at least one of the second and third cable based on the lead-lag relationship. When presence of a water tree is determined, the control device generates a control action associated with repairing or replacing the second and/or third cable.

Burkes, Klaehn W.↗

DOE OSTI2023

Vehicle Localization in 3D World Coordinates Using Single Camera at Traffic Intersection

Optimizing traffic control systems at traffic intersections can reduce the network-wide fuel consumption, as well as emissions of conventional fuel-powered vehicles. While traffic signals have been controlled based on predetermined schedules, various adaptive signal control systems have recently been developed using advanced sensors such as cameras, radars, and LiDARs. Among these sensors, cameras can provide a cost-effective way to determine the number, location, type, and speed of the vehicles for better-informed decision-making at traffic intersections. In this research, a new approach for accurately determining vehicle locations near traffic intersections using a single camera is presented. For that purpose, a well-known object detection algorithm called YOLO is used to determine vehicle locations in video images captured by a traffic camera. YOLO draws a bounding box around each detected vehicle, and the vehicle location in the image coordinates is converted to the world coordinates using camera calibration data. During this process, a significant error between the center of a vehicle’s bounding box and the real center of the vehicle in the world coordinates is generated due to the angled view of the vehicles by a camera installed on a traffic light pole. As a means of mitigating this vehicle localization error, two different types of regression models are trained and applied to the centers of the bounding boxes of the camera-detected vehicles. The accuracy of the proposed approach is validated using both static camera images and live-streamed traffic video. Based on the improved vehicle localization, it is expected that more accurate traffic signal control can be made to improve the overall network-wide energy efficiency and traffic flow at traffic intersections.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

EmSense: A High-Resolution Emulated Sensor for Experiments with the Smart Grid and Distributed Ledger Technology

This work involves the development of a device - EmSense (“Emulated Sensor”) - that emulates a high-resolution sensor for a power grid. The device collects raw current and voltage sensor data which derive from ORNL's signature library. This library is a dataset that ORNL curates from many different sources that include power systems from various utilities. The EmSense packages the data from the library in the form of IEC 61850 Sampled Value (SV) packets and then broadcasts these SV packets on the network. In another mode, EmSense can generate artificial sinusoidal data that appears as waveforms for voltage and current signals. EmSense has an internal algorithm for determining the period of a signal based on the data so that the period can be specified as a variable in the IEC 61850 packets. The purpose of EmSense is to allow for experimentation with the Dark Net Infrastructure where a variety of power line sensors must be represented along with their typical communication traffic. The EmSense device was developed in coordination with the software for receiving and processing the packets in the Distributed Ledger Technology (DLT) framework of the DarkNet Project. This receiving software must have a methodology for dealing with information of high velocity, variety, and volume. Experimenting with EmSense facilitates the development of such software. The results showed that the DLT framework and the trust-anchoring approach managed to process a large flow of traffic even with up to six instances of EmSense device broadcasting data. This was achieved without overfilling packet queues in the memory of the actual hardware of the DLT devices or causing the Central Processing Unit (CPU) of the hardware to be overwhelmed. The DLTs were also able to store the data in a compact and useful form for later analysis and archival purposes.

Werth, Aaron↗

DOE OSTI2026

Inverse Mapping of the Collision Kernel and Wall Flux Scaling in a Tall Convection‐Cloud Chamber Using Local Sensors and Knowledge‐Informed Deep Learning

Droplet collision–coalescence is a crucial process in cloud physics, but accurately representing this process under different dynamical conditions remains challenging. A proposed future convective‐cloud chamber aims to investigate this key process, but the method for observing it remains unclear, even though it is theoretically established that collision‐coalescence will occur. This study serves as a proof‐of‐concept demonstration of how knowledge‐informed deep learning, combined with measurement data from local sensors in the chamber, can be used to estimate the collision kernels, which determine how the droplet size distribution evolves during collision‐coalescence. In addition to estimating the collision kernel, we also address wall fluxes, another uncertain but important process that acts as a source of heat and moisture in the chamber. Ensemble runs of large‐eddy simulations are conducted by scaling the wall fluxes and the collision kernel, while the measured flow and cloud properties are used as inputs for a neural network. Results indicate that this approach successfully maps the scaling of wall fluxes and the collision kernel with biases of approximately 1% or less relative to the range of the target data. This proof‐of‐concept lays the groundwork for future applications; when the real measurements are available, real sensor data combined with the trained model presented in this work will enable estimation of the actual wall fluxes and collision kernel.

cloud chamber↗

NASA NTRS2020

High-Resolution Mapping of SO2 Using Airborne Observations from the GeoTASO Instrument During the KORUS-AQ Field Study: PCA-Based Vertical Column Retrievals

The Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) instrument is an airborne hyperspectral spectrometer measuring backscattered solar radiation in the ultraviolet (290–400 nm) and visible (415–695 nm) wavelength regions. This paper presents high-resolution sulfur dioxide (SO2) maps over the Korean Peninsula, produced by SO2 retrievals from GeoTASO measurements during the Korea–United States Air Quality Field Study (KORUS-AQ) from May to June 2016. The highly sensitive GeoTASO instrument with a spatial resolution of ~250 m × 250 m can detect point emission sources of SO2 within its fields of view, even without merging multiple overlapping observations. To retrieve SO2 vertical columns from the GeoTASO measurements, we apply an algorithm based on principal component analysis (PCA), which is effective in suppressing noise and biases in SO2 retrievals. The retrievals successfully capture SO2 plumes and various point sources such as power plants, a petrochemical complex, and a steel mill, located in South Chungcheong Province, some of which are not detected by a ground-based in situ measurement network. Spatial distributions of SO2 from GeoTASO observations in source areas are consistent with those from the Stack Tele-Monitoring System reports and airborne in situ SO2 measurements. Comparisons of SO2 retrievals from GeoTASO and existing satellite sensors demonstrate the significance of high-resolution SO2 observations, by indicating that GeoTASO detects small SO2 emission sources that are not precisely resolved by single overpasses of satellites. To assess future geostationary SO2 observations having a higher spatial resolution, we upscale the GeoTASO SO2 retrievals to a spatial resolution of the Geostationary Environment Monitoring Spectrometer (GEMS). Since the upscaled GeoTASO retrievals also detect SO2 plumes clearly, we expect from GEMS to identify even small SO2 emission sources over Asia.

Chong, Heesung↗

DOE OSTI2022

Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system

To support the reliable and resilient operation of modular reactors and microreactors, anticipatory control strategies have been proposed for achieving faster-than-real-time predictions and decision-making capabilities in anticipation of potential anomalies, including setpoint changes and cyber incidents. Here this work presents how anticipatory control strategies can be implemented via model predictive control (MPC) of a single heat pipe’s temperature. Considering the uncertainty in developing and applying MPC, this work evaluates MPC performance given three different model forms: a linear response surface model, an artificial neural network (ANN), and an autoregressive model with exogenous input (ARX). This work also evaluates the impacts of different input biases and variance on MPC performance in order to account for potential sensor reading variations due to cyber incidents. We observe that nonparametric models such as the ANN and ARX result in more fluctuated control actions compared to the MPC applied to the linear response surface model. However, when the cyber incidents are of a large magnitude, the linear response surface model produces smaller feasible regions than the nonparametric models under identical constraints.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Data from the Development Evolution of a Vehicle for Custom Control

In order to develop custom controllers intended to operate vehicles on a live highway, a series of data collection-focused tests were performed at increasing stages of complexity. Modern vehicles with features like Adaptive Cruise Control (ACC) feature a rich set of sensors and drive-by-wire mechanisms. The presented stages of data collection begins with the analysis of raw data provided by various vehicles, and eventually results in spoofing Controller Area Network (CAN) protocols for sending control commands to operate a vehicle. This paper covers the data and technical efforts needed at various stages. The raw data and tools to plot the data are also publicly available.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2026

Temporal Convolutional Network Using Empirical Mode Decomposition to Detect Faults in Grid Connected Systems

Grid-connected power electronic systems require timely and reliable fault detection to prevent equipment damage and reduce downtime. This paper presents a forecasting-based anomaly detection pipeline that decomposes voltage and current measurements into intrinsic mode functions (IMFs) using empirical mode decomposition (EMD), then trains a causal temporal convolutional network (TCN) on normal-operation IMF data to predict short-horizon future dynamics. Deviations between forecasts and observations are summarized as reliability-weighted residual scores and thresholded per sensor using robust statistics with temporal persistence constraints to suppress false positives. To reduce runtime, EMD is performed on downsampled signals for detection, while raw-rate EMD is applied only within a short region of interest for high-frequency interpretability near detected events. Results on a simulated grid-connected converter system demonstrate that IMF-domain forecasting improves anomaly separability relative to raw-signal forecasting and provides interpretable evidence of faults across decomposition channels.

Sutton, Elizabeth [ORNL] (ORCID:0009000078885935)↗