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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Edge passivated charge-coupled device image sensor

A charge-coupled device (CCD) image sensor includes in a substrate of single crystalline silicon of one conductivity type an array of a plurality of spaced, parallel channel regions of the opposite conductivity type extending along one major surface of the substrate. A plurality of parallel conductive gates are over the one major surface of the substrate and extend transversely across the channel regions. The outermost channel regions of the array are positioned adjacent edges of the substrate so that a plurality of the image sensors can be mounted in edge-to-edge relation with the channel regions of the various sensors being close together. The sensor includes passivating means between each outermost channel region and the adjacent edge to prevent charge carriers generated by the edge from being injected into the outermost channel region. The passivating means includes a highly conductive drain region of a conductivity type opposite to that of the substrate within the substrate and extending along the one major surface between the outermost channel region and the edge. Also, a highly conductive region of the same conductivity type as the substrate is on the substrate and extends along the one major surface directly at the edge.

Kosonocky, Walter F.↗
NASA NTRS1993

Method of continuously determining crack length

The determination of crack lengths in an accurate and straight forward manner is very useful in studying and preventing load created flaws and cracks. A crack length sensor according to the present invention is fabricated in a rectangular or other geometrical form from a conductive powder impregnated polymer material. The long edges of the sensor are silver painted on both sides and the sensor is then bonded to a test specimen via an adhesive having sufficient thickness to also serve as an insulator. A lead wire is connected to each of the two outwardly facing silver painted edges. The resistance across the sensor changes as a function of the crack length in the specimen and sensor. The novel aspect of the present invention includes the use of relatively uncomplicated sensors and instrumentation to effectively measure the length of generated cracks.

Prabhakaran, Ramamurthy↗
NASA NTRS1992

Elemental analyses of hypervelocity micro-particle impact sites on interplanetary dust experiment sensor surfaces

The Interplanetary Dust Experiment (IDE) had over 450 electrically active ultra-high purity metal-oxide-silicon impact detectors located on the six primary sides of the Long Duration Exposure Facility (LDEF). Hypervelocity micro-particles that struck the active sensors with enough energy to breakdown the 0.4 to 1.0 micron thick SiO2 insulator layer separating the silicon base (the negative electrode), and the 1000 A thick surface layer of aluminum (the positive electrode) caused electrical discharges that were recorded for the first year of orbit. These discharge features, which include 50 micron diameter areas where the aluminum top layer has been vaporized, facilitate the location of the impacts. The high purity Al-SiO2-Si substrates allow detection of trace (ppm) amounts of hypervelocity impactor residues. After sputtering through a layer of surface contamination, secondary ion mass spectrometry (SIMS) is used to create two-dimensional elemental ion intensity maps of micro-particle impact sites on the IDE sensors. The element intensities in the central craters of the impacts are corrected for relative ion yields and instrumental conditions and then normalized to silicon. The results are used to classify the particles' origins as 'manmade', 'natural' or 'indeterminate'. The last classification results from the presence of too little impactor residue (a frequent occurrence on leading edge impacts), analytical interference from high background contamination, the lack of information on silicon residue, the limited usefulness of data on aluminum in the central craters, or a combination of these circumstances. Several analytical 'blank' discharges were induced on flight sensors by pressing down on the sensor surface with a pure silicon shard. Analyses of these blank discharges showed that the discharge energy blasts away the layer of surface contamination. Only Si and Al were detected inside the discharge zones, including the central craters, of these features. A total of 35 impacts on leading edge sensors and 22 impacts on trailing edge sensors were analyzed.

Simon, Charles G.↗
NASA NTRSDate not supplied

Thin-Film Embedded Sensors for Battery Health Monitoring

Hybrid or all-electric aircraft are being developed as the next generation of aircraft to both allow new forms of aviation and decrease environmental impact. Since these types of aircraft are based on high-capacity battery technology, safe operation of these batteries becomes increasingly important. In particular, the potential for battery failure due to uncontrolled chemical reactions resulting in thermal runaway, catastrophic failure, and battery fires must be addressed in order for such battery technology to have the level of safety needed for standard aviation implementation. Efforts to ensure battery safety often involve engineering solutions that seek to contain rather than prevent such events by early detection. Such approaches increase the system weight and decrease the power per unit mass provided by the battery system. Existing methods for measuring battery parameters to determine the battery state-of-health are limited. These methods include electrical measurements of the cell current and/or voltage output as well as temperature measurements taken externally on the cell surface. Such external temperature measurements are limited in their ability to provide early warning of impending battery failure. In response, an effort to develop sensors operating internal to battery for health monitoring has been ongoing in the NASA Sensor-based Prognostics to Avoid Runaway Reactions & Catastrophic Ignition (SPARRCI) project. The basic approach associated with this sensor work is the deposition of thin film sensors on the battery separator located between the anode and cathode of the battery. These thin film sensors are then monitored to determine changes in battery parameters and health. Microfabrication techniques are employed to minimize the overall impact of the sensors on battery operation through the implementation of sensors with minimal size, weight, and power consumption. The thickness of the films, which are fabricated through physical vapor deposition (sputtering), are on the order of thousands of angstroms and can have minimal surface area. Thin film sensors for system health management have been implemented for a many decades on complex components for aerospace applications. However, the application of thin films of this type on a battery separator for internal battery monitoring applications has not previously been demonstrated to our knowledge. This paper describes the development of sensors for the internal battery monitoring through the use of thin film sensor technology. Thin metal films were successfully deposited on a battery separator polymer material with good adherence and electrical continuity. Multiple types of sensors have been deposited, as well as lead connections from the sensor to the edge of the separator material. The ability of these thin film sensors immersed in electrolyte to perform multiple types of battery parameter measurements has been demonstrated. For example, a multiparameter sensor system measured multiple properties simultaneously inside of a pouch cell over a wide temperature range. Further, real time measurement of interior temperature changes in a battery pouch cell with an integrated interior temperature sensor was demonstrated. These changes include detecting a fault in the battery (shorting) in situ with rapid response time (less than a minute) corresponding to a more limited response by a temperature sensor mounted externally. Other aspects of monitoring battery health were also explored, such as real-time measurement of simulated dendrite growth/metal deposition by sensor on separator material demonstrated. Future efforts will include improvements in the durability of the sensor structure to allow introduction of the approach into standard battery fabrication techniques. Overall, this work is a step forward in providing a method to prevent catastrophic battery failures and provide a foundation for safer, lighter, and higher energy batteries for the electric aircraft industry.

thin film battery health↗
NASA NTRS2006

Variations in the Sea Ice Edge and the Marginal Ice Zone on Different Spatial Scales as Observed from Different Satellite Sensor

The Marginal sea Ice Zone (MIZ) and the sea ice edge are the most dynamic areas of the sea ice cover. Knowledge of the sea ice edge location is vital for routing shipping in the polar regions. The ice edge is the location of recurrent plankton blooms, and is the habitat for a number of animals, including several which are under severe ecological threat. Polar lows are known to preferentially form along the sea ice edge because of induced atmospheric baroclinicity, and the ice edge is also the location of both vertical and horizontal ocean currents driven by thermal and salinity gradients. Finally, sea ice is both a driver and indicator of climate change and monitoring the position of the ice edge accurately over long time periods enables assessment of the impact of global and regional warming near the poles. Several sensors are currently in orbit that can monitor the sea ice edge. These sensors, though, have different spatial resolutions, different limitations, and different repeat frequencies. Satellite passive microwave sensors can monitor the ice edge on a daily or even twice-daily basis, albeit with low spatial resolution - 25 km for the Special Sensor Microwave Imager (SSM/I) or 12.5 km for the Advanced Microwave Scanning Radiometer (AMSR-E). Although special methods exist that allow the detection of the sea ice edge at a quarter of that nominal resolution (PSSM). Visible and infrared data from the Advanced Very High Resolution Radiometer (AVHRR) and from the Moderate Resolution Imaging Spectroradiometer (MODIS) provide daily coverage at 1 km and 250 m, respectively, but the surface observations me limited to cloud-free periods. The Landsat 7 Enhanced Thematic Mapper (ETM+) has a resolution of 15 to 30 m but is limited to cloud-free periods as well, and does not provide daily coverage. Imagery from Synthetic Aperture Radar (SAR) instruments has resolutions of tens of meters to 100 m, and can be used to distinguish open water and sea ice on the basis of surface and volume scattering characteristics. The Canadian RADARSAT C-band SAR provides data that cover the Arctic Ocean and the MIZ every 3 days. A change-point detection approach was utilized to obtain an ice edge estimate from the RADARSAT data The Quickscat scatterometer provides ice edge information with a resolution of a few kilometers on a near-daily basis. During portions of March and April of 2003 a series of aircraft flights were conducted over the ice edge in the Bering Sea carrying the Polarimetric Scanning Radiometer (PSR), which provides spectral coverage identical with the AMSR-E instrument at a resolution of 500 meters. In this study we investigated these different data sets and analyzed differences in their definition of the sea ice edge and the marginal ice zone and how these differences as well as their individual limitations affect the monitoring of the ice edge dynamics. We also examined how the nature of the sea ice edge, including its location, compactness and shape, changes over the seasons. Our approach was based on calculation of distances between ice edges derived from the satellite and aircraft data sets listed above as well as spectral coherence methods and shape parameters such as tortuosity, curvature, and fractional dimension.

Markus, Thorsten↗
DOE OSTI2018

Detection of electric current pulses by a fibre-optic sensor using spun fibre

We have studied distinctive features of the response of a fibre-optic current sensor with a spun-fibre sensing loop to short current pulses. It has been shown theoretically that, in a reflective interferometer, the polarisation mode dispersion (PMD) in the sensing spun fibre has no effect on the pulsed output signal of the sensor, in contrast to the PMD in a connecting fibre line. The response of the current sensor to a rectangular pulse has linear edges and its duration is determined by the relationship between the current pulse duration and the light propagation time in the spun fibre. The transition from a linear edge to the maximum sensor response is not accompanied by any transient process. The maximum response amplitude corresponds to the response to an equivalent direct current at a current pulse duration exceeding the light propagation time in the spun fibre. The present calculation results and experimental data agree well for current pulses longer than 300 ns at a light propagation time in the spun fibre of ∼1 μs. (fibre-optic sensors)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Cognition at the Point of Sensing

Over the last 15 years, compressive sensing techniques have been developed which have the potential to greatly reduce the amount of data collected by systems while preserving the amount of information obtained. A cost of this efficiency is that a computationally-intensive optimization routine must be used to put the sensed data into a form that a person can interpret. At the same time, machine learning techniques have experienced tremendous growth as well. Machines have demonstrated the ability learn how to effectively perform tasks such as detection and classification at speeds much faster than humanly possible. Our goal in this project was to study the feasibility of using compressive sensing systems "at the edge." That is, how can compressive sensing sensors be deployed such that information is created at the remote sensor rather than sending raw data to a central processing location? Studies were performed to analyze whether machine learning could be done on the compressively sensed data in its raw form. If a machine is performing the task, is it possible to do so without putting the data into a human interpretable form? We show that this is possible for some systems, in particular a compressive sensing snapshot imaging spectrometer. Machine learning tasks were demonstrated to be more effective and more robust to noise when the machine learning algorithm worked on data in its raw form. This system is shown to outperform a traditional spectrometer. Techniques for reducing the complexity of the reconstruction routine were also analyzed. Techniques for such as data regularization, deep neural networks, and matrix completion were studied and shown to have benefits over traditional reconstruction techniques. In this project we showed that compressive sensing sensors are indeed feasible at the edge. As always, sensors and algorithms must be carefully tuned to work in the constrained environment. In this project we developed tools and techniques to enable those analyses.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Towards Secure Autonomous Vehicles: An Integrated Edge and Multi-Modal Machine Learning Framework for Intrusion Detection

Autonomous vehicles (AVs) are vulnerable to cyberattacks targeting both internal communication networks and external perception sensors. While edge-based intrusion de- tection for Controller Area Network (CAN) buses offers real-time protection, it cannot detect cross-modal threats. Conversely, multi-modal fusion approaches improve coverage but often lack efficiency for in-vehicle deployment. This thesis integrates two complemen- tary solutions: (1) a lightweight, edge-deployable machine learning framework for CAN bus intrusion detection, and (2) a late-fusion system combining CAN FD and LiDAR data. Together, they form a hierarchical defense capable of handling single-modality and coordi- nated attacks. Simulations show that CAN-only models reach 93% accuracy on simulated DoS, spoofing, replay, and fuzzy attacks, while the fusion system achieves 0.87 AUC and 0.82 F1-score at 2 ms latency. This unified framework establishes a scalable, explainable, and field-ready strategy for AV cybersecurity.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2003

Single Station System and Method of Locating Lightning Strikes

An embodiment of the present invention uses a single detection system to approximate a location of lightning strikes. This system is triggered by a broadband RF detector and measures a time until the arrival of a leading edge of the thunder acoustic pulse. This time difference is used to determine a slant range R from the detector to the closest approach of the lightning. The azimuth and elevation are determined by an array of acoustic sensors. The leading edge of the thunder waveform is cross-correlated between the various acoustic sensors in the array to determine the difference in time of arrival, AT. A set of AT S is used to determine the direction of arrival, AZ and EL. The three estimated variables (R, AZ, EL) are used to locate a probable point of the lightning strike.

Medelius, Pedro J.↗
DOE OSTI2022

Analyzing Data Privacy for Edge Systems

Internet-of-Things (IoT)-based streaming applications are all around us. Currently, we are transitioning from IoT processing being performed on the cloud to the edge. While moving to the edge provides significant networking efficiency benefits, IoT edge computing creates significant data privacy concerns. We propose a methodology that can successfully privacy protect the continual data streams generated by sensors on the edge device. We implement local differential privacy on streaming data and incorporate Bayesian inference and Gaussian process to evaluate the privacy policy. We demonstrate our methodology on a real-world smart meter testbed and identify the optimal privacy protection settings.

Kotevska, Olivera↗
DOE OSTI2018

Design of the first full size ATLAS ITk strip sensor for the endcap region

The ATLAS collaboration is designing the all-silicon Inner Tracker (ITk) that will operate in the HL-LHC replacing the current design. The silicon microstrip sensors for the barrel and the endcap regions in the ITk are fabricated in 6 inch, p-type, float-zone wafers, where large-area strip sensor designs are laid out together with a number of miniature sensors. The radiation tolerance and specific system issues like the need for slim edge of 450 μm have been tested with square shaped sensors intended for the barrel part of the tracker. This work presents the design of the first full size silicon microstrip sensor for the endcap region with a slim edge of 450 μm. The strip endcaps will consist of several wheels with two layers of silicon strip sensors each. The strips have to lie along the azimuthal direction, apart from a small stereo angle rotation (20 mrad on each side, giving 40 mrad total) for measuring the second coordinate of tracks. This stereo angle is built into the strip layout of the sensor and, in order to avoid orphan strips, the sensor edges are inclined by the stereo angle. On top of this, the top and bottom edges are designed as arcs to have equal length strips. Together with the design of this new Stereo Annulus sensor, we will report on the initial measurements of the leakage current as a function of bias voltage, after dicing, and the depletion voltage.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Machine learning without a processor: Emergent learning in a nonlinear analog network

Standard deep learning algorithms require differentiating large nonlinear networks, a process that is slow and power-hungry. Electronic contrastive local learning networks (CLLNs) offer potentially fast, efficient, and fault-tolerant hardware for analog machine learning, but existing implementations are linear, severely limiting their capabilities. These systems differ significantly from artificial neural networks as well as the brain, so the feasibility and utility of incorporating nonlinear elements have not been explored. Here, we introduce a nonlinear CLLN—an analog electronic network made of self-adjusting nonlinear resistive elements based on transistors. We demonstrate that the system learns tasks unachievable in linear systems, including XOR (exclusive or) and nonlinear regression, without a computer. We find our decentralized system reduces modes of training error in order (mean, slope, curvature), similar to spectral bias in artificial neural networks. The circuitry is robust to damage, retrainable in seconds, and performs learned tasks in microseconds while dissipating only picojoules of energy across each transistor. This suggests enormous potential for fast, low-power computing in edge systems like sensors, robotic controllers, and medical devices, as well as manufacturability at scale for performing and studying emergent learning.

Science & Technology - Other Topics↗
DOE OSTI2022

DRIPS: Dynamic Rebalancing of Pipelined Streaming Applications on CGRAs

Coarse-grained reconfigurable arrays (CGRAs) provide higher flexibility than application-specific integrated circuits (ASICs) and higher efficiency than fine-grained reconfigurable devices such as Field Programmable Gate Arrays (FPGAs). However, CGRAs are generally designed to support offloading of a single kernel. While their design, based on communicating functional units, appears to naturally suit data streaming applications composed of multiple cooperating kernels, current approaches only statically partition the resources across application kernels. However, emerging streaming applications at the edge (scientific instruments, sensor networks, network processing) perform much more than digital signal processing and often are data and input dependent. This leads to extremely variable kernel execution times, severely impacting the throughput of the entire pipeline if resources are only statically allocated. Therefore, in this paper, we propose DRIPS — a coarse-grained, dynamically, and partially reconfigurable array for data-dependent streaming applications. We present a unified compiler framework to facilitate the mapping of a given streaming application onto the DRIPS CGRA architecture. The experimental results show that DRIPS achieves an average throughput improvement of 1.46$\times$ across a set of representative applications over a statically partitioned solution. The additional area overhead to enable dynamic rebalancing consumes 16.34% of the entire area for a 5x5 CGRA prototype.

Tan, Cheng↗
DOE OSTI2023

Ultra: Underwater Laser Telecommunications & Remote Access (Final Report)

High speed wireless communication has proven elusive in subsea environments due to the inherent bandwidth limitations of acoustics and range limitations of other transmission modalities. A truly connected subsea system necessitates a high-speed, resilient architecture that can enable the integration of new sensor technologies and edge analytics and allow closed-loop monitoring and control of subsea operations for integrity monitoring and optimization. Like terrestrial Internet of Things applications, the realization of this “digital subsea” vision requires the application of high speed, point-to-point wireless technologies to complement rather than replace “hard-wired” communications such as optical fiber or acoustic systems. This work addresses the development of ULTRA (Underwater LASER Telemetry and Remote Access), an ultra-long range underwater laser communications system for use in critical points of the subsea communications architecture to increase reliability, operational flexibility, and reduce communication system maintenance associated with physical subsea connections.

02 PETROLEUM↗
DOE OSTI2023

Human-Building Collaboration: Toward lighting enabled collaborative system design

The profession of lighting design is evolving as contemporary lighting systems increasingly rely on cutting-edge computational technologies, sensors, and IOT systems. This trend requires designers to incorporate ideas of automation, dynamic controls, and user-system interaction into their design logic. However, real-time lighting simulations are constrained by inherent limitations arising from the reductive assumptions inevitably introduced in simulated lighting environments. This raises the question of how can designers account for the discrepancies between simulated and real lighting environments, and how can collaboration between humans and autonomous lighting systems bridge this gap. To address this question, we propose a protocol for designing collaborative interactions between humans and systems. Furthermore, this protocol builds on the Human-Lighting System Interaction Framework and demonstrates how human and system intelligence can be combined to fine-tune lighting qualities in a given space. Our paper shows how interactive lighting systems can customize lighting based on user preferences in real-time and how global lighting configurations can be adjusted over time. Specifically, we demonstrate: a) Human-system collaboration assumptions and goals, as well as how the protocol can be integrated into digitally programmable lighting systems. b) Implementations of collaboration that reveal how system autonomy, performance, and user experience are improved over short and long-term timeframes. c) How lighting design can be enhanced beyond simulation-driven design optimization capacities. The associated affordances and limitations are discussed with respect to existing lighting simulation design frameworks.

autonomous systems↗
NASA NTRS1988

Synchronous Photodiode-Signal Sampler

Synchronous sampling circuit increases signal-to-noise ratio of measurements of chopped signal of known phase and frequency in presence of low-frequency or dc background noise. Used with linear array of photoelectric sensors for locating edge of metal plate. Multiplexing circuit cycles through 16 light-emitting-diode/photodiode pairs, under computer control. Synchronized with multiplexer so edge detector makes one background-subtracted signal measurement per emitter/detector pair in turn.

Primus, Howard K.↗
NASA NTRS2005

Magnetoresistive Flux Focusing Eddy Current Flaw Detection

A giant magnetoresistive flux focusing eddy current device effectively detects deep flaws in thick multilayer conductive materials. The probe uses an excitation coil to induce eddy currents in conducting material perpendicularly oriented to the coil s longitudinal axis. A giant magnetoresistive (GMR) sensor, surrounded by the excitation coil, is used to detect generated fields. Between the excitation coil and GMR sensor is a highly permeable flux focusing lens which magnetically separates the GMR sensor and excitation coil and produces high flux density at the outer edge of the GMR sensor. The use of feedback inside the flux focusing lens enables complete cancellation of the leakage fields at the GMR sensor location and biasing of the GMR sensor to a location of high magnetic field sensitivity. In an alternate embodiment, a permanent magnet is positioned adjacent to the GMR sensor to accomplish the biasing. Experimental results have demonstrated identification of flaws up to 1 cm deep in aluminum alloy structures. To detect deep flaws about circular fasteners or inhomogeneities in thick multi-layer conductive materials, the device is mounted in a hand-held rotating probe assembly that is connected to a computer for system control, data acquisition, processing and storage.

Russell A Wincheski↗