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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 1,297 records · Page 72

Substrate-Wide Confined Shear Alignment of Carbon Nanotubes for Thin Film Transistors

To exploit their charge transport properties in transistors, semiconducting carbon nanotubes must be assembled into aligned arrays comprised of individualized nanotubes at optimal packing densities. However, achieving this control on the wafer-scale is challenging. Here, solution-based shear in substrate-wide, confined channels is investigated to deposit continuous films of well-aligned, individualized, semiconducting nanotubes. Polymer-wrapped nanotubes in organic ink are forced through sub-mm tall channels, generating shear up to 10 000 s -1 uniformly aligning nanotubes across substrates. The ink volume and concentration, channel height, and shear rate dependencies are elucidated. Optimized conditions enable alignment within a ±32° window, at 50 nanotubes µm-1, on 10 × 10 cm 2 substrates. Transistors (channel length of 1–5 µm) are fabricated parallel and perpendicular to the alignment. The parallel transistors perform with 7× faster charge carrier mobility (101 and 49 cm 2 V -1 s -1 assuming array and parallel-plate capacitances, respectively) with high on/off ratio of 105. The spatial uniformity varies ±10% in density, ±2° in alignment, and ±7% in mobility. Deposition occurs within seconds per wafer, and further substrate scaling is viable. Compared to random networks, aligned nanotube films promise to be a superior platform for applications including sensors, flexible/stretchable electronics, and light emitting and harvesting devices.

36 MATERIALS SCIENCE↗

Distributed Acoustic Sensing to Estimate the Permeability

Optical fiber in a borehole can be interrogated with distributed acoustic sensors (DAS) to capture fracture displacements with the potential to map surrounding fracture networks. We designed a laboratory experiment to test the capability of DAS to determine borehole flow characteristics, and we show that for the first time DAS can be used to remotely estimate permeability. Optical fiber was wrapped around a bead filled pipe and the pressure drop and flow velocity were measured to directly calculate permeability. A machine learning model using statistical features from continuous DAS estimated the bulk permeability. Fluid interactions with the permeable material demonstrate insufficient resolution using DAS amplitude-based measurements for estimating pressure drop to infer permeability. Variations in the spectral domain relate DAS measurements to the pressure drop and provide consistent permeability estimates. Resolution with DAS is sufficient to estimate permeability and provides a reliable method to monitor at depth in borehole conditions.

58 GEOSCIENCES↗

The Skylab communications system.

Skylab is an experimental manned space station which will be operated by three astronauts intermittently over eight months in 1973. It will be in a 235 nautical mile, 50 degree inclination orbit. The communication system includes an onboard audio distribution hard-line network with 13 stations; a teleprinter for recording messages from the ground; telemetry, voice and television links; an onboard video distribution network. The radio frequencies system also includes radiometer and scatterometer earth-scanning sensors operating at 13.9 GHz and 1.4 GHz. The overall Skylab Program is discussed briefly and the communications system is described in more detail.

Disher, J. H.↗

Signal conditioner for nickel temperature sensors

Simple circuit conditions output of 50 ohm sensor for readout on straingage recorder. It consists of resistors, switch, and "matching" network. Device saves time and reduced instrumentation costs when strain and temperature are measured in same setup.

Walker, R. R.↗

Multivariate Statistical Inference of Lightning Occurrence, and Using Lightning Observations

Two classes of multivariate statistical inference using TRMM Lightning Imaging Sensor, Precipitation Radar, and Microwave Imager observation are studied, using nonlinear classification neural networks as inferential tools. The very large and globally representative data sample provided by TRMM allows both training and validation (without overfitting) of neural networks with many degrees of freedom. In the first study, the flashing / or flashing condition of storm complexes is diagnosed using radar, passive microwave and/or environmental observations as neural network inputs. The diagnostic skill of these simple lightning/no-lightning classifiers can be quite high, over land (above 80% Probability of Detection; below 20% False Alarm Rate). In the second, passive microwave and lightning observations are used to diagnose radar reflectivity vertical structure. A priori diagnosis of hydrometeor vertical structure is highly important for improved rainfall retrieval from either orbital radars (e.g., the future Global Precipitation Mission "mothership") or radiometers (e.g., operational SSM/I and future Global Precipitation Mission passive microwave constellation platforms), we explore the incremental benefit to such diagnosis provided by lightning observations.

Boccippio, Dennis↗

Geometrical approach to neural net control of movements and posture

In one approach to modeling brain function, sensorimotor integration is described as geometrical mapping among coordinates of non-orthogonal frames that are intrinsic to the system; in such a case sensors represent (covariant) afferents and motor effectors represent (contravariant) motor efferents. The neuronal networks that perform such a function are viewed as general tensor transformations among different expressions and metric tensors determining the geometry of neural functional spaces. Although the non-orthogonality of a coordinate system does not impose a specific geometry on the space, this "Tensor Network Theory of brain function" allows for the possibility that the geometry is non-Euclidean. It is suggested that investigation of the non-Euclidean nature of the geometry is the key to understanding brain function and to interpreting neuronal network function. This paper outlines three contemporary applications of such a theoretical modeling approach. The first is the analysis and interpretation of multi-electrode recordings. The internal geometries of neural networks controlling external behavior of the skeletomuscle system is experimentally determinable using such multi-unit recordings. The second application of this geometrical approach to brain theory is modeling the control of posture and movement. A preliminary simulation study has been conducted with the aim of understanding the control of balance in a standing human. The model appears to unify postural control strategies that have previously been considered to be independent of each other. Third, this paper emphasizes the importance of the geometrical approach for the design and fabrication of neurocomputers that could be used in functional neuromuscular stimulation (FNS) for replacing lost motor control.

Review↗

Mars Tumbleweed: FY2003 Conceptual Design Assessment

NASA LaRC is studying concepts for a new type of Mars exploration vehicle that would be propelled by the wind. Known as the Mars Tumbleweed, it would derive mobility through use of the Martian surface winds. Tumbleweeds could conceivably travel greater distances, cover larger areas of the surface, and provide access to areas inaccessible by conventional vehicles. They would be lightweight and relatively inexpensive, allowing a multiple vehicle network to be deployed on a single mission. Tumbleweeds would be equipped with sensors for conducting science and serve as scouts searching broad areas to identify specific locations for follow-on investigation by other explorers. An extensive assessment of LaRC Tumbleweed concepts was conducted in FY03, including refinement of science mission scenarios, definition of supporting subsystems (structures, power, communications), testing in wind tunnels, and development of a dynamic simulation capability.

Antol, Jeffrey↗

Satellite Validation: A Project to Create a Data-Logging System to Monitor Lake Tahoe

Flying aboard the satellite Terra, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is an imaging instrument used to acquire detailed maps of Earth's surface temperature, elevation, emissivity, and reflectance. An automated site consisting of four buoys was established 6 years ago at Lake Tahoe for the validation of ASTERS thermal infrared data. Using Campbell CR23X Dataloggers, a replacement system to be deployed on a buoy was designed and constructed for the measurement of the lake's temperature profile, surrounding air temperature, humidity, wind direction and speed, net radiation, and surface skin temperature. Each Campbell Datalogger has been programmed to control, power, and monitor 14 different temperature sensors, a JPL-built radiometer, and an RM Young 32500 meteorological station. The logger communicates with the radiometer and meteorological station through a Campbell SDM-SIO4 RS232 serial interface, sending polling commands, and receiving filtered data back from the sensors. This data is then cataloged and sent back across a cellular modem network every hour to JPL. Each instrument is wired via a panel constructed with 18 individual plugs that allow for simple installation and expansion. Data sent back from the system are analyzed at JPL, where they are used to calibrate ASTER data.

Roy, Rudy A.↗

Conceptual Design of a Communication-Based Deep Space Navigation Network

As the need grows for increased autonomy and position knowledge accuracy to support missions beyond Earth orbit, engineers must push and develop more advanced navigation sensors and systems that operate independent of Earth-based analysis and processing. Several spacecraft are approaching this problem using inter-spacecraft radiometric tracking and onboard autonomous optical navigation methods. This paper proposes an alternative implementation to aid in spacecraft position fixing. The proposed method Network-Based Navigation technique takes advantage of the communication data being sent between spacecraft and between spacecraft and ground control to embed navigation information. The navigation system uses these packets to provide navigation estimates to an onboard navigation filter to augment traditional ground-based radiometric tracking techniques. As opposed to using digital signal measurements to capture inherent information of the transmitted signal itself, this method relies on the embedded navigation packet headers to calculate a navigation estimate. This method is heavily dependent on clock accuracy and the initial results show the promising performance of a notional system.

Anzalone, Evan J.↗

Sono-Assisted Surface Energy Driven Assembly of 2D Materials on Flexible Polymer Substrates: A Green Assembly Method Using Water

The challenges in achieving a green and scalable integration of two-dimensional (2D) materials with flexible polymer substrates present a major barrier for the application of 2D materials, such as graphene, MoS2, and h-BN for flexible devices. Here, we create a sono-assisted surface energy driven assembly (SASEDA) method that can achieve foot-scale to micrometer-scale assembly of 2D materials, form a conductive network in as short as 10 s, and build hierarchical and hybrid flexible devices such as sensors, resistors, and capacitors by using water as the dispersion solvent. SASEDA highlights two counterintuitive innovations. First, we use an 'unfavorable' solvent (i.e., water) for both 2D materials (e.g., graphene, MoS2, and h-BN) and polymer substrates (e.g., polydimethylsiloxane) to drive the assembly process. Second, we use a weak sono-field (0.3 W/cm2) generated by a regular sonication bath cleaner to enhance the assembly efficiency and reorganize and unify the assembly network. This method and its principle pave the way toward affordable large-scale 2D material-based flexible devices.

2D material↗

The Multi-Sensor Aerosol Products Sampling System (MAPSS) for Integrated Analysis of Satellite Retrieval Uncertainties

Among the known atmospheric constituents, aerosols represent the greatest uncertainty in climate research. Although satellite-based aerosol retrieval has practically become routine, especially during the last decade, there is often disagreement between similar aerosol parameters retrieved from different sensors, leaving users confused as to which sensors to trust for answering important science questions about the distribution, properties, and impacts of aerosols. As long as there is no consensus and the inconsistencies are not well characterized and understood ', there will be no way of developing reliable climate data records from satellite aerosol measurements. Fortunately, the most globally representative well-calibrated ground-based aerosol measurements corresponding to the satellite-retrieved products are available from the Aerosol Robotic Network (AERONET). To adequately utilize the advantages offered by this vital resource,., an online Multi-sensor Aerosol Products Sampling System (MAPSS) was recently developed. The aim of MAPSS is to facilitate detailed comparative analysis of satellite aerosol measurements from different sensors (Terra-MODIS, Aqua-MODIS, Terra-MISR, Aura-OMI, Parasol-POLDER, and Calipso-CALIOP) based on the collocation of these data products over AERONET stations. In this presentation, we will describe the strategy of the MAPSS system, its potential advantages for the aerosol community, and the preliminary results of an integrated comparative uncertainty analysis of aerosol products from multiple satellite sensors.

Ichoku, Charles↗

Toward a Coherent Detailed Evaluation of Aerosol Data Products from Multiple Satellite Sensors

Atmospheric aerosols represent one of the greatest uncertainties in climate research. Although satellite-based aerosol retrieval has practically become routine, especially during the last decade, there is often disagreement between similar aerosol parameters retrieved from different sensors, leaving users confused as to which sensors to trust for answering important science questions about the distribution, properties, and impacts of aerosols. As long as there is no consensus and the inconsistencies are not well characterized and understood, there will be no way of developing reliable climate data records from satellite aerosol measurements. Fortunately, the most globally representative well-calibrated ground-based aerosol measurements corresponding to the satellite-retrieved products are available from the Aerosol Robotic Network (AERONET). To adequately utilize the advantages offered by this vital resource, an online Multi-sensor Aerosol Products Sampling System (MAPSS) was recently developed. The aim of MAPSS is to facilitate detailed comparative analysis of satellite aerosol measurements from different sensors (Terra-MODIS, Aqua-MODIS, TerraMISR, Aura-OMI, Parasol-POLDER, and Calipso-CALIOP) based on the collocation of these data products over AERONET stations. In this presentation, we will describe the strategy of the MA~SS system, its potential advantages for the aerosol community, and the preliminary results of an integrated comparative uncertainly analysis of aerosol products from multiple satellite sensors.

Ichoku, Charles↗

Self-powered Wireless Dual-mode Langasite Sensor for Pressure/Temperature Monitoring of Nuclear Reactors

Safety issues in nuclear power plants are of extreme importance. Numerous sensors are used within nuclear power plants for sensing and monitoring of its critical operation parameters. As currently implemented, during massive losses of power across the plant in accidents, the backup batteries for the monitoring sensors will ultimately be drained and can cease to operate. The overall objective of this NEUP project is to develop a novel self-powered wireless hybrid sensor which can accurately monitor both pressure and temperature using a single device, even in the extreme harsh environments of severe nuclear accidents, without the requirement of external electricity. At Virginia Tech side, a heat pipe assisted thermoelectric generator (TEG) energy harvester and associated power management and data transmission circuits were developed. The energy harvester can produce electrical power continuously, even in loss of plant and external power situations, providing power for important data transmission for accident diagnosis. The self-powered sensing system could be integrated directly onto key nuclear components including pipes, pump housings, heat exchangers, reactor vessels, and shielding structures inside and outside of the reactor core. Two TEG based energy harvesters which can work in relatively low (50-250 ºC) and high temperature ranges (250-350 ºC) were established and tested. In the prototypes, heat pipes were introduced to reduce the heat resistances of the device at the hot and cold ends. The power output of the heat pipe assisted TEG module is 2.22 W, which is 6 times more than one using an aluminum rod. In-lab tests showed that these devices canmeet the requirement to power the sensors and circuit during normal and off-normal conditions.Amodel was then built to analyze the thermal network in the energy harvester, and was used to optimize its performance and reduce its geometry size. The design and testing of the vibrational energy harvester were completed. The harvester, has shown to effectively increase the vibrational potential of the main coolant pump, though it has been shown that thermal energy has a much higher potential for the powering of sensors. In this project, a self-start charger circuit for the energy harvester was designed to regulate and manage the power. An energy storage element was incorporated into the energy harvesting module for sensors and wireless transceivers in an off-normal situation. The signal conditioning and wireless communication electronic circuits were developed and tested in the lab. An important consideration for an energy harvester within a nuclear power plant is the level of radiation it will encounter during operation. The two main sources of radiation in a nuclear power plant are gamma and neutron radiation. Aradiation experiment was conducted in the Westinghouse Electric Company during which the prototypes were placed in a 106 rad radiation environment to test their ability to operate in the extreme environment. The results showed that this level of radiation had little effect on the performance of the TEGmaterial and heat pipe heat exchanging system. Its influence on the circuit was still under research, however the need for more development on radiation hardened electronics is needed. Pressure measurement based on piezoelectric crystal resonators has advantages over the commercially available methods like capacitive, electromagnetic, optical, potentiometric, and piezo resistive strain gauges. Since the output of piezoelectric based sensors are digital in format, it has a very high resolution, high accuracy, and long-term stability. Quartz is one of the commonly used piezoelectric material in digital pressure sensors design. This material is elastic in nature, providing high stability, high repeatability, high elasticity, repeatable mechanical behavior for many cycles of operation, and is free of hysteresis. The quartz resonator has a high Q, which means that very high vibration can be driven with the very low electric power. Langasite (LGS) is a new promising piezoelectric material that combines the advantageous properties of quartz with a better performance at high temperatures. LGS will not undergo any phase transitions up to its melting temperature of 1473 C, and hence, the application of Langasite in measuring pressure at high temperatures is promising. In this project, the design and fabrication of the piezo oscillator crystal for temperature and pressure measurement has been completed. The entire sensor has been fabricated and tested to see its sensitivity to temperature and pressure changes. The design and optimization of the radiation shielding systems that can effective protect the duel-mode Langasite sensor inside reactor core were demonstrated. To maximize the radiation shielding effectiveness, different types of materials are investigated based upon recent developments of radiation shielding systems including aluminum + Boron carbide and bismuth borosilicate glass B2O3 in addition to tungsten/B4C. New materials based upon metatheses is included as potential shielding to mitigate neutron fluencies. The radiation shielding effectiveness of selected composites are evaluated within a broader neutron spectrum from thermal, epithermal, fast (1 MeV) and 15 MeV neutrons. Performance analysis was also conducted to evaluate the effects of selected composite systems.Aconceptual design was also proposed for the packaging and integration of the radiation shielding system with the sensor, and its applicability within the reactor core was discussed. At end of the project, all components necessary for a complete sensing and monitoring system, including a langasite sensor, electrodes, the TEG harvester, an energy storage device, and power management and transmission electronics were integrated to demonstrate feasibility in powering the sensor. Finally, integration of each component into a full package, and a full demonstration were done.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Embedded Sensing in Additive Manufacturing Metal and Polymer Parts: A Comparative Study of Integration Techniques and Structural Health Monitoring Performance

This study presents a comparative evaluation of post-process sensor integration in additively manufactured (AM) metal and the in-situ process for polymer structures for structural health monitoring (SHM), with an emphasis on embedded sensors. Geometrically identical specimens were fabricated using copper via metal fused filament fabrication (FFF) and PLA via polymer FFF, with piezoelectric transducers (PZTs) inserted into internal cavities to assess the influence of material and placement on sensing fidelity. Mechanical testing under compressive and point loads generated signals that were transformed into time–frequency spectrograms using a Short-Time Fourier Transform (STFT) framework. An engineered RGB representation was developed, combining global amplitude scaling with an amplitude-envelope encoding to enhance contrast and highlight subtle wave features. These spectrograms served as inputs to convolutional neural networks (CNNs) for classification of load conditions and detection of damage-related features. Results showed reliable recognition in both copper and PLA specimens, with CNN classification accuracies exceeding 95%. Embedded PZTs were especially effective in PLA, where signal damping and environmental sensitivity often hinder surface-mounted sensors. This work demonstrates the advantages of embedded sensing in AM structures, particularly when paired with spectrogram-based feature engineering and CNN modeling, advancing real-time SHM for aerospace, energy, and defense applications.

additive manufacturing↗

CONGO²: Scalable Online Anomaly Detection and Localization in Power Electronics Networks

Rapid and accurate detection and localization of electronic disturbances simultaneously are important for preventing its potential damages and determining potential remedies. Existing anomaly detection methods are severely limited by the low accuracy, the expensive computational cost and the need for highly trained personnel. There is an urgent need for a scalable online algorithm for in-field analysis of large-scale power electronics networks. Here in this paper, we propose a fast and accurate algorithm for anomaly detection and localization of power electronics networks: stratified colored-node graph (CONGO2). This algorithm hierarchically models the change of correlated waveforms and then correlated sensors using the colored-node graph. By aggregating the change of each sensor with its neighbors’ inputs, we can spontaneously identify and localize the anomaly that cannot be detected by data collected from a single sensor. As our proposed method only focuses on the changes within a short time frame, it is highly computational efficient and only needs small data storage. Thus, our method is ideal for online and reliable anomaly detection and localization of large-scale power electronic networks. Compared to existing anomaly detection methods, our method is entirely data-driven without training data, highly accurate and reliable for wide-spectrum anomalies detection, and more importantly, capable of both detection and localization. Thus, it is ideal for infield deployment for large-scale power electronic networks. As illustrated by a distributed energy resources (DERs) power grid with 37-node, our method can effectively detect and localize various cyber and physical attacks.

42 ENGINEERING↗

General purpose intelligent sensor interface

The long range goal is to develop an intelligent sensor system that will simplify the design and development of expert systems that use sensors of physical phenomena as a source of input data. This phase of the research concentrated on the integration of image processing sensors with expert system environments. The anticipated result of this research is the ability to design systems in which the user will not need to be an expert in such areas as image processing algorithms, local area networks, image processor hardware selection or interfacing, or television cameras selection. The user will be able to access data from video sensors through standard expert system statements without any need to know about the sensor hardware or software.

Mckee, J. W.↗

VIGILANTE: An Advanced Sensing/Processing Testbed for ATR Applications

VIGILANTE consists of two major components:1. the Viewing Imager/Gimballed Instrumentation Laboratory (VIGIL)- advanced infrared, visible, and ultraviolet sensors with appropriate optics and camera electronics & 2. the Analog Neural Three-dimensional processing Experiment (ANTE)-a massively parallel, neural network-based, high-speed processor.

automatic↗

Autonomous exploration system: Techniques for interpretation of multispectral data

An on-board autonomous exploration system that fuses data from multiple sensors, and makes decisions based on scientific goals is being developed using a series of artificial neural networks. Emphasis is placed on classifying minerals into broad geological categories by analyzing multispectral data from an imaging spectrometer. Artificial neural network architectures are being investigated for pattern matching and feature detection, information extraction, and decision making. As a first step, a stereogrammetry net extracts distance data from two gray scale stereo images. For each distance plane, the output is the probable mineral composition of the region, and a list of spectral features such as peaks, valleys, or plateaus, showing the characteristics of energy absorption and reflection. The classifier net is constructed using a grandmother cell architecture: an input layer of spectral data, an intermediate processor, and an output value. The feature detector is a three-layer feed-forward network that was developed to map input spectra to four geological classes, and will later be expanded to encompass more classes. Results from the classifier and feature detector nets will help to determine the relative importance of the region being examined with regard to current scientific goals of the system. This information is fed into a decision making neural net along with data from other sensors to decide on a plan of activity. A plan may be to examine the region at higher resolution, move closer, employ other sensors, or record an image and transmit it back to Earth.

Yates, Gigi↗