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At least 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Multi-Sensor Data Acquisition System for Process Monitoring

Idaho National Laboratory is building a new nuclear fuel cycle test bed. This fuel cycle test bed named “Beartooth” will give researchers the opportunity to study the nuclear fuel cycle process. The process operations include the use of centrifugal contactors and process flow for the purification of special nuclear material recovery in used fuel. This new research facility has the need to use non-traditional measurement sensors to determine the state-of-health of the solvent extraction process. These non-traditional sensors can also enhance nuclear nonproliferation supervision and other activities. The non-traditional sensors include accelerometers, acoustic, current, flow, colorimetric, temperature, pH and conductivity. The challenge behind deploying all these sensors, is the need for fast and reliable data collection. The data acquisition system (DAQ) needs to be capable of recording up to 12 accelerometers and/or acoustic microphones simultaneously at rates up to 12.8 kSamples/second/channel. This requires the need for a strong architecture and data collection solution. This summary identifies the system architecture, DAQ, and sensors needed to support non-traditional measurements in a solvent extraction process.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1989

On the development of a reactive sensor-based robotic system

Flexible robotic systems for space applications need to use local information to guide their action in uncertain environments where the state of the environment and even the goals may change. They have to be tolerant of unexpected events and robust enough to carry their task to completion. Tactical goals should be modified while maintaining strategic goals. Furthermore, reactive robotic systems need to have a broader view of their environments than sensory-based systems. An architecture and a theory of representation extending the basic cycles of action and perception are described. This scheme allows for dynamic description of the environment and determining purposive and timely action. Applications of this scheme for assembly and repair tasks using a Universal Machine Intelligence RTX robot are being explored, but the ideas are extendable to other domains. The nature of reactivity for sensor-based robotic systems and implementation issues encountered in developing a prototype are discussed.

Hexmoor, Henry H.↗
DOE OSTI2021

Landscape-scale characterization of Arctic tundra vegetation composition, structure, and function with a multi-sensor unoccupied aerial system

The Arctic is experiencing some of the most rapid climate change on Earth, with strong impacts on tundra ecosystems that are characterized by high land-surface and vegetation heterogeneity. Previous studies have explored this complexity using satellite remote sensing, however these typically coarse spatial resolution data have generally missed sub-pixel heterogeneity, leaving critical gaps in our understanding of tundra vegetation dynamics from the community to landscape scales. To address these gaps, we collected very high-resolution (1-5 cm) optical, structural, and thermal data at three low-Arctic tundra sites on the Seward Peninsula, Alaska, using a multi-sensor unoccupied aerial system (UAS). We examined the application of these data to studying tundra vegetation dynamics, by quantifying (1) canopy height and thermoregulation (leaf-air temperature) of representative plant functional types (PFTs), (2) fine-scale patterns of vegetation composition across landscapes, and (3) impacts of fine-scale vegetation composition on landscape-scale variation of canopy height and thermoregulation. Our results show that deciduous tall shrubs (those that can potentially grow >2 m) had a strong cooling effect, with canopy temperatures significantly lower than local air temperatures and other PFTs. Increased cover of tall shrubs also had the potential to reduce the cover of low-stature PFTs across the landscape, potentially associated with their closed canopy (i.e., increased light competition) and strong thermoregulation. To understand the connections between fine-scale vegetation composition and large-scale ecosystem processes, we produced a random forest model which showed that fine-scale PFT composition accounted for 86.8% and 74.2% of the landscape-scale variation in canopy height and thermoregulation, respectively. These findings highlight the importance of spatially detailed characterization of tundra PFTs to improve our ecological understanding and model representation of tundra vegetation, also transcend our study to show the need for continued collection of similar datasets to better understand the impacts of surface heterogeneity on the mapping and modeling of tundra ecosystem dynamics, as well as assist with conservation management and biodiversity monitoring strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Comprehensive AI-based System for Control, Sensor Estimation, and Fault Detection of Cascaded Multilevel Inverters

In this paper, an Artificial Intelligence-based (AI) system is proposed for an 11-level cascaded H-bridge multilevel inverter (MLI) with the aims of harmonic suppression and reliability enhancement. The system consists of three seamlessly integrated Neural Networks (NNs). First, a multilayer perceptron is used to generalize the optimal switching angles for selective harmonic elimination under non-equal DC voltages. Next, an autoencoder NN estimates the voltage sensor readings to address potential drifting. Finally, a perceptron NN detects inverter faults based solely on the output voltage of the MLI. Simulation scenarios were evaluated, and the results show that the proposed system provides a comprehensive solution for the robust operation of the MLI. The proposed solution is capable of minimizing the targeted harmonics orders with minimal impact on the fundamental voltage, even when the voltage sensor drifts. Furthermore, the inverter under fault conditions was successfully identified.

Rezende da Costa Reis Kimpara, Renata↗
NASA NTRS1987

Spectrum-modulating fiber-optic sensors for aircraft control systems

A family of fiber-optic sensors for aircraft engine control systems is described. Each of these sensors uses a spectrum-modulation method to obtain an output which is largely independent of the fiber link transmissivity. A position encoder is described which uses a code plate to digitally modulate the sensor output spectrum. Also described are pressure and temperature sensors, each of which uses a Fabry-Perot cavity to modulate the sensor output spectrum as a continuous function of the measurand. A technique is described whereby a collection of these sensors may be effectively combined to perform a number of the measurements which are required by an aircraft-engine control system.

Beheim, Glenn↗
NASA NTRS2010

Adaptive Fault Detection on Liquid Propulsion Systems with Virtual Sensors: Algorithms and Architectures

Prior to the launch of STS-119 NASA had completed a study of an issue in the flow control valve (FCV) in the Main Propulsion System of the Space Shuttle using an adaptive learning method known as Virtual Sensors. Virtual Sensors are a class of algorithms that estimate the value of a time series given other potentially nonlinearly correlated sensor readings. In the case presented here, the Virtual Sensors algorithm is based on an ensemble learning approach and takes sensor readings and control signals as input to estimate the pressure in a subsystem of the Main Propulsion System. Our results indicate that this method can detect faults in the FCV at the time when they occur. We use the standard deviation of the predictions of the ensemble as a measure of uncertainty in the estimate. This uncertainty estimate was crucial to understanding the nature and magnitude of transient characteristics during startup of the engine. This paper overviews the Virtual Sensors algorithm and discusses results on a comprehensive set of Shuttle missions and also discusses the architecture necessary for deploying such algorithms in a real-time, closed-loop system or a human-in-the-loop monitoring system. These results were presented at a Flight Readiness Review of the Space Shuttle in early 2009.

Matthews, Bryan L.↗
NASA NTRS1992

Inertial navigation sensor integrated obstacle detection system

A system that incorporates inertial sensor information into optical flow computations to detect obstacles and to provide alternative navigational paths free from obstacles. The system is a maximally passive obstacle detection system that makes selective use of an active sensor. The active detection typically utilizes a laser. Passive sensor suite includes binocular stereo, motion stereo and variable fields-of-view. Optical flow computations involve extraction, derotation and matching of interest points from sequential frames of imagery, for range interpolation of the sensed scene, which in turn provides obstacle information for purposes of safe navigation.

Bhanu, Bir↗
NASA NTRS2015

On-Orbit, Immuno-Based, Label-Free White Blood Cell Counting System with Microelectromechanical Sensor Technology (OILWBCS-MEMS)

Aurora Flight Sciences, in partnership with Draper Laboratory, has developed a miniaturized system to count white blood cells in microgravity environments. The system uses MEMS technology to simultaneously count total white blood cells, the five white blood cell differential subgroups, and various lymphocyte subtypes. The OILWBCS-MEMS detection technology works by immobilizing an array of white blood cell-specific antibodies on small, gold-coated membranes. When blood flows across the membranes, specific cells' surface protein antigens bind to their corresponding antibodies. This binding can be measured and correlated to cell counts. In Phase I, the partners demonstrated surface chemistry sensitivity and specificity for total white blood cells and two lymphocyte subtypes. In Phase II, a functional prototype demonstrated end-to-end operation. This rugged, miniaturized device requires minimal blood sample preparation and will be useful for both space flight and terrestrial applications.

Edmonds, Jessica↗
DOE OSTI2023

Presentation: Sensor Anomaly Detection for Nuclear Reactor Systems Utilizing Linear Regression and K-Means Unsupervised Machine Learning: An overview of methods and results

This presentation is a culmination of work which has occurred over the course of a 10-week internship. Anomaly detection methods must be both robust enough to detect subtle anomalies yet not so sensitive to report false positives, which would result significant loss of revenue. Methods currently being developed for autonomous systems are often pursuing a Digital Twin method, which will look at the entire system and model it as a whole. This presentation, however, focuses less on direct application to an NPP, rather acting as a proof of concept for the methods developed. For the project, we look to develop methods to analyze steady-state data and report anomalies.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1999

MEMS Rate Sensors for Space

Micromachined Electro Mechanical System Rate sensors offer many advantages that make them attractive for space use. They are smaller, consume less power, and cost less than the systems currently available. MEMS Rate Sensors however, have not been optimized for use on spacecraft. This paper describes an approach to developing MEMS Rate Sensors systems for space use.

Gambino, Joel P.↗
DOE OSTI2020

A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra

Changes in vegetation distribution, structure, and function can modify the canopy properties of terrestrial ecosystems, with potential consequences for regional and global climate feedbacks. In the Arctic, climate is warming twice as fast as compared to the global average (known as ‘Arctic amplification’), likely having stronger impacts on arctic tundra vegetation. In order to quantify these changes and assess their impacts on ecosystem structure and function, methods are needed to accurately characterize the canopy properties of tundra vegetation types. However, commonly used ground-based measurements are limited in spatial and temporal coverage, and differentiating low-lying tundra plant species is challenging with coarse-resolution satellite remote sensing. The collection and processing of multi-sensor data from unoccupied aerial systems (UASs) has the potential to fill the gap between ground-based and satellite observations. To address the critical need for such data in the Arctic, we developed a cost-effective multi-sensor UAS (the ‘Osprey’) using off-the-shelf instrumentation. The Osprey simultaneously produces high-resolution optical, thermal, and structural images, as well as collecting point-based hyperspectral measurements, over vegetation canopies. In this paper, we describe the setup and deployment of the Osprey system in the Arctic to a tundra study site located in the Seward Peninsula, Alaska. We present a case study demonstrating the processing and application of Osprey data products for characterizing the key biophysical properties of tundra vegetation canopies. In this study, plant functional types (PFTs) representative of arctic tundra ecosystems were mapped with an overall accuracy of 87.4%. The Osprey image products identified significant differences in canopy-scale greenness, canopy height, and surface temperature among PFTs, with deciduous low to tall shrubs having the lowest canopy temperatures while non-vascular lichens had the warmest. The analysis of our hyperspectral data showed that variation in the fractional cover of deciduous low to tall shrubs was effectively characterized by Osprey reflectance measurements across the range of visible to near-infrared wavelengths. Therefore, the development and deployment of the Osprey UAS, as a state-of-the-art methodology, has the potential to be widely used for characterizing tundra vegetation composition and canopy properties to improve our understanding of ecosystem dynamics in the Arctic, and to address scale issues between ground-based and airborne/satellite observations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1997

Radiometric Calibration of the Earth Observing System's Imaging Sensors

The work on the grant was mainly directed towards developing new, accurate, redundant methods for the in-flight, absolute radiometric calibration of satellite multispectral imaging systems and refining the accuracy of methods already in use. Initially the work was in preparation for the calibration of MODIS and HIRIS (before the development of that sensor was canceled), with the realization it would be applicable to most imaging multi- or hyper-spectral sensors provided their spatial or spectral resolutions were not too coarse. The work on the grant involved three different ground-based, in-flight calibration methods reflectance-based radiance-based and diffuse-to-global irradiance ratio used with the reflectance-based method. This continuing research had the dual advantage of: (1) developing several independent methods to create the redundancy that is essential for the identification and hopefully the elimination of systematic errors; and (2) refining the measurement techniques and algorithms that can be used not only for improving calibration accuracy but also for the reverse process of retrieving ground reflectances from calibrated remote-sensing data. The grant also provided the support necessary for us to embark on other projects such as the ratioing radiometer approach to on-board calibration (this has been further developed by SBRS as the 'solar diffuser stability monitor' and is incorporated into the most important on-board calibration system for MODIS)- another example of the work, which was a spin-off from the grant funding, was a study of solar diffuser materials. Journal citations, titles and abstracts of publications authored by faculty, staff, and students are also attached.

Slater, Philip N.↗
NASA NTRS1999

Fiber Optic Sensor Components and Systems for Smart Materials and Structures

The general objective of the funded research effort has been the development of discrete and distributed fiber sensors and fiber optic centered opto-electronic networks for the intelligent monitoring of phenomena in various aerospace structures related to NASA Marshall specific applications. In particular, we have proposed and have been developing technologies that we believe to be readily transferrable and which involve new fabrication techniques. The associated sensors developed can be incorporated into the matrix or on the surfaces of structures for the purpose of sensing stress, strain, temperature-both low and high, pressure field variations, phase changes, and the presence of various chemical constituents.

Lyons, R.↗
NASA NTRS2007

System Characterization Results for the QuickBird Sensor

An overall system characterization was performed on several DigitalGlobe' QuickBird image products by the NASA Applied Research & Technology Project Office (formerly the Applied Sciences Directorate) at the John C. Stennis Space Center. This system characterization incorporated geopositional accuracy assessments, a spatial resolution assessment, and a radiometric calibration assessment. Geopositional assessments of standard georeferenced multispectral products were obtained using an array of accurately surveyed geodetic targets evenly spaced throughout a scene. Geopositional accuracy was calculated in terms of circular error. Spatial resolution of QuickBird panchromatic imagery was characterized based on edge response measurements using edge targets and the tilted-edge technique. Relative edge response was estimated as a geometric mean of normalized edge response differences measured in two directions of image pixels at points distanced from the edge by -0.5 and 0.5 of ground sample distance. A reflectance-based vicarious calibration approach, based on ground-based measurements and radiative transfer calculations, was used to estimate at-sensor radiance. These values were compared to those measured by the sensor to determine the sensor's radiometric accuracy. All imagery analyzed was acquired between fall 2005 and spring 2006. These characterization results were compared to previous years' results to identify any temporal drifts or trends.

Holekamp, Kara↗
NASA NTRS2008

Range-Gated Metrology: An Ultra-Compact Sensor for Dimensional Stabilization

Point-to-point laser metrology systems can be used to stabilize large structures at the nanometer levels required for precision optical systems. Existing sensors are large and intrusive, however, with optical heads that consist of several optical elements and require multiple optical fiber connections. The use of point-to-point laser metrology has therefore been limited to applications where only a few gauges are needed and there is sufficient space to accommodate them. Range-Gated Metrology is a signal processing technique that preserves nanometer-level or better performance while enabling: (1) a greatly simplified optical head - a single fiber optic collimator - that can be made very compact, and (2) a single optical fiber connection that is readily multiplexed. This combination of features means that it will be straightforward and cost-effective to embed tens or hundreds of compact metrology gauges to stabilize a large structure. In this paper we describe the concept behind Range-Gated Metrology, demonstrate the performance in a laboratory environment, and give examples of how such a sensor system might be deployed.

laser metrology↗
NASA NTRS2009

NPP Clouds and the Earth's Radiant Energy System (CERES) Predicted Sensor Performance Calibration and Preliminary Data Product Performance

Continuation of the Earth Radiation Budget (ERB) Climate Data Record (CDR) has been identified as critical in the 2007 NRC Decadal Survey, the Global Climate Observing System WCRP report, and in an assessment titled Impacts of NPOESS Nunn-McCurdy Certification on Joint NASA-NOAA Climate Goals. In response, NASA, NOAA and NPOESS agreed in early 2008 to fly the final existing CERES Flight Model (FM-5) on the NPP spacecraft for launch in 2010. Future opportunities for ERB CDR continuity consist of procuring an additional CERES Sensor with modest performance upgrades for flight on the NPOESS C1 spacecraft in 2013, followed by a new CERES follow-on sensor for flight in 2018 on the NPOESS C3 spacecraft. While science goals remain unchanged for the long-term ERB Climate Data Record, it is now understood that the task of achieving these goals is more difficult for two reasons. The first is an increased understanding of the dynamics of the Earth/atmosphere system which demonstrates that rigorous separation of natural variability from anthropogenic change on decadal time scales requires higher accuracy and stability than originally envisioned. Secondly, future implementation scenarios involve less redundancy in flight hardware (1 vs. 2 orbits and operational sensors) resulting in higher risk of loss of continuity and reduced number of independent observations to characterize performance of individual sensors. Although EOS CERES CDR's realize a factor of 2 to 4 improvement in accuracy and stability over previous ERBE CDR's, future sensors will require an additional factor of 2 improvement to answer rigorously the science questions moving forward. Modest investments, defined through the CERES Science Team s 30-year operational history of the EOS CERES sensors, in onboard calibration hardware and pre-flight calibration and test program will ensure meeting these goals while reducing costs in re-processing scientific datasets. The CERES FM-5 pre-flight radiometric characterization program benefited from the 30-year operational experience of the CERES EOS sensors, as well as a stronger emphasis of radiometric characterization in the Statement of Work with the sensor provider. Improvements to the pre-flight program included increased spectral, spatial, and temporal sampling under vacuum conditions as well as additional tests to characterize the primary and transfer standards in the calibration facility. Future work will include collaboration with NIST to further enhance the understanding of the radiometric performance of this equipment prior to flight. The current effort summarizes these improvements to the CERES FM-5 pre-flight sensor characterization program, as well as modifications to inflight calibration procedures and operational tasking. In addition, an estimate of the impacts to the system level accuracy and traceability is presented.

Priestly, Kory↗
NASA NTRS1986

Development of sensor augmented robotic weld systems for aerospace propulsion system fabrication

In order to meet stringent performance goals for power and reuseability, the Space Shuttle Main Engine was designed with many complex, difficult welded joints that provide maximum strength and minimum weight. To this end, the SSME requires 370 meters of welded joints. Automation of some welds has improved welding productivity significantly over manual welding. Application has previously been limited by accessibility constraints, requirements for complex process control, low production volumes, high part variability, and stringent quality requirements. Development of robots for welding in this application requires that a unique set of constraints be addressed. This paper shows how robotic welding can enhance production of aerospace components by addressing their specific requirements. A development program at the Marshall Space Flight Center combining industrial robots with state-of-the-art sensor systems and computer simulation is providing technology for the automation of welds in Space Shuttle Main Engine production.

Jones, C. S.↗