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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 1,585 records · Page 88
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Measurement of Atmospheric Composition from Geostationary Platforms

Satellite instruments flown since 1970 have had great success in elucidating the processes that control stratospheric ozone. In contrast, space-based data for tropospheric constituents that affect air quality and climate have only recently become available. While these datasets highlight the rapidly advancing capabilities of spacebased tropospheric sensors, they are also pointing to the limitations of sun-synchronous, low-earth orbiting (SSO/LEO) satellite platforms for making such measurements. In our talk we will highlight the science requirements for new missions and the technological and algorithmic approaches that we are developing to meet these requirements. From these studies a clear need for advanced atmospheric composition sensors has emerged that can be put on geostationary (GEO) platforms to provide 5 km horizontal resolution with 15-60 minutes repeat cycle. Such measurements have been high priority in the recently released Decadal Survey report by the US National Research Council. The need for GEO is driven not only by the science requirements to track rapidly changing pollution events but also by the need to provide altitude-resolved information about tropospheric constituents. Currently, with the exception of aerosols, it is not possible to derive profile information about lower tropospheric constituents from satellite measurements. New algorithmic approaches are being developed to obtain this information by combining UV and IR data, by monitoring the spatial and temporal structures of the constituents, and by using low-level clouds to separate boundary layer constituents from free troposphere. All these approaches require better spatial and temporal resolution than that provided by LEO sensors.

Bhartia, P. K.↗
DOE OSTI2020

Porous Membranes for Moisture Probe Protection (SR18024)

In the presence of contaminants, moisture probes can give false moisture readings and have shorter lifetimes. Absolute humidity probes are composed of porous, hygroscopic metallic oxide thin films (Al 2 O 3 ) coupled to electrodes (e.g. Au and Al). While these materials provide absolute humidity readings at low water levels at a relatively low cost, they are sensitive to the presence of contaminants, especially ammonia, due to its high hygroscopicity and ability to bind to the probe. Without frequent recalibration, especially in the presence of contaminants, the humidity levels are inaccurately reported due to measurement drift that occurs when the pores close, changing the impedance of the probe. The goal of this project was to improve the stability and reliability of these moisture probes by incorporating a molecular sieve into the probe to allow only the water to reach the sensor. Molecular sieves contain pores that allow molecule specific permeation. Two dimensional inorganic nanosheets (e.g. BN, MoO 2 , MoS 2 ) have recently been shown to behave as molecular sieves for water. Compared to zeolites, nanoporous materials have higher flux rates through the pores, leading to faster water permeation. Graphene oxide sheets have also been successfully employed for unimpeded water permeation; however, at lower humidity levels, the structure is unstable and the pores shrink, preventing the sieving of molecules. Compared to graphene oxide, sheets based off of inorganic materials, such as molybdenum, have been found to be more stable at lower humidity levels and have faster permeation rates. By creating a barrier in before the Al 2 O 3 sensor chip to prevent the permeation of contaminants into the probe, the lifetime of the probe will be extended. While the molybdenum sheets are a promising material for this application, their performance against ammonia and tritiated compounds have not yet been evaluated. This technology can be applied to other applications, including gas and liquid purification for environmental remediation, single molecule sensors, field effect transistors, and catalysts.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRSDate not supplied

Multiclass Flight Anomaly Detection Using Sensor Fusion Based on Dempster-Shafer Theory

As aviation systems in commercial operations continue to grow in complexity, the anomalies exhibited by these systems become more elaborate and difficult to detect. To address the challenge of detecting these complex anomalies, deep learning models have been used extensively in aviation anomaly detection studies, at the expense of end-user interpretability. Aiming to maintain the same level of interpretability as traditional threshold-exceedance methods, we continue our development of prediction models using ordinal patterns and their distributions throughout the flight. Specifically, this study extends our work into multiclass anomaly detection using sensor fusion based on Dempster-Shafer theory (DST), a second-order probability theory used to combine information from different sources of evidence. Our approach uses DST to reduce the uncertainty in the class predictions of an ensemble of classifiers. These classifiers rely on the similarity between flight data and class templates to make a prediction of the state of the aircraft. Our approach aims to take advantage of simple models trained on interpretable features (ordinal patterns) to correctly predict an anomaly and identify the flight dynamics linked to the anomaly. Our results show an improvement when using DST-based sensor fusion over simple majority voting. Additionally, our results provide insight into aircraft states linked to rare high-risk anomalies.

Risk detection↗
NASA NTRSDate not supplied

Multiclass Flight Anomaly Detection Using Sensor Fusion Based on Dempster-Shafer Theory

As aviation systems in commercial operations continue to grow in complexity, the anomalies exhibited by these systems become more elaborate and difficult to detect. To address the challenge of detecting these complex anomalies, deep learning models have been used extensively in aviation anomaly detection studies, at the expense of end-user interpretability. Aiming to maintain the same level of interpretability as traditional threshold-exceedance methods, we continue our development of prediction models using ordinal patterns and their distributions throughout the flight. Specifically, this study extends our work into multiclass anomaly detection using sensor fusion based on Dempster-Shafer theory (DST), a second-order probability theory used to combine information from different sources of evidence. Our approach uses DST toreduce the uncertainty in the class predictions of an ensemble of classifiers. These classifiers rely on the similarity between flight data and class templates to make a prediction of the state of the aircraft. Our approach aims to take advantage of simple models trained on interpretable features (ordinal patterns) to correctly predict an anomaly and identify the flight dynamics linked to the anomaly. Our results show an improvement when using DST-based sensor fusion over simple majority voting. Additionally, our results provide insight into aircraft states linked to rare high-risk anomalies.

Risk detection↗
DOE OSTI2017

Measuring changes in ambient noise levels from the installation and operation of a surge wave energy converter in the coastal ocean

Ecosystem impacts resulting from elevated underwater noise levels generated by anthropogenic activities in the coastal ocean are poorly understood and remain difficult to address as a result of a significant gap in knowledge for existing nearshore sound levels. Ambient noise is an important habitat component for marine mammals and fish that use sound for essential functions such as communication, navigation, and foraging. Questions surrounding the amplitudes, frequency distributions, and durations of noise emissions from renewable wave energy conversion (WEC) projects during their construction and operation present concerns for long-term consequences in marine habitats. Oregon’s dynamic nearshore environment presents significant challenges for passive acoustic monitoring that include flow noise contamination from wave orbital motions, turbulence from breaking surf, equipment burial, and fishing pressure from sport and commercial crabbers. This project included 2 techniques for passive acoustic data collection: 1) campaign style deployments of fixed hydrophone lander stations to capture temporal variations in noise levels and 2) a drifting hydrophone system to record spatial variations within the project site. The hydrophone lander deployments were effective and economically feasible for enabling robust temporal measurements of ambient noise levels in a variety of sea state conditions. Limiting factors for the fixed stations included 1) a flow shield mitigation strategy failure in the first deployment resulting in significant wideband data contamination and 2) flow noise contamination of the unshielded sensors restricting valuable analysis to frequencies above 500 Hz for subsequent deployments. Drifting hydrophone measurements were also effective and economically feasible (although logistically challenging in the beginning of the project due to vessel time constraints) providing a spatial distribution of sound levels, comparisons of noise levels in varying levels of vessel traffic during similar sea states, and reducing the frequencies contaminated by flow noise to f < 50 Hz by an effective drifting hydrophone system design strategy. Results from this project can still assist regulatory agencies and WEC developers in permitting and licensing, reducing project costs overall and assisting the economic development of the WEC industry, thus furthering the MHK energy industry and easing the U.S. reliance on foreign oil for energy production. Additionally, results from this project can be used to help inform coastal resource managers and regulatory agencies on existing baseline noise level variability and ecosystem health.

16 TIDAL AND WAVE POWER↗
NASA NTRSDate not supplied

Developing Fast and Accurate Radiative Transfer Models to Meet the Needs of Modern Satellite Remote Sensing Applications

Modern hyperspectral satellite remote sensors provide highly accurate measurements the Earth’s Top-of-Atmosphere (TOA) radiance, reflectance, or polarized spectra with hundreds to thousands of spectral channels and with millions of observations per day. The large data volume and high spectral dimensionality of the data pose challenges for retrieval algorithms. To process the satellite Level-1 data (e.g. calibrated TOA spectra) into Level-2 products (e.g. atmospheric and surface properties) using physical-based retrieval algorithms, accurate and fast Radiative Transfer Models (RTMs) are needed. RTMs are usually the limiting factor in determining the speed of a level-2 algorithm. For example, more than one million Line-by-Line (LBL) radiative transfer (RT) calculations are needed in order to properly capture the spectral contributions of important atmospheric molecules for an IR hyperspectral sensor with a spectral coverage from 3.5 m to 15 m or a solar hyperspectral sensor with spectral coverage from 0.25 m to 2.5 m. In this presentation, we will discuss advantages and disadvantages of different ways (e.g. correlated k and effective transmittance) to accelerate the speed of a fast RTM. We finally describe a Principal Component-based Radiative Transfer Model (PCRTM), which can calculate TOA radiance or reflectance spectra from 50 cm-1 to 40,000 cm-1 (200 m to 0.25 m). It has demonstrated very good accuracy relative to reference LBL RTMs and saves orders of magnitude in computational time. The PCRTM has been used in many satellite remote sensing applications. Examples include forward modeling in Level-2 and Level-3 retrieval algorithms, high fidelity satellite instrument simulators and instrument performance trade studies, spectral and radiometric accuracy characterizations of satellite Level-1 data, tools for inter-satellite calibrations, tools for satellite RTM lookup table generations, and tools for generating physically based training datasets for Artificial Intelligence (AI) algorithms.

climate data record↗
NASA NTRS1995

Solid-state image sensor with focal-plane digital photon-counting pixel array

A photosensitive layer such as a-Si for a UV/visible wavelength band is provided for low light level imaging with at least a separate CMOS amplifier directly connected to each PIN photodetector diode to provide a focal-plane array of NxN pixels, and preferably a separate photon-counting CMOS circuit directly connected to each CMOS amplifier, although one row of counters may be time shared for reading out the photon flux rate of each diode in the array, together with a buffer memory for storing all rows of the NxN image frame before transfer to suitable storage. All CMOS circuitry is preferably fabricated in the same silicon layer as the PIN photodetector diode for a monolithic structure, but when the wavelength band of interest requires photosensitive material different from silicon, the focal-plane array may be fabricated separately on a different semiconductor layer bump-bonded or otherwise bonded for a virtually monolithic structure with one free terminal of each diode directly connected to the input terminal of its CMOS amplifier and digital counter for integration of the photon flux rate at each photodetector of the array.

Fossum, Eric R.↗
NASA NTRS2011

Target Assembly to Check Boresight Alignment of Active Sensors

A compact and portable target assembly (Fig. 1) has been developed to measure the boresite alignment of LRO's Lunar Orbiter Laser Altimeter (LOLA) instrument at the spacecraft level. The concept for this target assembly has evolved over many years with earlier versions used to test the Mars Observer Laser Altimeter (MOLA), the Geoscience Laser Altimeter System (GLAS), and the Mercury Laser Altimeter (MLA) space-based instruments.

Ramos-Izquierdo, Luis↗
NASA NTRS1989

Piezoresistive silicon pressure sensors in cryogenic environment

This paper presents data on low-temperature measurements of silicon pressure sensors. It was found that both the piezoresistance coefficients and the charge-carrier mobility increase with decreasing temperature. For lightly doped semiconductor materials, the density of free charge carriers decreases with temperature and can freeze out eventually. However, the effect of carrier freeze-out can be minimized by increasing the impurity content to higher levels, at which the temperature dependency of piezoresistance coefficients is reduced. An impurity density of 1 x 10 to the 19th/cu cm was found to be optimal for cryogenic applications of pressure sensor dies.

Kahng, Seun K.↗
NASA NTRS1984

Optical-system design for next-generation pushbroom sensors

Next-generation pushbroom sensors for earth observation require high-performance optics that provide high spatial resolution over wide fields of view. Specifically, blur diameters on the order of 10 to 15 microns are needed over 5 to 15 deg fields. In addition to this fundamental level of optical performance, other characteristics, such as spatial coregistration of spectral bands, flat focal plane, telecentricity, and workable pupil location are significant instrument design considerations. The detector-assembly design, optical line-of-sight pointing method and sensor packaging all hinge on these secondary attributes. Moreover, the need for broad spectral coverage, ranging from 0.4 to 12.5 microns, places an additional constraint on optical design. This paper presents alternative design forms that are candidates for wide-field pushbroom sensors, and discusses the instrument-design tradeoffs that are linked to the selection of these alternate optical approaches.

Mika, A. M.↗
DOE OSTI2024

L0 Data from the 2018 NGEE Arctic LiDAR and Imagery Unoccupied Aerial System Campaign at the Teller 27 Field Site, Seward Peninsula, Alaska

Airborne remote sensing data collected from Los Alamos National Laboratory's (LANL) heavy-lift unoccupied aerial system (UAS) hexacopter platform operated by NGEE Arctic scientists from the EES-14 group at Los Alamos National Laboratory. These data were collected in July 2018 at a field site near mile marker 27 along the Teller road between Nome, Alaska and Teller, Alaska. A DJI Matrice 600 Pro Airframe and Routescene UAV LiDARSystem was used to collect LiDAR data along 12 flight paths, and DJI Phantom 4 Advanced was used to collect optical red/green/blue (RGB) imagery at regular intervals along 5 flight paths. This data package contains unprocessed data products (processing level 0) including flight paths, raw photos, and raw lidar data files (*.kml, *.jpg, and *.lpd formats). Ancillary aircraft data, flight mission parameters, and general flight conditions are also included (see Supplemental Files, *.rinex, and *.rtcm3 files). NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.- The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1999

On Orbit Measurement of Response vs. Scan Angle for the Infrared Bands on TRMM/VIRS

The Visible and Infrared Scanner on the Tropical Rainfall Measuring Mission (TRMM/VIRS) is a whiskbroom imaging radiometer with two reflected solar bands and three emissive infrared bands. All five detectors are on a single cooled focal plane. This configuration necessitated the use of a paddlewheel scan mirror to avoid the effects of focal plane rotation that arise when using a scan mirror that is inclined to its axis of rotation. System radiometric requirements led to the need for protected silver as the mirror surface. Unfortunately, the SiO(x) coatings currently used to protect silver from oxidation introduce a change in reflectance with angle of incidence (AOI). This AOI dependence results in a modulation of system level response with scan angle. Measurement of system response vs. scan angle (RVS) was not difficult for the VIRS reflected solar bands, but attaining the required accuracy for the IR bands in the laboratory was not possible without a large vacuum chamber and a considerable amount of custom designed testing apparatus. Therefore, the decision was made to conduct the measurement on-orbit. On three separate occasions, the TRMM spacecraft was rotated about its pitch axis and, after the nadir view passed over the Earth's limb, the VIRS performed several thousand scans while viewing deep space. The resulting data has been analyzed and the RVS curves generated for the three IR bands are being used in the VIRS radiometric calibration algorithm. This, to our knowledge, the first time this measurement has been made on-orbit. Similar measurements are planned for the EOS-AM and EOS-PM MODIS sensors and are being considered for several systems under development. The VIRS on-orbit results will be compared to VIRS and MODIS system level laboratory measurements, MODIS scan mirror witness sample measurements and modeled data.

Barnes, William L.↗
DOE OSTI2022

What you get is not always what you see—pitfalls in solar array assessment using overhead imagery

Effective integration planning for small, distributed solar photovoltaic (PV) arrays into electric power grids requires access to high quality data: the location and power capacity of individual solar PV arrays. Unfortunately, national databases of small-scale solar PV do not exist; those that do are limited in their spatial resolution, typically aggregated up to state or national levels. While several promising approaches for solar PV detection have been published, strategies for evaluating the performance of these models are often highly heterogeneous from study to study. The resulting comparison of these methods for practical applications for energy assessments becomes challenging and may imply that the reported performance evaluations overly optimistic. The heterogeneity comes in many forms, each of which we explore in this work: the degree of diversity of the locations and sensors (e.g. different satellites, aerial photography) from which the training and validation data originate, the validation of ground truth (manual annotation of imagery vs known solar PV locations), the level of spatial aggregation (e.g. array-level vs regional estimates), and inconsistencies in the training and validation datasets (e.g. different datasets are used for each study and those data are not always made accessible). For each, we discuss emerging practices from the literature to address them or suggest directions of future research. As part of our investigation, we evaluate solar PV identification performance in two large regions: the entire state of Connecticut and the city of San Diego, CA. In Connecticut, we also use 33,114 known parcel-level solar PV installations from Berkeley Lab’s Tracking the Sun dataset to evaluate parcel-level performance and evaluate capacity estimates using 169 municipalities. We also make our code (which we call SolarMapper), pre-trained models, training data, and predictions publicly available and provide a web portal for interactively inspecting each prediction that was made. Here our findings suggest that traditional performance evaluation of the automated identification of solar PV from satellite imagery may be optimistic due to common limitations in the validation process. The takeaways from this work are intended to inform and catalyze the large-scale practical application of automated solar PV assessment techniques by energy researchers and professionals.

14 SOLAR ENERGY↗
DOE OSTI2020

Diamond magnetometer enhanced by ferrite flux concentrators

Magnetometers based on nitrogen-vacancy (NV) centers in diamond are promising room-temperature, solid-state sensors. However, their reported sensitivity to magnetic fields at low frequencies (≲1 kHz) is presently ≳10 pTs 1/2 , precluding potential applications in medical imaging, geoscience, and navigation. Here we show that high-permeability magnetic flux concentrators, which collect magnetic flux from a larger area and concentrate it into the diamond sensor, can be used to improve the sensitivity of diamond magnetometers. By inserting an NV-doped diamond membrane between two ferrite cones in a bowtie configuration, we realize a ~ 250-fold increase of the magnetic field amplitude within the diamond. We demonstrate a sensitivity of ~ 0.9pTs 1/2 to magnetic fields in the frequency range between 10 and 1000 Hz. This is accomplished using a dual-resonance modulation technique to suppress the effect of thermal shifts of the NV spin levels. The magnetometer uses 200 mW of laser power and 20 mW of microwave power. This work introduces a new degree of freedom for the design of diamond sensors by using structured magnetic materials to manipulate magnetic fields.

47 OTHER INSTRUMENTATION↗
NASA NTRS2012

Absolute Radiometric Calibration of Narrow-Swath Imaging Sensors with Reference to Non-Coincident Wide-Swath Sensors

An inter-calibration method is developed to provide absolute radiometric calibration of narrow-swath imaging sensors with reference to non-coincident wide-swath sensors. The method predicts at-sensor radiance using non-coincident imagery from the reference sensor and knowledge of spectral reflectance of the test site. The imagery of the reference sensor is restricted to acquisitions that provide similar view and solar illumination geometry to reduce uncertainties due to directional reflectance effects. Spectral reflectance of the test site is found with a simple iterative radiative transfer method using radiance values of a well-understood wide-swath sensor and spectral shape information based on historical ground-based measurements. At-sensor radiance is calculated for the narrow-swath sensor using this spectral reflectance and atmospheric parameters that are also based on historical in situ measurements. Results of the inter-calibration method show agreement on the 2 5 percent level in most spectral regions with the vicarious calibration technique relying on coincident ground-based measurements referred to as the reflectance-based approach. While the variability of the inter-calibration method based on non-coincident image pairs is significantly larger, results are consistent with techniques relying on in situ measurements. The method is also insensitive to spectral differences between the sensors by transferring to surface spectral reflectance prior to prediction of at-sensor radiance. The utility of this inter-calibration method is made clear by its flexibility to utilize image pairings with acquisition dates differing in excess of 30 days allowing frequent absolute calibration comparisons between wide- and narrow-swath sensors.

McCorkel, Joel↗
NASA NTRS1997

XV-15 Structural-Acoustic Data

Tiltrotor aircraft are a potentially viable means of intercity travel. The tiltrotor is able to transport passengers relatively quickly from the center of a city to destinations within a 300-mile radius. For such vehicles to be commercially viable, the interior noise and vibration levels must be acceptable to the passengers. A review of the literature revealed very little structural-acoustic data related to the tiltrotor. For this reason, structural-acoustic measurements were taken aboard an XV-15 tiltrotor. The six flight conditions included five in level flight, nominally 140-220 knots, for airplane mode (nacelle at 0 degrees) and one out-of-ground-effect (OGE) hover (nacelle at 90 degrees). The flight test measurements included nine exterior surface pressures, five structural accelerations, and two interior pressures. These sensors were located near the tip path plane on the port side of the aircraft. One minute of data was acquired at each condition. The data is presented as time histories, autospectra, coherence functions, and cross-spectra. In general, for level flight, the measured data showed very little effect of forward flight speed except to change the amplitude of the response; however, the character of the response was found to be dependent on spatial location. In contrast, in the hover mode the spatial location had very little effect on the character of the response. Additionally, the report highlights: the coherence between the transducer data and the rotor tach signal; and transfer function calculations between the exterior pressures.

Lyle, Karen H.↗
DOE OSTI2022

Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing

Finding actionable trends in laser-based metal additive manufacturing process monitoring data is challenging owing to the diversity and complexity of the underlying physical interactions. A single monitoring solution that captures a particular process phenomenon, such as a photodiode that tracks melt pool intensity, is not alone capable of evaluating process stability or detecting flaw formation with sufficient precision for routine application in industry. In this work, to improve flaw detection performance, we adopted a data fusion approach that captures multiple process phenomena. To demonstrate this, we acquired data from laser powder bed fusion (LPBF) builds of cylindrical specimens produced with different laser spot sizes, emulating defocusing due to process faults such as thermal lensing. The resulting specimens had porosity of varying types and severity, quantified by post-build non-destructive X-ray computed tomography, Archimedes density measurements, and destructive metallographic characterization. During the build, the melt pool state was monitored with two coaxial high-speed video cameras and a temperature field imaging system. Physically intuitive low-level melt pool signatures, such as melt pool temperature, shape and size, and spatter intensity were extracted from this high-dimensional, image-based sensor data. These process signatures were subsequently used as input features in relatively simple machine learning models, such as a support vector machine, which were trained to detect laser defocusing, and in addition, predict porosity type and severity. The results show that the data fusion approach significantly enhanced system performance by reducing the overall false positive rate from ~ 0.1 to ~ 0.001 without sacrificing the true positive rate (~0.90). These results were at par with a black-box, deep machine learning approach (convolutional neural network).

36 MATERIALS SCIENCE↗