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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Remote Sensing Based Yield Monitoring: Application to Winter Wheat in United States and Ukraine

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a new crop yield model based on the Difference Vegetation Index (DVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1 km resolution and the un-mixing of DVI at coarse resolution to a pure wheat signal (100 percent of wheat within the pixel). The model was applied to estimate the national and subnational winter wheat yield in the United States and Ukraine from 2001 to 2017. The model at the subnational level shows very good performance for both countries with a coefficient of determination higher than 0.7 and a root mean square error (RMSE) of lower than 0.6 t/ha (tonnes per hectare) (15-18 percent). At the national level for the United States (US) and Ukraine the model provides a strong coefficient of determination of 0.81 and 0.86, respectively, which demonstrates good performance at this scale. The model was also able to capture low winter wheat yields during years with extreme weather events, for example 2002 in US and 2003 in Ukraine. The RMSE of the model for the US at the national scale is 0.11 t/ha (3.7 percent) while for Ukraine it is 0.27 t/ha (8.4 percent).

DVI↗
DOE OSTI2013

Compensator models for fluence field modulated computed tomography

Purpose: Fluence field modulated computed tomography (FFMCT) presents a novel approach for acquiring CT images, whereby a patient model guides dynamically changing fluence patterns in an attempt to achieve task-based, user-prescribed, regional variations in image quality, while also controlling dose to the patient. This work aims to compare the relative effectiveness of FFMCT applied to different thoracic imaging tasks (routine diagnostic CT, lung cancer screening, and cardiac CT) when the modulator is subject to limiting constraints, such as might be present in realistic implementations.Methods: An image quality plan was defined for a simulated anthropomorphic chest slice, including regions of high and low image quality, for each of the thoracic imaging tasks. Modulated fluence patterns were generated using a simulated annealing optimization script, which attempts to achieve the image quality plan under a global dosimetric constraint. Optimization was repeated under different types of modulation constraints (e.g., fixed or gantry angle dependent patterns, continuous or comprised of discrete apertures) with the most limiting case being a fixed conventional bowtie filter. For each thoracic imaging task, an image quality map (IQM{sub sd}) representing the regionally varying standard deviation is predicted for each modulation method and compared to the prescribed image quality plan as well as against results from uniform fluence fields. Relative integral dose measures were also compared.Results: Each IQM{sub sd} resulting from FFMCT showed improved agreement with planned objectives compared to those from uniform fluence fields for all cases. Dynamically changing modulation patterns yielded better uniformity, improved image quality, and lower dose compared to fixed filter patterns with optimized tube current. For the latter fixed filter cases, the optimal choice of tube current modulation was found to depend heavily on the task. Average integral dose reduction compared to a uniform fluence field ranged from 10% using a bowtie filter to 40% or greater using an idealized modulator.Conclusions: The results support that FFMCT may achieve regionally varying image quality distributions in good agreement with user-prescribed values, while limiting dose. The imposition of constraints inhibits dose reduction capacity and agreement with image quality plans but still yields significant improvement over what is afforded by conventional dose minimization techniques. These results suggest that FFMCT can be implemented effectively even when the modulator has limited modulation capabilities.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2011

A HIGH SPATIAL RESOLUTION STUDY OF THE {lambda} = 3 mm CONTINUUM OF ORION-KL

Recent interferometric observations have called into question the traditional view of the Orion-KL region, which displays one of the most well-defined cases of chemical differentiation in a star-forming region. Previous lower-resolution images of Orion-KL show emission signatures for oxygen-bearing organic molecules toward the Orion Compact Ridge and emission for nitrogen-bearing organic molecules toward the Orion Hot Core. However, more recent observations at higher spatial resolution indicate that the bulk of the molecular emission arises from many smaller, compact clumps that are spatially distinct from the traditional Hot Core and Compact Ridge sources. It is this type of observational information that is critical for guiding astrochemical models, as the spatial distribution of molecules and their relation to energetic sources will govern the chemical mechanisms at play in star-forming regions. We have conducted millimeter-imaging studies of Orion-KL with various beam sizes using the Combined Array for Research in Millimeter-Wave Astronomy in order to investigate the continuum structure. These {lambda} = 3 mm observations have synthesized beam sizes of {approx}0.''5-5.''0. These observations reveal the complex continuum structure of this region, which stands in sharp contrast to the previous structural models assumed for Orion-KL based on lower spatial resolution images. The new results indicate that the spatial scaling previously used in determination of molecular abundances for this region are in need of complete revision. Here we present the results of the continuum observations, discuss the sizes and structures of the detected sources, and suggest an observational strategy for determining the proper spatial scaling to accurately determine molecular abundances in the Orion-KL region.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1980

A theoretical study of microwave beam absorption by a rectenna

The rectenna's microwave power beam absorption limit was theoretically confirmed by two mathematical models descriptive of the microwave absorption process; first one model was based on the current sheet equivalency of a large planar array above a reflector and the second model, which was based on the properties of a waveguide with special imaging characteristics, quantified the electromagnetic modes (field configurations) in the immediate vicinity of a Rectenna element spacing which permit total power beam absorption by preventing unwanted modes from propagating (scattering) were derived using these models. Several factors causing unwanted scattering are discussed.

Ott, J. H.↗
DOE OSTI2021

SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees

Deep unfolding networks have recently gained popularity for solving imaging inverse problems. However, the computational and memory complexity of data-consistency layers within traditional deep unfolding networks scales with the number of measurements, limiting their applicability to large-scale imaging inverse problems. We propose SGD-Net as a new methodology for improving the efficiency of deep unfolding through stochastic approximations of the data-consistency layers. Our theoretical analysis shows that SGD-Net can be trained to approximate batch deep unfolding networks to an arbitrary precision. Our simulations on intensity diffraction tomography and sparse-view computed tomography show that SGD-Net can match the performance of the traditional batch network at a fraction of training and testing complexity.Deep unfolding networks have recently gained popularity for solving imaging inverse problems. However, the computational and memory complexity of data-consistency layers within traditional deep unfolding networks scales with the number of measurements, limiting their applicability to large-scale imaging inverse problems. We propose SGD-Net as a new methodology for improving the efficiency of deep unfolding through stochastic approximations of the data-consistency layers. Our theoretical analysis shows that SGD-Net can be trained to approximate batch deep unfolding networks to an arbitrary precision. Our simulations on intensity diffraction tomography and sparse-view computed tomography show that SGD-Net can match the performance of the traditional batch network at a fraction of training and testing complexity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Convolution Neural Network for Fault Identification in Distribution Feeder with High Penetration Solar PV

Identification and zonal classification of the faults is a decisive factor in the relay’s decision to trip or not. Different types of fault like three-phase, line-to-line-to-ground and single-line-to-ground can occur at various locations in the feeder. These faults are seen as the variation in the instantaneous values of three-phase voltages and currents, i.e., waveforms, that are measured at the relay location. The objective of this work is to develop a machine learning model that can identify a fault and classify it to various protection zones based on measured waveforms. In this work, a data-driven relay based on Convolutional Neural Network (CNN) is proposed for fault identification in distribution feeders with high penetration solar PV. The proposed CNN model takes local current and voltage waveforms as input and classify it into fault, no-fault or a capacitor switching. Further, the CNN also attempts to identify fault zones based on the images of waveforms. The overall testing accuracy of the trained model exceeds 95%.

Ramesh, Meghana↗
DOE OSTI2012

Geophysical Imaging for Investigating the Delivery and Distribution of Amendments in the Heterogeneous Subsurface of the F.E. Warren AFB: ESTCP Project ER-200834

This study focused on improving our ability to develop conceptual amendment delivery models for in situ bioremediation and is based on the premise that geophysical imaging of amendment emplacement via hydraulic fracturing can reduce uncertainty in design and performance monitoring phases, thereby increasing efficacy and cost effectiveness of the remedial treatment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

DETECTING FIRE WITH MACHINE LEARNING-ENABLED VISUAL MONITORING FOR NUCLEAR POWER PLANT ENVIRONMENTS

Nuclear power plants are experiencing significant cost challenges to remain competitive with other energy-generation utilities. Unlike other industries, the cost of operation and maintenance activities is mostly attributed to workforce costs. To mitigate this, nuclear power plant stakeholders are increasingly interested in the development and deployment of machine learning methods to potentially automate or augment manually intensive tasks to reduce costs, especially for monitoring activities. One monitoring function that is visually demanding and that can occur frequently to meet the requirements of a fire protection program is visually monitoring an area for fire occurrence. Currently, fire watch activities consist of a worker physically stationed at a given location with the sole responsibility of observing a given area to ensure a fire is detected and mitigated promptly. This effort focused on the development and evaluation of a suitable deep convolutional neural network to classify individual video frames at a sub-second frequency for the occurrence of “fire” and “no fire” in varying industrial environments similar to nuclear power plants. It is believed that a trained neural network model could be integrated with existing facility video surveillance camera feeds to generate alerts when fire inferences occur in individual frames captured at sub-second temporal resolutions. Extensive effort was dedicated to identifying and curating suitable imagery training data representing varying environments and scene settings with and without flame features to maximize generalization in nuclear power plant environments. The data collection effort resulted in the aggregation of a large, labeled image library exceeding 12,000 images to support model training for diverse industrial environments. A deep neural network model incorporating parallel multi-scale capabilities was developed and trained to support accurate image-based detection of flame incidents of varying sizes and spectral feature properties within heterogeneous scenes. Analysis results show that the trained model can achieve high inference accuracy despite heterogeneous scene environments and components. Testing accuracy exceeded 95.0 percent with very low false positive and false negative inferences.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

THREE-DIMENSIONAL RADIO AND X-RAY MODELING AND DATA ANALYSIS SOFTWARE: REVEALING FLARE COMPLEXITY

Many problems in solar physics require analysis of imaging data obtained in multiple wavelength domains with differing spatial resolution in a framework supplied by advanced three-dimensional (3D) physical models. To facilitate this goal, we have undertaken a major enhancement of our IDL-based simulation tools developed earlier for modeling microwave and X-ray emission. The enhanced software architecture allows the user to (1) import photospheric magnetic field maps and perform magnetic field extrapolations to generate 3D magnetic field models; (2) investigate the magnetic topology by interactively creating field lines and associated flux tubes; (3) populate the flux tubes with user-defined nonuniform thermal plasma and anisotropic, nonuniform, nonthermal electron distributions; (4) investigate the spatial and spectral properties of radio and X-ray emission calculated from the model; and (5) compare the model-derived images and spectra with observational data. The package integrates shared-object libraries containing fast gyrosynchrotron emission codes, IDL-based soft and hard X-ray codes, and potential and linear force-free field extrapolation routines. The package accepts user-defined radiation and magnetic field extrapolation plug-ins. We use this tool to analyze a relatively simple single-loop flare and use the model to constrain the magnetic 3D structure and spatial distribution of the fast electrons inside this loop. We iteratively compute multi-frequency microwave and multi-energy X-ray images from realistic magnetic flux tubes obtained from pre-flare extrapolations, and compare them with imaging data obtained by SDO, NoRH, and RHESSI. We use this event to illustrate the tool's use for the general interpretation of solar flares to address disparate problems in solar physics.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1995

Movement of water vapor in the atmosphere measured by an imaging spectrometer at Rogers Dry Lake, CA

Movement of water as vapor in the atmosphere is a fundamental process in the Earth's hydrological cycle. Investigations of spatial and time scales of water vapor transport in the atmosphere are important areas of research. Water vapor transmits energy as a function of its abundance across the spectrum. This is shown in the 400- to 2500-nm spectral region where the transmission of the terrestrial atmosphere has been modeled using the MODTRAN radiative transfer code for a range of water vapor abundances. Based on these model results, spectra measured by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) have been used to investigate the movement of water vapor at 20-m spatial resolution over an 11-by-30-km area at approximately 15-minute time intervals (1.25 hours total). AVIRIS measures the upwelling spectral radiance from 400 to 2500 nm at 10-nm spectral intervals and collects images of 11-by-up-to-1000 km at 20-m spatial resolution. Data are collected at a rate of 1 km of flight line per 4.5 seconds. A set of five AVIRIS flight lines was acquired in rapid succession over Rogers Dry Lake, CA on May 18, 1993 at 18:59, 19:13, 19:29, 19:47, and 19:59 UTC. Rogers Dry lake is located 2 hours north of Los Angeles, CA at 34.84 degrees north latitude and 117.83 degrees west longitude in the Mojave Desert.

Green, Robert O.↗
DOE OSTI2015

Respiratory Tract Lung Geometry and Dosimetry Model for Male Sprague-Dawley Rats

While inhalation toxicological studies of various compounds have been conducted using a number of different strains of rats, mechanistic dosimetry models have only had tracheobronchial (TB) structural data for Long-Evans rats, detailed morphometric data on the alveolar region of Sprague-Dawley rats and limited alveolar data on other strains. Based upon CT imaging data for two male Sprague-Dawley rats, a 15-generation, symmetric typical path model was developed for the TB region. Literature data for the alveolar region of Sprague-Dawley rats were analyzed to develop an eight-generation model, and the two regions were joined to provide a complete lower respiratory tract model for Sprague-Dawley rats. The resulting lung model was used to examine particle deposition in Sprague-Dawley rats and to compare these results with predicted deposition in Long-Evans rats. Relationships of various physiologic variables and lung volumes were either developed in this study or extracted from the literature to provide the necessary input data for examining particle deposition. While the lengths, diameters and branching angles of the TB airways differed between the two Sprague-Dawley rats, the predicted deposition patterns in the three major respiratory tract regions were very similar. Between Sprague-Dawley and Long-Evans rats, significant differences in TB and alveolar predicted deposition fractions were observed over a wide range of particle sizes, with TB deposition fractions being up to 3- to 4-fold greater in Sprague-Dawley rats and alveolar deposition being significantly greater in Long-Evans rats. Thus, strain-specific lung geometry models should be used for particle deposition calculations and interspecies dose comparisons.

Miller, Frederick J.↗
DOE OSTI2015

Au and Pt nanoparticle supported catalysts tailored for H-2 production: From models to powder catalysts

The use of metal nanoparticles (NPs), including Au and Pt, supported over oxides has been pivotal, and is ever increasing in enabling catalytic reactions which target the production of hydrogen. We review here the most recent works pertaining to the fundamental understanding of the structure, morphology, growth, characterization, and intrinsic phenomenological properties of Au– and Pt– based catalysts that influence the reactivity and selectivity to target hydrogen production. We draw on surface science and theoretical methods of model and powder catalysts using high resolution imaging, spectroscopy, scattering experiments, and theoretical studies. Based on these insights we identify key aspects of studies of supported metal nanoparticle (NP) catalysts for several reactions. The main focus of this review is on the intersection of catalytic chemistry related to the water-gas shift (WGS), oxygenate steam reforming (OSR), and solarassisted reactions (SAR).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2021

Landsat Derived Bathymetry of Lakes on the Arctic Coastal Plan of Northern Alaska

The Pleistocene sand sea on the Arctic Coastal Plain (ACP) of northern Alaska is underlain by anancient sand dune field, a geological feature that affects regional lake characteristics. Many of these lakes, whichcover approximately 20 % of the Pleistocene sand sea, are relatively deep (up to 25 m). In addition to the nat-ural importance of ACP sand sea lakes for water storage, energy balance, and ecological habitat, the need forwinter water for industrial development and exploration activities makes lakes in this region a valuable resource.However, ACP sand sea lakes have received little prior study. Here, we collect in situ bathymetric data to test12 model variants for predicting sand sea lake depth based on analysis of Landsat-8 Operational Land Imager(OLI) images. Lake depth gradients were measured at 17 lakes in midsummer 2017 using a Humminbird 798ciHD SI Combo automatic sonar system. The field-measured data points were compared to red–green–blue (RGB)bands of a Landsat-8 OLI image acquired on 8 August 2016 to select and calibrate the most accurate spectral-depth model for each study lake and map bathymetry. Exponential functions using a simple band ratio (withbands selected based on lake turbidity and bed substrate) yielded the most successful model variants. For eachlake, the most accurate model explained 81.8 % of the variation in depth, on average. Modeled lake bathymetrieswere integrated with remotely sensed lake surface area to quantify lake water storage volumes, which rangedfrom 1.056×10−3to 57.416×10−3km3. Due to variations in depth maxima, substrate, and turbidity betweenlakes, a regional model is currently infeasible, rendering necessary the acquisition of additional in situ datawith which to develop a regional model solution. Estimating lake water volumes using remote sensing will fa-cilitate better management of expanding development activities and serve as a baseline by which to evaluatefuture responses to ongoing and rapid climate change in the Arctic. All sonar depth data and modeled lakebathymetry rasters can be freely accessed at https://doi.org/10.18739/A2SN01440 (Simpson and Arp, 2018) andhttps://doi.org/10.18739/A2HT2GC6G (Simpson, 2019), respectively.

Claire E Simpson↗
DOE OSTI2019

ENSEMBLE KALMAN FILTERS FOR RELIABILITY ESTIMATION IN PERFUSION INFERENCE

We consider the solution of inverse problems in dynamic contrast–enhanced imaging by means of ensemble Kalman filters. Our quantity of interest is blood perfusion, i.e., blood flow rates in tissue. While existing approaches to compute blood perfusion parameters for given time series of radiological measurements mainly rely on deterministic, deconvolution–based methods, we aim at recovering probabilistic solution information for given noisy measurements. To this end, we model radiological image capturing as a sequential data assimilation process and solve it by an ensemble Kalman filter. Thereby, we recover deterministic results as an ensemble–based mean and are able to compute reliability information such as probabilities for the perfusion to be in a given range. Our target application is the inference of blood perfusion parameters in the human brain. A numerical study shows promising results for artificial measurements generated by a digital perfusion phantom.

Engineering↗
DOE OSTI2016

Model for mapping settlements

A programmable media includes a graphical processing unit in communication with a memory element. The graphical processing unit is configured to detect one or more settlement regions from a high resolution remote sensed image based on the execution of programming code. The graphical processing unit identifies one or more settlements through the execution of the programming code that executes a multi-instance learning algorithm that models portions of the high resolution remote sensed image. The identification is based on spectral bands transmitted by a satellite and on selected designations of the image patches.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1987

Cratering and obliteration history of the south polar region of Mars

Results are presented from a comprehensive analysis of crater size frequency distributions compiled from Viking Orbiter images of Martian south polar terrains. Depositional histories for the various terrain units are modelled based on deviations of cumulative crater size frequency plots from an assumed production function. Stratigraphic and morphologic data obtained from visual examination of Viking images are also used, primarily to corroborate inferences concerning ages and obliteration histories derived from the crater data.

Plaut, J. J.↗
NASA NTRS1995

Saturn's central flash from the 3 July 1989 occultation of 28 Sgr

We present observations of Saturn's central flash obtained from Palomar and McDonald Observatories during the 3 July 1989 occultation of 28 Sgr. As the star passed close to the geometric center of Saturn's shadow, the focusing of the incident starlight by the planet's atmosphere formed multiple stellar images along the limb which were detected in infrared images obtained at wavelengths of 3.9 (Palomar) and 2.1 microns (McDonald). These are the first reported observations of a central flash due to Saturn, and the first of any planet in which the signal flash from each stellar image could be determined separately, permitting a comparison of both intensity and position for each image with model predictions. Four separate flashes were observed from each observatory, corresponding to points on the limb where the starlight passed through the Cassini Division and the relatively transparent C Ring, with maximum brightness reaching 1-2% of the unocculted stellar intensity. The timing of the flashes is quite sensitive to the shape of Saturn's limb, which depends in turn on the planet's zonal gravity harmonics and on the zonal wind profile in the lower stratosphere, near the 2.5-mbar pressure level. The locations of the images along the limb, as well as the timing, shapes, and amplitudes of the individual flash light curves, are well matched by a smoothed model based on a tropospheric zonal wind profile obtained from tracking cloud features in Voyager images and the Saturn ring optical depth profile obtained from the Voyager Photopolarimeter experiment. The smoothing required to give the best match to the data exceeds that attributable to the finite angular extent of the occulted star and may be due to refractive scattering by turbulence or wave structure in Saturn's atmosphere. There is no evidence of significant atmospheric absorption at the observed wavelengths, which correspond to spectral regions of weak methane absorption.

Nicholson, Philip D.↗
DOE OSTI2024

AstroCLIP: a cross-modal foundation model for galaxies

ABSTRACT We present AstroCLIP, a single, versatile model that can embed both galaxy images and spectra into a shared, physically meaningful latent space. These embeddings can then be used – without any model fine-tuning – for a variety of downstream tasks including (1) accurate in-modality and cross-modality semantic similarity search, (2) photometric redshift estimation, (3) galaxy property estimation from both images and spectra, and (4) morphology classification. Our approach to implementing AstroCLIP consists of two parts. First, we embed galaxy images and spectra separately by pre-training separate transformer-based image and spectrum encoders in self-supervised settings. We then align the encoders using a contrastive loss. We apply our method to spectra from the Dark Energy Spectroscopic Instrument and images from its corresponding Legacy Imaging Survey. Overall, we find remarkable performance on all downstream tasks, even relative to supervised baselines. For example, for a task like photometric redshift prediction, we find similar performance to a specifically trained ResNet18, and for additional tasks like physical property estimation (stellar mass, age, metallicity, and specific-star-formation rate), we beat this supervised baseline by 19 per cent in terms of R2. We also compare our results with a state-of-the-art self-supervised single-modal model for galaxy images, and find that our approach outperforms this benchmark by roughly a factor of two on photometric redshift estimation and physical property prediction in terms of R2, while remaining roughly in-line in terms of morphology classification. Ultimately, our approach represents the first cross-modal self-supervised model for galaxies, and the first self-supervised transformer-based architectures for galaxy images and spectra.

Parker, Liam (ORCID:0009000749521674)↗