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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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A14H-04 Analysis of Simulated and Observed Trends in Global Surface PM2.5 and Aerosol Optical Properties from 1958 to 2018 Using the NASA GEOSCCM

Modeling of long-term trends of aerosols and their properties is important for constraining aerosol-climate forcing, and for characterizing changes in particulate matter pollution speciation and exposure. Here we study global and regional long-term trends in surface fine particulate matter (PM ) and aerosol optical properties for 60 years from 1958 to 2018, using simulations performed with the NASA Goddard Earth Observing System Chemistry Climate Model (GEOSCCM), and evaluate the model hindcast with observations for the last three decades. Comparing the modeled aerosols with a diverse set of observations helps interpret observed and simulated trends, and serves as a benchmark for future GEOSCCM developments and input datasets improvements. We first characterize modeled global and regional temporal changes in surface PM and its components, aerosol optical depth (AOD) and single scattering albedo (SSA) and we interpret their link with emissions drivers. We then compare modeled surface PM with ground-based observations from monitoring networks and with global reconstructed PM datasets from observations-model data fusion. Total AOD is compared with long-term satellite measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) and measurements from the ground-based Aerosol Robotic Network (AERONET). Additional aerosol optical properties, such as absorption and scattering coefficients, are evaluated with ground-based observations from the Global Atmosphere Watch (GAW) records.

aerosols↗
NASA NTRS1993

New techniques for reversible compression of multispectral data

While spatial correlations are adequately exploited by standard lossless image compression techniques, little success has been attained in exploiting spectral correlations when dealing with multispectral image data. In this paper, we present some new lossless image compression techniques that capture spectral correlations as well as spatial correlation in a simple and elegant manner. The schemes are based on the notion of a prediction tree, which defines a non-causal prediction model for an image. We present a backward adaptive technique and a forward adaptive technique. We then give a computationally efficient way of approximating the backward adaptive technique. The approximation gives good results and is extremely easy to compute. Simulation results show that for high spectral resolution images, significant savings can be made by using spectral correlations in addition to spatial correlations. Furthermore, the increase in complexity incurred in order to make these gains is minimal.

Memon, N. D.↗
DOE OSTI2022

Automated bubble analysis of high-speed subcooled flow boiling images using U-net transfer learning and global optical flow

Capturing and analyzing the bubble dynamics is crucial to improving the understanding of boiling heat transfer mechanisms and predicting boiling heat transfer coefficient and boiling crisis. High speed video (HSV) imaging has been used for decades towards this end. Still, there is no universal approach to quantitatively analyze bubble dynamics from HSV images. In this study, we propose a data-driven post-processing approach to segment, track, and identify wall-attached vapor bubbles from HSV images of the boiling process in subcooled flow conditions. Firstly, we employ a transfer learning framework with a U-Net-based convolution neural network (CNN) architecture to detect and segment bubbles in HSV images of diverse contrast and surface texture using very little data (e.g., 10 images) for training. Then, we evaluate the trained CNN model with 100 ground-truth images, and the validation results show that the model accuracy and precision in detecting the optical footprint of bubbles are higher than 90%. Finally, we suggest a criterion to identify a condensing bubble based on the divergence of the bubble displacement, which is calculated from sequential segmented bubble images using a global optical flow code. Using this combination of machine learning and optical flow, we can identify nucleation sites and track the growth of bubbles nucleating at each site to quantify nucleation site density, nucleation frequency, and other fundamental boiling parameters. The proposed system is validated using results obtained on a special heater, which enables both infrared (IR) thermometry and HSV imaging on a metallic surface. We compare the fundamental boiling parameters obtained by the two different diagnostics. The results show good agreement. In conclusion, the difference between the measurements of nucleation site density, averaged nucleation frequency, and averaged growth time performed with the two techniques is always within ± 20% and mostly ± 10% of the values measured with IR thermometry.

42 ENGINEERING↗
DOE OSTI2015

Monte Carlo modeling of ultrasound probes for image guided radiotherapy

Purpose: To build Monte Carlo (MC) models of two ultrasound (US) probes and to quantify the effect of beam attenuation due to the US probes for radiation therapy delivered under real-time US image guidance. Methods: MC models of two Philips US probes, an X6-1 matrix-array transducer and a C5-2 curved-array transducer, were built based on their megavoltage (MV) CT images acquired in a Tomotherapy machine with a 3.5 MV beam in the EGSnrc, BEAMnrc, and DOSXYZnrc codes. Mass densities in the probes were assigned based on an electron density calibration phantom consisting of cylinders with mass densities between 0.2 and 8.0 g/cm{sup 3}. Beam attenuation due to the US probes in horizontal (for both probes) and vertical (for the X6-1 probe) orientation was measured in a solid water phantom for 6 and 15 MV (15 × 15) cm{sup 2} beams with a 2D ionization chamber array and radiographic films at 5 cm depth. The MC models of the US probes were validated by comparison of the measured dose distributions and dose distributions predicted by MC. Attenuation of depth dose in the (15 × 15) cm{sup 2} beams and small circular beams due to the presence of the probes was assessed by means of MC simulations. Results: The 3.5 MV CT number to mass density calibration curve was found to be linear with R{sup 2} > 0.99. The maximum mass densities in the X6-1 and C5-2 probes were found to be 4.8 and 5.2 g/cm{sup 3}, respectively. Dose profile differences between MC simulations and measurements of less than 3% for US probes in horizontal orientation were found, with the exception of the penumbra region. The largest 6% dose difference was observed in dose profiles of the X6-1 probe placed in vertical orientation, which was attributed to inadequate modeling of the probe cable. Gamma analysis of the simulated and measured doses showed that over 96% of measurement points passed the 3%/3 mm criteria for both probes placed in horizontal orientation and for the X6-1 probe in vertical orientation. The X6-1 probe in vertical orientation caused the highest attenuation of the 6 and 15 MV beams, which at 10 cm depth accounted for 33% and 43% decrease compared to the respective (15 × 15) cm{sup 2} open fields. The C5-2 probe in horizontal orientation, on the other hand, caused a dose increase of 10% and 53% for the 6 and 15 MV beams, respectively, in the buildup region at 0.5 cm depth. For the X6-1 probe in vertical orientation, the dose at 5 cm depth for the 3-cm diameter 6 MV and 5-cm diameter 15 MV beams was attenuated compared to the corresponding open fields to a greater degree by 65% and 43%, respectively. Conclusions: MC models of two US probes used for real-time image guidance during radiotherapy have been built. Due to the high beam attenuation of the US probes, the authors generally recommend avoiding delivery of treatment beams that intersect the probe. However, the presented MC models can be effectively integrated into US-guided radiotherapy treatment planning in cases for which beam avoidance is not practical due to anatomy geometry.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Do Remote Camera Arrangements and Image Capture Settings Improve Individual Identification of Golden Eagles?

Individual identification of animals from camera traps has become an important task in wildlife research, but camera deployment methods often do not facilitate this important undertaking. Identification of individual golden eagles (Aquila chrysaetos) is possible using uniquely marked rectrices, but no studies have explored methods to maximize the rate of individual identification from camera images. Furthermore, our objectives were to assess whether different camera heights (1 m vs. 3 m), image capture settings (one image after a 1-min delay vs. burst of 5 images after a 30 sec delay), and arrangements relative to bait (dorsally vs. ventrally aimed) affected views of rectrices on golden eagles and our ability to identify individuals. We conducted our study from 15 December 2016 to 3 March 2017 on the Savannah River Site, South Carolina. First, we developed a scoring system based on views of rectrices and used a linear mixed-effects model to compare image scores among different camera arrangements and image settings. Next, after identifying individual eagles, we used generalized linear mixed-effects models to compare total individual eagle detections, total days an individual was detected, and probability of obtaining an unknown individual identification among camera arrangements and settings. Overall, we scored a total of 27,499 images, with 8,083 providing views of marked rectrices that allowed identification of 18 individual eagles. Average image scores and proportion of images suitable for individual identification were higher from elevated (3 m) camera arrangements than standard arrangements (1 m) across sites. Regardless of camera height, faster frequency of image capture provided more images that could be used to identify individuals and the most trap days per individual. Researchers and managers should consider deploying elevated cameras traps with faster frequency of image capture to improve data quality and potential for analysis of golden eagle populations and trends across the species’ range.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Experimental Investigation of Laws during Deformation of Circular Roadway in 10° Inclined Layered Rock

In order to analyze the deformation characteristics and mechanisms caused by high tectonic stress in gentle dip strata, a physical modeling with circular tunnel was designed. The model was constructed by the so-called “Physically Finite Elemental Slab Assemblage (PFESA)” to bring about the structural effect of the deep strata. The gravity was fixed while the lateral pressure increased step by step to mimic high tectonic stress. In order to observe the displacement in different area, the sketches of monitoring points and frame in the model surface were drawn down through video pictures in different periods and to be compared. For the sake of analyzing the deformation and failure mechanism of layers, rock structural mechanics models were set up for the left side and right side in the same stratum, respectively. For verifying the experimental phenomenon and its mechanism, infrared images were utilized based on the temperature variation mechanisms of material. Through systematic study, this paper enriches the research methods of model test and can provide a certain reference for practical engineering of similar conditions.

42 ENGINEERING↗
NASA NTRS2015

Space-Based Remote Sensing of Atmospheric Aerosols: The Multi-Angle Spectro-Polarimetric Frontier

The review of optical instrumentation, forward modeling, and inverse problem solution for the polarimetric aerosol remote sensing from space is presented. The special emphasis is given to the description of current airborne and satellite imaging polarimeters and also to modern satellite aerosol retrieval algorithms based on the measurements of the Stokes vector of reflected solar light as detected on a satellite. Various underlying surface reflectance models are discussed and evaluated.

polarimetry↗
NASA NTRS2018

Developing a Remotely Sensed Drought Monitoring Indicator for Morocco

Drought is one of the most serious climatic and natural disasters inflicting serious impacts on the socio-economy of Morocco, which is characterized both by low-average annual rainfall and high irregularity in the spatial distribution and timing of precipitation across the country. This work aims to develop a comprehensive and integrated method for drought monitoring based on remote sensing techniques. The main input parameters are derived monthly from satellite data at the national scale and are then combined to generate a composite drought index presenting different severity classes of drought. The input parameters are: Standardized Precipitation Index calculated from satellite-based precipitation data since 1981 (CHIRPS), anomalies in the day-night difference of Land Surface Temperature as a proxy for soil moisture, Normalized Difference Vegetation Index anomalies from Moderate Resolution Imaging Spectroradiometer (MODIS) data and Evapotranspiration anomalies from surface energy balance modeling. All of these satellite-based indices are being used to monitor vegetation condition, rainfall and land surface temperature. The weighted combination of these input parameters into one composite indicator takes into account the importance of the rainfall-based parameter (SPI). The composite drought index maps were generated during the growing seasons going back to 2003. These maps have been compared to both the historical, in situ precipitation data across Morocco and with the historical yield data across different provinces with information being available since 2000. The maps are disseminated monthly to several main stakeholders' groups including the Ministry of Agriculture and Department of Water in Morocco.

Drought monitoring↗
DOE OSTI2020

Removing imaging systematics from galaxy clustering measurements with Obiwan: application to the SDSS-IV extended Baryon Oscillation Spectroscopic Survey emission-line galaxy sample

This article presents the application of a new tool, Obiwan, which uses image simulations to determine the selection function of a galaxy redshift survey and calculate three-dimensional (3D) clustering statistics. Obiwan relies on a forward model of the process by which images of the night sky are transformed into a 3D large-scale structure catalogue, and offers several advantages over more traditional map-based techniques – such as operating on individual exposures and adopting a maximum likelihood approach. The photometric pipeline automatically detects and models galaxies and then generates a catalogue of such galaxies with detailed information for each one of them, including their location, redshift, and so on. Systematic biases in the imaging data are therefore imparted into the catalogues and must be accounted for in any scientific analysis of their information content. Obiwan simulates this process for samples selected from the Legacy Surveys imaging data. This imaging data will be used to select target samples for the next-generation Dark Energy Spectroscopic Instrument (DESI) experiment. Here, we apply Obiwan to a portion of the SDSS-IV extended Baryon Oscillation Spectroscopic Survey emission-line galaxies (ELGs). Systematic biases in the data are clearly identified and removed. We compare the 3D clustering results to those obtained by the map-based approach applied to the complete eBOSS Data Release 16 (DR16) sample. We find the results are consistent, thereby validating the eBOSS DR16 ELG catalogues, which is used to obtain cosmological results.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Calibration of elastoplastic constitutive model parameters from full-field data with automatic differentiation-based sensitivities

Here, we present a framework for calibration of parameters in elastoplastic constitutive models that is based on the use of automatic differentiation (AD). The model calibration problem is posed as a partial differential equation-constrained optimization problem where a finite element (FE) model of the coupled equilibrium equation and constitutive model evolution equations serves as the constraint. The objective function quantifies the mismatch between the displacement predicted by the FE model and full-field digital image correlation data, and the optimization problem is solved using gradient-based optimization algorithms. Forward and adjoint sensitivities are used to compute the gradient at considerably less cost than its calculation from finite difference approximations. Through the use of AD, we need only to write the constraints in terms of AD objects, where all of the derivatives required for the forward and inverse problems are obtained by appropriately seeding and evaluating these quantities. We present three numerical examples that verify the correctness of the gradient, demonstrate the AD approach's parallel computation capabilities via application to a large-scale FE model, and highlight the formulation's ease of extensibility to other classes of constitutive models.

42 ENGINEERING↗
DOE OSTI2014

Prognostic Value and Reproducibility of Pretreatment CT Texture Features in Stage III Non-Small Cell Lung Cancer

Purpose: To determine whether pretreatment CT texture features can improve patient risk stratification beyond conventional prognostic factors (CPFs) in stage III non-small cell lung cancer (NSCLC). Methods and Materials: We retrospectively reviewed 91 cases with stage III NSCLC treated with definitive chemoradiation therapy. All patients underwent pretreatment diagnostic contrast enhanced computed tomography (CE-CT) followed by 4-dimensional CT (4D-CT) for treatment simulation. We used the average-CT and expiratory (T50-CT) images from the 4D-CT along with the CE-CT for texture extraction. Histogram, gradient, co-occurrence, gray tone difference, and filtration-based techniques were used for texture feature extraction. Penalized Cox regression implementing cross-validation was used for covariate selection and modeling. Models incorporating texture features from the 33 image types and CPFs were compared to those with models incorporating CPFs alone for overall survival (OS), local-regional control (LRC), and freedom from distant metastases (FFDM). Predictive Kaplan-Meier curves were generated using leave-one-out cross-validation. Patients were stratified based on whether their predicted outcome was above or below the median. Reproducibility of texture features was evaluated using test-retest scans from independent patients and quantified using concordance correlation coefficients (CCC). We compared models incorporating the reproducibility seen on test-retest scans to our original models and determined the classification reproducibility. Results: Models incorporating both texture features and CPFs demonstrated a significant improvement in risk stratification compared to models using CPFs alone for OS (P=.046), LRC (P=.01), and FFDM (P=.005). The average CCCs were 0.89, 0.91, and 0.67 for texture features extracted from the average-CT, T50-CT, and CE-CT, respectively. Incorporating reproducibility within our models yielded 80.4% (±3.7% SD), 78.3% (±4.0% SD), and 78.8% (±3.9% SD) classification reproducibility in terms of OS, LRC, and FFDM, respectively. Conclusions: Pretreatment tumor texture may provide prognostic information beyond that obtained from CPFs. Models incorporating feature reproducibility achieved classification rates of ∼80%. External validation would be required to establish texture as a prognostic factor.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

A semblance measure for model comparison

Algorithmic and computational advances have made it possible that geophysical survey and earth model design can be aided by many systematic trial inverse-modelling runs with synthetic data. Such may, for example, come up in machine-learning approaches. Automated image appraisal pertaining to such applications will involve common statistical tests for goodness-of-data fit as a primary evaluation method. However, solution non-uniqueness may render multiple images equivalent in terms of their data fit, requiring secondary categorizers. A logical choice for classifying synthetic-imaging results quantifies the goodness of model fit where a known reference model replaces the observational input. The task of model intercomparison in terms of measuring the resemblance to the reference model poses challenges to common distance-based metrics like root mean square error and mean absolute error. First, distance-based metrics can introduce spurious contributions when smooth models with fuzzy target contours are to be compared against a sharp reference. Second, large differences due to parameter-estimation overshoots can dominate distance metrics. Here, we propose a remedy that is referred to as semblance and is based on the idea of logistic functions, where a binary-dependent variable adds non-zero or zero accumulation terms for the, respectively, passing or failing of preset target thresholds. This classifying approach is amenable to an objective where model feature recognition is primary. Numerical comparisons to distance-based metrics provide evidence for the advantages of the semblance in view of this objective. Geophysical imaging in conjunction with machine-learning is seen as a benefitting upcoming application area.

58 GEOSCIENCES↗
NASA NTRS2003

Synthesis of Multispectral Bands from Hyperspectral Data: Validation Based on Images Acquired by AVIRIS, Hyperion, ALI, and ETM+

Spectral band synthesis is a key step in the process of creating a simulated multispectral image from hyperspectral data. In this step, narrow hyperspectral bands are combined into broader multispectral bands. Such an approach has been used quite often, but to the best of our knowledge accuracy of the band synthesis simulations has not been evaluated thus far. Therefore, the main goal of this paper is to provide validation of the spectral band synthesis algorithm used in the ART software. The next section contains a description of the algorithm and an example of its application. Using spectral responses of AVIRIS, Hyperion, ALI, and ETM+, the following section shows how the synthesized spectral bands compare with actual bands, and it presents an evaluation of the simulation accuracy based on results of MODTRAN modeling. In the final sections of the paper, simulated images are compared with data acquired by actual satellite sensors. First, a Landsat 7 ETM+ image is simulated using an AVIRIS hyperspectral data cube. Then, two datasets collected with the Hyperion instrument from the EO-1 satellite are used to simulate multispectral images from the ALI and ETM+ sensors.

Blonski, Slawomir↗
NASA NTRS1989

Development of an infrared imaging system for the surface tension driven convection experiment

An infrared imaging system is used to quantify the imposed surface temperature distribution along a liquid/gas free surface in support of the Surface Tension Driven Convection Experiment, a planned Space Transportation System flight experiment. For ground-based work a commercially available instrument was used to determine the feasibility of using this type of imaging system for this experiment. The ground-based work was used as a baseline for compiling specifications for a flight qualified imager to be designed, fabricated, tested and qualified for flight. The requirements and the specifications for the flight model are given along with the reasons for departures from the ground-based equipment. The flight qualification requirements discussed are a representative sample of the necessary procedures which must be followed to flight qualify diagnostic equipment for use aboard the STS. The potential problems and concerns associated with operating an imaging system on orbit are also discussed.

Pline, Alexander D.↗
NASA NTRS2017

Planetary Crater Detection and Registration Using Marked Point Processes, Graph Cut Algorithms, and Wavelet Transforms

This paper addresses the problem of semi-automatic image registration on planetary images. A joint feature-based and area-based approach is proposed. Firstly, the most relevant craters are extracted from the two images to register, and then, registration is performed in two steps. The first step matches the craters extracted from the images based on a generalized Hausdorff distance. In the second step, the mutual information between the two images is maximized to achieve high registration accuracy. Craters are detected by a stochastic-geometry approach based on a marked point process model and of a multiple-birth-and-cut energy minimization algorithm. The experimental validation is carried out with 13 images for the crater extraction stage, and with 20 semi-synthetic pairs of images with ground truth and several images extracted from actual multi-temporal lunar scenes for the registration phase.

Image Processing; Pattern Recognition↗
DOE OSTI2013

DETECTION OF SUBSTRUCTURE IN THE GRAVITATIONALLY LENSED QUASAR MG0414+0534 USING MID-INFRARED AND RADIO VLBI OBSERVATIONS

We present 11.2 {mu}m observations of the gravitationally lensed, radio-loud z{sub s} = 2.64 quasar MG0414+0534, obtained using the Michelle camera on Gemini North. We find a flux ratio anomaly of A2/A1 = 0.93 {+-} 0.02 for the quasar images A1 and A2. When combined with the 11.7 {mu}m measurements from Minezaki et al., the A2/A1 flux ratio is nearly 5{sigma} from the expected ratio for a model based on the two visible lens galaxies. The mid-IR flux ratio anomaly can be explained by a satellite (substructure), 0.''3 northeast of image A2, as can the detailed very long baseline interferometry (VLBI) structures of the jet produced by the quasar. When we combine the mid-IR flux ratios with high-resolution VLBI measurements, we find a best-fit mass between 10{sup 6.2} and 10{sup 7.5} M{sub Sun} inside the Einstein radius for a satellite substructure modeled as a singular isothermal sphere at the redshift of the main lens (z{sub l} = 0.96). We are unable to set an interesting limit on the mass to light ratio due to its proximity to the quasar image A2. While the observations used here were technically difficult, surveys of flux anomalies in gravitational lenses with the James Webb Space Telescope will be simple, fast, and should well constrain the abundance of substructure in dark matter halos.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A microstructure-based modeling approach to predict the mechanical properties of Zr alloy with hydride precipitates

In nuclear reactors, hydrides can form in fuel cladding due to hydrogen absorption in ZIrcaloy and cause embrittlement. This work presents a microstructure-based finite element model to predict the stress-strain response of Zircaloy containing hydrides. Quantitative microstructural details extracted from scanning electron microscopy (SEM) images were used to generate heterogeneous microstructures including the morphology and spatial distribution of hydrides. The constitutive material model for zircaloy in this study is based on crystal plasticity theory which considers the hexagonal close-packed (HCP) atomic structure of Zircaloy material. The hydrides were modeled as brittle material along with a damage model. Hydride formation inside the zircoloy matrix results in residual stress. This phenomenon is also captured in this model. A parametric study has been conducted to understand the effect of volume fraction, orientation, and lamellae thickness of the hydride phase on the mechanical properties of the overall material.

Kulkarni, Shank S.↗