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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Dust-obscured galaxies in the local universe

We use Wide-field Infrared Survey Explorer (WISE), AKARI, and Galaxy Evolution Explorer (GALEX) data to select local analogs of high-redshift (z ∼ 2) dust obscured galaxies (DOGs). We identify 47 local DOGs with S {sub 12μm}/S {sub 0.22μm} ≥ 892 and S {sub 12μm} > 20 mJy at 0.05 < z < 0.08 in the Sloan Digital Sky Survey data release 7. The infrared (IR) luminosities of these DOGs are in the range 3.4 × 10{sup 10} (L {sub ☉}) ≲ L {sub IR} ≲ 7.0 × 10{sup 11} (L {sub ☉}) with a median L {sub IR} of 2.1 × 10{sup 11} (L {sub ☉}). We compare the physical properties of local DOGs with a control sample of galaxies that have lower S {sub 12μm}/S {sub 0.22μm} but have similar redshift, IR luminosity, and stellar mass distributions. Both WISE 12 μm and GALEX near-ultraviolet (NUV) flux densities of DOGs differ from the control sample of galaxies, but the difference is much larger in the NUV. Among the 47 DOGs, 36% ± 7% have small axis ratios in the optical (i.e., b/a < 0.6), larger than the fraction among the control sample (17% ± 3%). There is no obvious sign of interaction for many local DOGs. No local DOGs have companions with comparable optical magnitudes closer than ∼50 kpc. The large- and small-scale environments of DOGs are similar to the control sample. Many physical properties of local DOGs are similar to those of high-z DOGs, even though the IR luminosities of local objects are an order of magnitude lower than for the high-z objects: the presence of two classes (active galactic nuclei- and star formation-dominated) of DOGs, abnormal faintness in the UV rather than extreme brightness in the mid-IR, and diverse optical morphology. These results suggest a common underlying physical origin of local and high-z DOGs. Both seem to represent the high-end tail of the dust obscuration distribution resulting from various physical mechanisms rather than a unique phase of galaxy evolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Porotomo: InSAR Data from San Emidio Geothermal Field, Nevada, 1992-2010

This submission contains tarred pair directories for interferometric synthetic aperture radar (InSAR) data covering San Emidio Geothermal Field in Nevada, USA as part of the porotomo project. Data included within this submission are the following: > ENVI_T120_GDR.tgz: Tarred directory containing 39 Envisat track 120 tarred, pair subdirectories spanning from 2003-Oct-29 to 2010-Jun-09. > ENVI_T27_GDR.tgz: Tarred directory containing 32 Envisat track 27 tarred, pair subdirectories spanning from 2004-Jun-23 to 2010-Apr-28. > ERS_T27_GDR.tgz: Tarred directory containing 35 ERS track 27 tarred, pair subdirectories spanning from 1992-Jun-07 to 2000-Sep-27. > ENVI_T27_SQR_drange_utm_cut.grd: SqueeSAR-derived data product for Enivsat track 27 from Eneva, et al. (2011) (provided by TRE Altamira) in terms of unwrapped range change rate. > MSTvsSQR_GDR.tgz: Tarred directory containing Easting gradient files for each satellite and track corresponding to the MST data products and the SqueeSAR data products. > sanem_dem_utm.grd: Digital elevation model (DEM) used in the creation of the interferograms. Explanation of pair subdirectories (contained within ENVI_T120_GDR, ENVI_T27_GDR, ERS_T27_GDR): Pairs are chosen based on a minimum spanning tree (MST) algorithm with image quality (as measured by the amount of phase noise within an interferometric pair) as the weighting criterion. Pairs are formed using the InSAR processing software GMT5SAR (Sandwell et al., 2011). Pair subdirectories are named by starting and ending epochs in YYYYMMDD_YYYYMMDD format. Each tarred pair directory contains GRD files for wrapped phase data (radians) and wrapped phase data after application of a modified Goldstein filter that depends on coherence (Goldstein & Werner, 1997; Baran et al., 2003; Sandwell et al., 2011). The data are given in both latitude/longitude and Universal Transverse Mercator (Zone 11N). Raw Synthetic Aperture Radar (SAR) data from the Envisat and ERS satellite missions operated by the European Space Agency (ESA) are copyrighted by ESA and were provided through the WInSAR consortium at the UNAVCO facility.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Implementation of a Novel Algorithm For Generating Synthetic CT Images From Magnetic Resonance Imaging Data Sets for Prostate Cancer Radiation Therapy

Purpose: To describe and evaluate a method for generating synthetic computed tomography (synCT) images from magnetic resonance simulation (MR-SIM) data for accurate digitally reconstructed radiograph (DRR) generation and dose calculations in prostate cancer radiation therapy. Methods and Materials: A retrospective evaluation was performed in 9 prostate cancer patients who had undergone MR-SIM in addition to CT simulation (CT-SIM). MR-SIM data were used to generate synCT images by using a novel, voxel-based weighted summation approach. A subset of patients was used for weight optimization, and the number of patients to use during optimization was determined. Hounsfield unit (HU) differences between CT-SIM and synCT images were analyzed via mean absolute error (MAE). Original, CT-based treatment plans were mapped onto synCTs. DRRs were generated, and agreement between CT and synCT-generated DRRs was evaluated via Dice similarity coefficient (DSC). Dose was recalculated, and dose-volume metrics and gamma analysis were used to evaluate resulting treatment plans. Results: Full field-of-view synCT MAE across all patients was 74.3 ± 10.9 HU with differences from CTs of 2.0 ± 8.1 HU and 11.9 ± 46.7 HU for soft tissue structures (prostate, bladder, and rectum) and femoral bones, respectively. Calculated DSCs for anterior-posterior and lateral DRRs were 0.90 ± 0.04 and 0.92 ± 0.05, respectively. Differences in D99%, mean dose, and maximum dose to the clinical target volume from CT-SIM dose calculations were 0.75% ± 0.35%, 0.63% ± 0.34%, and 0.54% ± 0.33%, respectively, for synCT-generated plans. Gamma analysis (2%/2 mm dose difference/distance to agreement) revealed pass rates of 99.9% ± 0.1% (range, 99.7%-100%). Conclusion: Generated synCTs enabled accurate DRR generation and dose computation for prostate MR-only simulation. Dose recalculated on synCTs agreed well with original planning distributions. Further validation using a larger patient cohort is warranted.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Quantitative Methods for Comparing Different Polyline Stream Network Models

Two techniques for exploring relative horizontal accuracy of complex linear spatial features are described and sample source code (pseudo code) is presented for this purpose. The first technique, relative sinuosity, is presented as a measure of the complexity or detail of a polyline network in comparison to a reference network. We term the second technique longitudinal root mean squared error (LRMSE) and present it as a means for quantitatively assessing the horizontal variance between two polyline data sets representing digitized (reference) and derived stream and river networks. Both relative sinuosity and LRMSE are shown to be suitable measures of horizontal stream network accuracy for assessing quality and variation in linear features. Both techniques have been used in two recent investigations involving extracting of hydrographic features from LiDAR elevation data. One confirmed that, with the greatly increased resolution of LiDAR data, smaller cell sizes yielded better stream network delineations, based on sinuosity and LRMSE, when using LiDAR-derived DEMs. The other demonstrated a new method of delineating stream channels directly from LiDAR point clouds, without the intermediate step of deriving a DEM, showing that the direct delineation from LiDAR point clouds yielded an excellent and much better match, as indicated by the LRMSE.

58 GEOSCIENCES↗
DOE OSTI2023

Fantastic Fits with fantasy of Active Galactic Nuclei Spectra: Exploring the Fe II Emission near the Hα Line

In this study, a refined approach for multicomponent fitting of active galactic nuclei (AGNs) spectra is presented utilizing the newly developed Python code Fully Automated pythoN Tool for AGN Spectra analYsis (fantasy). AGN spectra are modeled by simultaneously considering the underlying broken power-law continuum, predefined emission line lists, and an Fe II model, which is here extended to cover the wavelength range 3700–11000 Å. The Fe II model, founded solely on atomic data, effectively describes the extensive emission of the complex iron ion in the vicinity of the Hγ and Hβ lines, as well as near the Hα line, which was previously rarely studied. The proposed spectral fitting approach is tested on a sample of high-quality AGN spectra from the Sloan Digital Sky Survey Data Release 17. The results indicate that when Fe II emission is present near Hβ, it is also detected redward from Hα, potentially contaminating the broad Hα line wings and thus affecting the measurements of its flux and width. The production of Fe II emission is found to be strongly correlated with Eddington luminosity and appears to be controlled by a similar mechanism as the hydrogen Balmer lines. The study highlights the benefits of fitting type 1 AGN spectra with the fantasy code, pointing that it may be used as a robust tool for analyzing a large number of AGN spectra in the coming spectral surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

redMaGiC: Selecting luminous red galaxies from the DES Science Verification data

Here, we introduce redMaGiC, an automated algorithm for selecting luminous red galaxies (LRGs). The algorithm was specifically developed to minimize photometric redshift uncertainties in photometric large-scale structure studies. redMaGiC achieves this by self-training the colour cuts necessary to produce a luminosity-thresholded LRG sample of constant comoving density. We demonstrate that redMaGiC photo-zs are very nearly as accurate as the best machine learning-based methods, yet they require minimal spectroscopic training, do not suffer from extrapolation biases, and are very nearly Gaussian. We apply our algorithm to Dark Energy Survey (DES) Science Verification (SV) data to produce a redMaGiC catalogue sampling the redshift range z ϵ [0.2, 0.8]. Our fiducial sample has a comoving space density of 10 –3 (h –1 Mpc) –3 , and a median photo-z bias (zspec – zphoto) and scatter (σz/(1 + z)) of 0.005 and 0.017, respectively. The corresponding 5σ outlier fraction is 1.4 per cent. We also test our algorithm with Sloan Digital Sky Survey Data Release 8 and Stripe 82 data, and discuss how spectroscopic training can be used to control photo-z biases at the 0.1 per cent level.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

The Atacama Cosmology Telescope: The Two-season ACTPol Sunyaev–Zel’dovich Effect Selected Cluster Catalog

We introduce a catalog of 182 galaxy clusters detected through the Sunyaev–Zel'dovich (SZ) effect by the Atacama Cosmology Telescope in a contiguous 987.5 deg 2 field. The clusters were detected as SZ decrements by applying a matched filter to 148 GHz maps that combine the original ACT equatorial survey with data from the first two observing seasons using the ACTPol receiver. Optical/IR confirmation and redshift measurements come from a combination of large public surveys and our own follow-up observations. Where necessary, we calculated photometric redshifts for clusters using a pipeline that achieves accuracy Δz/(1 + z) = 0.015 when tested on Sloan Digital Sky Survey data. Under the assumption that clusters can be described by the so-called universal pressure profile (UPP) and its associated mass scaling law, the full signal-to-noise ratio > 4 sample spans the mass range $1.6\lt {M}_{500{\rm{c}}}^{\mathrm{UPP}}/{10}^{14}\,{M}_{\odot }\lt 9.1$, with median ${M}_{500{\rm{c}}}^{\mathrm{UPP}}=3.1\times {10}^{14}\,{M}_{\odot }$. The sample covers the redshift range 0.1 < z < 1.4 (median z = 0.49), and 28 clusters are new discoveries (median z = 0.80). We compare our catalog with other overlapping cluster samples selected using the SZ, optical, and X-ray wavelengths. We find that the ratio of the UPP-based SZ mass to richness-based weak-lensing mass is $\langle {M}_{500{\rm{c}}}^{\mathrm{UPP}}\rangle /\langle {M}_{500{\rm{c}}}^{\lambda \mathrm{WL}}\rangle =0.68\pm 0.11$. After applying this calibration, the mass distribution for clusters with M 500c > 4 × 10 14 M ⊙ is consistent with the number of such clusters found in the South Pole Telescope SZ survey.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Elucid—exploring the local universe with reconstructed initial density field. III. Constrained simulation in the sdss volume

A method we developed recently for the reconstruction of the initial density field in the nearby universe is applied to the Sloan Digital Sky Survey Data Release 7. A high-resolution N-body constrained simulation (CS) of the reconstructed initial conditions, with 3072{sup 3} particles evolved in a 500 h{sup −1} Mpc box, is carried out and analyzed in terms of the statistical properties of the final density field and its relation with the distribution of Sloan Digital Sky Survey galaxies. We find that the statistical properties of the cosmic web and the halo populations are accurately reproduced in the CS. The galaxy density field is strongly correlated with the CS density field, with a bias that depends on both galaxy luminosity and color. Our further investigations show that the CS provides robust quantities describing the environments within which the observed galaxies and galaxy systems reside. Cosmic variance is greatly reduced in the CS so that the statistical uncertainties can be controlled effectively, even for samples of small volumes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Dust reddened quasars in first and UKIDSS: Beyond the tip of the iceberg

We present the results of a pilot survey to find dust-reddened quasars by matching the Faint Images of the Radio Sky at Twenty-Centimeters (FIRST) radio catalog to the UKIDSS near-infrared survey and using optical data from Sloan Digital Sky Survey to select objects with very red colors. The deep K-band limit provided by UKIDSS allows for finding more heavily reddened quasars at higher redshifts as compared with previous work using FIRST and Two Micron All Sky Survey (2MASS). We selected 87 candidates with K ≤ 17.0 from the UKIDSS Large Area Survey (LAS) First Data Release (DR1), which covers 190 deg{sup 2}. These candidates reach up to ∼1.5 mag below the 2MASS limit and obey the color criteria developed to identify dust-reddened quasars. We have obtained 61 spectroscopic observations in the optical and/or near-infrared, as well as classifications in the literature, and have identified 14 reddened quasars with E(B – V) > 0.1, including 3 at z > 2. We study the infrared properties of the sample using photometry from the Wide-Field Infrared Survey Explorer and find that infrared colors improve the efficiency of red quasar selection, removing many contaminants in an infrared-to-optical color-selected sample alone. The highest-redshift quasars (z ≳ 2) are only moderately reddened, with E(B – V) ∼ 0.2-0.3. We find that the surface density of red quasars rises sharply with faintness, comprising up to 17% of blue quasars at the same apparent K-band flux limit. We estimate that to reach more heavily reddened quasars (i.e., E(B – V) ≳ 0.5) at z > 2 and a depth of K = 17, we would need to survey at least ∼2.5 times more area.

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 OSTI2018

Multivariate physiological recordings in an experimental hemorrhage model

In this paper we describe a data set of multivariate physiological measurements recorded from conscious sheep ( N = 8; 37.4 ± 1.1 kg) during hemorrhage. Hemorrhage was experimentally induced in each animal by withdrawing blood from a femoral artery at two different rates (fast: 1.25 mL/kg/min; and slow: 0.25 mL/kg/min). Data, including physiological waveforms and continuous/intermittent measurements, were transformed to digital file formats (European Data Format [EDF] for waveforms and Comma-Separated Values [CSV] for continuous and intermittent measurements) as a comprehensive data set and stored and publicly shared here (Appendix A). The data set comprises experimental information (e.g., hemorrhage rate, animal weight, event times), physiological waveforms (arterial and central venous blood pressure, electrocardiogram), time-series records of non-invasive physiological measurements (SpO 2 , tissue oximetry), intermittent arterial and venous blood gas analyses (e.g., hemoglobin, lactate, SaO 2 , SvO 2 ) and intermittent thermodilution cardiac output measurements. A detailed explanation of the hemodynamic and pulmonary changes during hemorrhage is available in a previous publication (Scully et al., 2016)

Yaghouby, Farid↗
DOE OSTI2024

Canopy reflectance spectroscopy, thermal images and digital photographs, San Lorenzo, Panama, 2020

This data package comprises reflectance spectra, thermal and visible images of top of canopy tree crowns at the San Lorenzo Protected Area, Panama. Data were collected using the Smithsonian Tropical Research Institute (STRI) canopy access crane, from positions approximately 4 m above the target tree canopy. Canopy reflectance spectra were collected using an HR-1024i full range spectroradiometer (350–2500 nm, SVC, Poughkeepsie, NY, USA) with a 14-degree lens. Canopy thermal images were captured using an 8640-S series USB Calibrated Thermal Camera (ICI International, Beaumont, TX, USA). Visible images were collected using an AW130 waterproof/shockproof camera (Nikon, Tokyo, Japan). Data were collected on three days, on 16 canopies from 10 tree species. On both January 30, 2020, and February 27, 2020, we collected spectra at three time points, in the morning, midday, and afternoon. On February 18, 2020, we collected data around midday only. Due to technical issues, digital photography was collected on 30 January only. This dataset includes unprocessed data only, including spectral data (.sig or .sed), thermal images (.i16), and photographs (.raw), a detailed method description (.pdf), species information (.csv) and ESS-DIVE file-level metadata (FLMD.csv).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

An Autonomous Critical Data Extrapolator for the AGN-201m

Nuclear nonproliferation serves as a key goal, being undertaken by the International Atomic Energy Agency (IAEA). To recognize proliferation there are two pathways that states, who intend to use nuclear material for malicious purposes can take, diversion can misuse. Diversion is when fissile nuclear material is declared to the IAEA for non-weapon purposes, but then covertly removed. If the source of nuclear material, that is not declared and not fissionable, is placed inside the reactor core to create fissile material used to create weapons then the state is using the second pathway of proliferation, misuse. With the emerging development in areas of simulation and machine learning the creation of virtual models of reactor systems, digital twins, serve as a potential method to identify proliferation through detecting anomalous behavior in the reactor. A digital twin for a physical nuclear reactor has never been developed, as digital twins serve as an emerging technology. To investigate the process for development and use of a digital twin for a nuclear reactor Idaho State University’s AGN-201m serves as the nuclear reactor used for development of this digital twin. A data acquisition system has been installed to the reactor system allowing for the transfer of collected data from a reactor operation to Idaho National Laboratory’s Deeplynx data warehouse. When utilizing data to train reactor physics and machine learning models, a significant challenge encountered is the initial state of the data. Nuclear proliferation will have the capacity to be detected when the reactor immediately starts up, nor will it occur after the reactor shuts down. Generally, it will be detected when the reactor is operating at some desired power over a sufficient period for that specific reactor design. For the AGN-201m this will be when the reactor is critical (generally 1 mW or above) for a timespan that is within or less than the range of a regular business day. Datasets sent to Deeplynx have had to be manually cut to when the reactor is critical based on plots of power levels. This method is inefficient and laborious, especially when using multiple datasets at once to train a model. To provide a more streamlined approach an automated critical data extrapolator is developed, with capabilities of recognizing when the reactor operation first reaches criticality, and when the reactor undergoes a SCRAM and is shutdown.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

A machine-learning-aided data recovery approach for predicting multi-material thermal behaviors in advanced test reactor capsules

Instrumented experiments conducted at test reactors are essential to the deployment of new advanced reactor systems. Designing new experiments and generating data on specific reactor conditions require significant investments in terms of both time and cost. Finite element analysis software can be used to create high-fidelity models of experiment environments in order to support the actual experiments, but computation time remains a concern in terms of applying outcomes to real-time usage of data (e.g., a digital twin [DT]). Here, the present research proposes a machine-learning (ML) aided approach to making temperature and displacement predictions based on the thickness of the outer gas gap on the experimental capsule used for in-pile demonstration of a novel new thermal conductivity probe in the Advanced Test Reactor (ATR). This capsule consisted of U10Zr fuel, a rodlet, sodium, and inner and outer capsules. Gas gaps existed between the fuel and the rodlet, and between the inner and the outer capsule. The learning data pertained to an experimental capsule's radial distributions of temperature and displacement, as obtained based on Abaqus and the physical features. For the first step of ML sequence, the temperature was predicted using three positional parameters. Next, the displacement was predicted using seven additional parameters. Each physical feature was normalized in order to be both nondimensional and standardized. The temperature and displacement predictions showed good agreement with the simulation results in all cases involving interpolation and extrapolation. Furthermore, data similarity enhancement increased the similarity between the training and the target data, thereby increasing the predictive accuracy of the ML models. In certain extrapolation cases involving limited original ML model accuracy, data similarity enhancement and data recovery was able to somewhat improve this accuracy.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Stilbene Scintillator Detector Array and Data-Acquisition System for Active Interrogation

A large area Stilbene crystal detector array has been assembled and calibrated for use in active interrogation measurements. The array consists of 10 high-efficiency 4”x2” stilbene cells. The cells were grown by Inrad Optics using LLNL developed technology. The array can be used to detect multiple signatures of SNM items, both actively and passively. Prompt and delayed gamma rays as well as prompt and delay neutrons can be detected and counted separately. The array is coupled to the Mesytec MDPP-16 VME-based digitizer module for data acquisition. This module performs on-board pulse-shape discrimination of signals from the Stilbene crystal detectors. This data-acquisition module allows separation of neutron and gamma-ray events with a low demand on data transfer rates. In addition, the high-resolution timing (<100 ps) is suitable for correlated gamma-ray and neutron multiplicity counting. This report describes the construction and calibration of the system. We also discuss applications of the system as well as how the system is currently being utilized by end users.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗