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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

On Quality Control Procedures Being Adopted for TRMM LBA and KWAJEX Soundings Data Sets

During NASA's Tropical Rainfall Measuring Mission (TRMM) field campaigns Large Scale Biosphere Atmosphere (LBA) held in Amazonia (Brazil) in the period January- February, 1999, and the Kwajalein Experiment (KWAJEX) held in the Republic of Marshall Islands in the period between August-September, 1999, extensive radiosonde observations (raob) were collected using VIZ and Vaisala sondes which have different response characteristics. In all, 320 raob for LBA and 972 fixed raob for KWAJEX have been obtained and are being processed. Most atmospheric sensible heat source (Q1) and apparent moisture sink (Q2) budget studies are based on sounding data, and the accuracy of the raob is important especially in regions of deep moist convection. A data quality control (QC) project has been initiated at GSFC by the principal investigator (JBH), and this paper addresses some of the quantitative findings for the level I and II QC procedures. Based on these quantitative assessment of sensor (or system) biases associated with each type of sonde, the initial data repair work will be started. Evidence of moisture biases between the two different sondes (VIZ and Vaisala) has been shown earlier by Halverson et al. (2000). Vaisala humidity sensors are found to have a low-level dry bias in the boundary layer, whereas above 600 mb the VIZ sensor tends to register a dryer atmosphere. All raob data were subjected to a limit check based on an algorithm already well tested for the raob data obtained during the Tropical Ocean Global Atmosphere (TOGA-COARE).

Roy, B.↗
DOE OSTI2021

Deep Learning Predicts Stress–Strain Relations of Granular Materials Based on Triaxial Testing Data

This study presents an AI-based constitutive modelling framework wherein the prediction model directly learns from triaxial testing data by combining discrete element modelling (DEM) and deep learning. A constitutive learning strategy is proposed based on the generally accepted frame-indifference assumption in constructing material constitutive models. The low-dimensional principal stress-strain sequence pairs, measured from discrete element modelling of triaxial testing, are used to train recurrent neural networks, and then the predicted principal stress sequence is augmented to other high-dimensional or general stress tensor via coordinate transformation. Through detailed hyperparameter investigations, it is found that long short-term memory (LSTM) and gated recurrent unit (GRU) networks have similar prediction performance in constitutive modelling problems, and both satisfactorily predict the stress responses of granular materials subjected to a given unseen strain path. Furthermore, the unique merits and ongoing challenges of data-driven constitutive models for granular materials are discussed.

42 ENGINEERING↗
DOE OSTI2019

Big Data Analytics on Fiber-Optical Distributed Acoustic Sensing with Rayleigh Enhancements

his paper presents a technique to use deep neural network on data analysis of distributed acoustic sensing (DAS) system built by phase-sensitive optical time-domain reflectometry (ϕ-OTDR) with Rayleigh enhancement. Rayleigh backscattering is enhanced by ultrafast laser direct writing technique that promise the excellent sensitivity. And the use of artificial intelligence (AI) drastically improve the efficiency of data pattern recognition.

Peng, Zhaoqiang↗
NASA NTRS1972

Tracking and data system support for the Mariner Mars 1971 mission. Volume 2: First trajectory correction maneuver through orbit insertion

The Deep Space Tracking and Data System activities in support of the Mariner Mars 1971 project from the first trajectory correction maneuver on 4 June 1971 through cruise and orbit insertion on 14 November 1971 are presented. Changes and updates to the TDS requirements and to the plan and configuration plus detailed information on the TDS flight support performance evaluation and the preorbital testing and training are included. With the loss of Mariner 8 at launch, a few changes to the Mariner Mars 1971 requirements, plan, and configuration were necessitated. Mariner 9 is now assuming the former mission plan of Mariner 8, including the TV mapping cycles and a 12-hr orbital period. A second trajectory correction maneuver was not required because of the accuracy of the first maneuver. All testing and training for orbital operations were completed satisfactorily and on schedule. The orbit insertion was accomplished with excellent results.

Textor, G. P.↗
DOE OSTI2017

Active Pacific meridional overturning circulation (PMOC) during the warm Pliocene

An essential element of modern ocean circulation and climate is the Atlantic meridional overturning circulation (AMOC), which includes deep-water formation in the subarctic North Atlantic. However, a comparable overturning circulation is absent in the Pacific, theworld’s largest ocean,where relatively fresh surface waters inhibitNorth Pacific deep convection. We present complementary measurement and modeling evidence that the warm, ~400–ppmv (parts per million by volume) CO 2 world of the Pliocene supported subarctic North Pacific deep-water formation and a Pacific meridional overturning circulation (PMOC) cell. In Pliocene subarctic North Pacific sediments, we report orbitally paced maxima in calcium carbonate accumulation rate, with accompanying pigment and total organic carbon measurements supporting deep-ocean ventilation-driven preservation as their cause. Together with high accumulation rates of biogenic opal, these findings require vigorous bidirectional communication between surface waters and interior waters down to ~3 km in the western subarctic North Pacific, implying deep convection. Redoxsensitive trace metal data provide further evidence of higher Pliocene deep-ocean ventilation before the 2.73-Ma (million years) transition. This observational analysis is supported by climate modeling results, demonstrating that atmospheric moisture transport changes, in response to the reduced meridional sea surface temperature gradients of the Pliocene, were capable of eroding the halocline, leading to deep-water formation in the western subarctic Pacific and a strong PMOC. This second Northern Hemisphere overturning cell has important implications for heat transport, the ocean/atmosphere cycle of carbon, and potentially the equilibrium response of the Pacific to global warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Secure Data Logging and Processing with Blockchain and Machine Learning (Final Report)

Secure Data Logging and Processing with Blockchain and Machine Learning (ML) research is focused on the development of a platform to securely log and process sensor data in fossil power plants. The platform integrates two emerging technologies, blockchain and ML, and incorporates several innovative mechanisms to ensure the integrity, reliability, and resiliency of power systems. The goal is to protect the power plant from various cyberattacks such as false data injection and denial of service attacks using these technologies. The research goal was enabled by the following Research Project Objectives: 1) Secure authentication and identity verification of sensor nodes, actuators, and other equipment within a network. 2) Development of mechanisms that ensure only data sent by legitimate sensors are accepted and stored in the data repository. 3) Development of data aggregation methodologies using ML / Deep Learning (DL) algorithms to minimize noise / faulty data. 4) Implementation of the blockchain technologies to provide data security using secured IOTA framework & nodes.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Deep Learning Denoising Applied to Regional Distance Seismic Data in Utah

Seismic waveform data are generally contaminated by noise from various sources. Suppressing this noise effectively so that the remaining signal of interest can be successfully exploited remains a fundamental problem for the seismological community. To date, the most common noise suppression methods have been based on frequency filtering. These methods, however, are less effective when the signal of interest and noise share similar frequency bands. Inspired by source separation studies in the field of music information retrieval (Jansson et al., 2017) and a recent study in seismology (Zhu et al., 2019), we implemented a seismic denoising method that uses a trained deep convolutional neural network (CNN) model to decompose an input waveform into a signal of interest and noise. In our approach, the CNN provides a signal mask and a noise mask for an input signal. The short-time Fourier transform (STFT) of the estimated signal is obtained by multiplying the signal mask with the STFT of the input signal. To build and test the denoiser, we used carefully compiled signal and noise datasets of seismograms recorded by the University of Utah Seismograph Stations network. Results of test runs involving more than 9000 constructed waveforms suggest that on average the denoiser improves the signal-to-noise ratios (SNRs) by ~5 db, and that most of the recovered signal waveforms have high similarity with respect to the target waveforms (average correlation coefficient of ~0.80) and suffer little distortion. Application to real data suggests that our denoiser achieves on average a factor of up to ~2-5 improvement in SNR over band-pass filtering and can suppress many types of noise that band-pass filtering cannot. For individual waveforms, the improvement can be as high as ~15 db.

58 GEOSCIENCES↗
NASA NTRS1994

Stellar populations in edge-on galaxies from deep CCD surface photometry, 1: NGC 5907

We present extremely deep charge coupled device (CCD) surface photometry of the edge-on Sc galaxy NGC 5907. Our data reach reliably to a surface brightness of R=27 mag/sq arcsec, some two magnitudes fainter than any previous work. We obtained this improvement using a 2048X2048 CCD with a wide (approximately 24 min) field, which made it possible to sky subtract directly from the galaxy frame, and by taking many dark sky flatfields. Our analysis of these data, using a full 2D model fitting procedure with a detailed error model, confirm the thin disk parameters of van der Kruit & Searle (1981). In particular, we confirm that the galaxy's disk has a radial cutoff and a constant scale height with radius. We find evidence for a stellar warp in this system, which has the same orientation as the H I warp. Our deep data also confirm that NGC 5907 has no thick disk. This suggests that theories of thick disk formation from star formation in the early stages of disk collapse, or by secular heating mechanisms, are unlikely to be correct, because they would predict that every galaxy would have a thick disk. Thick disk formation from the accretion of satellite galaxies is more likely.

Morrison, Heather L.↗
DOE OSTI2022

Optimization of the deep neural network parameters for generating homogenized fuel assembly data for nodal codes

Homogenized fuel assembly (FA) data is a typical input data for nodal codes. Generating that data, however, could be time-consuming. One of promising ways to mitigate the computational burden of generating macroscopic cross-sections is to use trained artificial neural network (ANN) models for predicting nuclear data. However, there is a challenge to make the model support variable FA geometry. In this work, two most common types of FA were combined in one ANN model. Since there could be multiple ways of converting 2-dimensional FA data into 1-dimensional input vector for ANN, three different approaches of data flattening were evaluated. The input parameters included each fuel pin enrichment, fuel temperature, moderator temperature and boron concentration. The output parameters were 2-group macroscopic cross-sections (XS) and pin power distribution (HFF). A fully connected deep neural network (DNN) model was trained and tested using pre-generated data obtained with lattice physics code STREAM. The results of this study showed no statistically significant difference in the accuracy of XS and HFF generation for all 3 tested input vector orders. This means that fully connected DNN for XS generation demonstrated input sequence invariance. Results of comparing predicted XS data with reference solutions were found sufficiently close considering the reduction of computation time offered by ANN. Mean relative difference (MRD) for all output XS parameters was found below 0.7%, while HFF MRD was found higher compared to XS values, in some cases slightly exceeding 1%, mostly near guide tube locations. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1994

Deep imaging of the field of the z = 4.9 quasar PC 1247+3406, and faint galaxy counts in the K band with the Keck telescope

We present deep images in the K(sub s) band of the field of the quasar PC 1247+3406 at z = 4.897, obtained using the near-infrared camera on the W. M. Keck telescope. A number of faint sources have been detected, some of which appear to be quite red. Their nature and redshifts remain uncertain at this time. These data are combined with deep Keck infrared images of five additional fields and present galaxy counts reaching down to K(sub s) = 22 mag, comparable to the deepest K-band surveys to date. The data presented here are in good agreement with the Hawaii Deep Survey and represent the first independent verification of those results. The slope of the log N-log S relation derived from these data agrees well with the Hawaii Deep Survey, while the counts are slightly higher, especially at the faintest levels probed here. This may be due to a presence of groups or clusters around the target objects at high redshifts.

Soifer, B. T.↗
DOE OSTI2022

Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: Comparison with linear subspace techniques

Natural convection in porous media is a highly nonlinear multiphysical problem relevant to many engineering applications (e.g., the process of CO 2 sequestration). Here, we extend and present a non-intrusive reduced order model of natural convection in porous media employing deep convolutional autoencoders for the compression and reconstruction and either radial basis function (RBF) interpolation or artificial neural networks (ANNs) for mapping parameters of partial differential equations (PDEs) on the corresponding nonlinear manifolds. To benchmark our approach, we also describe linear compression and reconstruction processes relying on proper orthogonal decomposition (POD) and ANNs. Further, we present comprehensive comparisons among different models through three benchmark problems. The reduced order models, linear and nonlinear approaches, are much faster than the finite element model, obtaining a maximum speed-up of 7 × 10 6 because our framework is not bound by the Courant–Friedrichs–Lewy condition; hence, it could deliver quantities of interest at any given time contrary to the finite element model. Our model’s accuracy still lies within a relative error of 7% in the worst-case scenario. We illustrate that, in specific settings, the nonlinear approach outperforms its linear counterpart and vice versa. We hypothesize that a visual comparison between principal component analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) could indicate which method will perform better prior to employing any specific compression strategy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Deep residual networks for crystallography trained on synthetic data

The use of artificial intelligence to process diffraction images is challenged by the need to assemble large and precisely designed training data sets. To address this, a codebase called Resonet was developed for synthesizing diffraction data and training residual neural networks on these data. Here, two per-pattern capabilities of Resonet are demonstrated: (i) interpretation of crystal resolution and (ii) identification of overlapping lattices. Resonet was tested across a compilation of diffraction images from synchrotron experiments and X-ray free-electron laser experiments. Crucially, these models readily execute on graphics processing units and can thus significantly outperform conventional algorithms. While Resonet is currently utilized to provide real-time feedback for macromolecular crystallography users at the Stanford Synchrotron Radiation Lightsource, its simple Python-based interface makes it easy to embed in other processing frameworks. This work highlights the utility of physics-based simulation for training deep neural networks and lays the groundwork for the development of additional models to enhance diffraction collection and analysis.

36 MATERIALS SCIENCE↗
NASA NTRS1999

Orbit Determination for Mars Global Surveyor During Mapping

The Mars Global Surveyor (MGS) spacecraft reached a low-altitude circular orbit on February 4, 1999, after the termination of the second phase of aerobraking. The MGS spacecraft carries the Mars Orbiter Laser Altimeter (MOLA) whose primary goal is to derive a global, geodetically referenced 0.2 deg x 0.2 deg topographic grid of Mars with a vertical accuracy of better than 30 meters. During the interim science orbits in the' Hiatus mission phase (October - November 1997), and the Science Phasing Orbits (March - April, 1998, and June - July 1998) 208 passes of altimeter data were collected by the MOLA instrument. On March 1, 1999 the first ten orbits of MOLA altimeter data from the near-circular orbit were successfully returned from MGS by the Deep Space Network (DSN). Data will be collected from MOLA throughout the Mapping phase of the MCS mission, or for at least one Mars year (687 days). Whereas the interim orbits of Hiatus and SPO were highly eccentric, and altimeter data were only collected near periapsis when the spacecraft was below 785 km, the Mapping orbit of MGS is near circular, and altimeter data will be collected continuously at a rate of 10 Hz. The proper analysis of the altimeter data requires that the orbit of the MGS spacecraft be known to an accuracy comparable to that of the quality of the altimeter data. The altimeter has an ultimate precision of 30 cm on mostly flat surfaces, so ideally the orbits of the MGS spacecraft should be known to this level. This is a stringent requirement, and more realistic goals of orbit error for MGS are ten to thirty meters. In this paper we will discuss the force and measurement modelling required to achieve this objective. Issues in force modelling include the proper modelling of the gravity field of Mars, and the modelling of non-conservatives forces, including the development of a 'macro-model', in a similar fashion to TOPEX/POSEIDON and TDRSS. During Cruise and Aerobraking, the high gain antenna (HGA) was stowed on the +X face of the spacecraft. On March 29, 1999 the HGA will be deployed on a meter long boom which will remain Earth-pointing while the instrument panel (including the MOLA instrument) remains pointed at nadir. The tracking data must be corrected for the regular motion of the high gain antenna with respect to the center of mass, and the success of the MGS determination during Mapping will depend on correctly accounting for this offset in the measurement model.

Lemoine, F. G.↗
NASA NTRS2017

Probing Large-Scale Coherence Between Spitzer IR and Chandra X-Ray Source-Subtracted Cosmic Backgrounds

We present new measurements of the large-scale clustering component of the cross-power spectra of the source-subtracted Spitzer-IRAC cosmic infrared background and Chandra-ACIS cosmic X-ray background surface brightness fluctuations. Our investigation uses data from the Chandra Deep Field South, Hubble Deep Field North, Extended Groth Strip/AEGIS (All-wavelength Extended Groth Strip International Survey) field, and UDS (Ultra-Deep Survey) / SXDF surveys, comprising 1160 Spitzer hours and approximately 12 megaseconds of Chandra data collected over a total area of 0.3 degrees squared. We report the first (greater than 5 sigma) detection of a cross-power signal on large angular scales greater than 20 arc seconds between [0.5-2] kiloelectronvolts and the 3.6 and 4.5 micron bands, at approximately 5 omega and 6.3 sigma significance, respectively. The correlation with harder X-ray bands is marginally significant. Comparing the new observations with existing models for the contribution of the known unmasked source population at redshift less than 7, we find an excess of about an order of magnitude at 5 sigma confidence. We discuss possible interpretations for the origin of this excess in terms of the contribution from accreting early black holes (BHs), including both direct collapse BHs and primordial BHs, as well as from scattering in the interstellar medium and intra-halo light.

Capelluti, N.↗
DOE OSTI2022

Deep learning-based spatio-temporal estimate of greenhouse gas emissions using satellite data

Accurate estimation of greenhouse gases (GHGs) emissions is very important for developing mitigation strategies to climate change by controlling and reducing GHG emissions. This project aims to develop multiple deep learning approaches to estimate anthropogenic greenhouse gas emissions using multiple types of satellite data. NO2 concentration is chosen as an example of GHGs to evaluate the proposed approach. Two sentinel satellites (sentinel-2 and sentinel-5P) provide multiscale observations of GHGs from 10-60m resolution (sentinel-2) to ~kilometer scale resolution (sentinel-5P). Among multiple deep learning (DL) architectures evaluated, two best DL models demonstrate that key features of spatio-temporal satellite data and additional information (e.g., observation times and/or coordinates of ground stations) can be extracted using convolutional neural networks and feed forward neural networks, respectively. In particular, irregular time series data from different NO 2 observation stations limit the flexibility of long short-term memory architecture, requiring zero-padding to fill in missing data. However, deep neural operator (DNO) architecture can stack time-series data as input, providing the flexibility of input structure without zero-padding. As a result, the DNO outperformed other deep learning architectures to account for time-varying features. Overall, temporal patterns with smooth seasonal variations were predicted very well, while frequent fluctuation patterns were not predicted well. In addition, uncertainty quantification using conformal inference method is performed to account for prediction ranges. Overall, this research will lead to a new groundwork for estimating greenhouse gas concentrations using multiple satellite data to enhance our capability of tracking the cause of climate change and developing mitigation strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Hydraulic Modeling of Induced and Propagated Fractures: Analysis of Flow and Pressure Data From Hydromechanical Experiments in the COSC-1 Deep Borehole in Crystalline Rock Near Åre, Sweden

To characterize the coupled hydromechanical behavior of rock fractures, the step-rate injection method for fracture in-situ properties (SIMFIP) was conducted with a specialized downhole probe developed by Guglielmi et.al. In June 2019, a field campaign was carried out near Åre, Sweden, where the SIMFIP probe was applied in the Collisional Orogeny in the Scandinavian Caledonides-1 scientific borehole to understand the dynamics of injection-induced fracture initiation, fracture opening, and shearing due to water injection-withdrawal in a borehole interval isolated by two packers. Three intervals were investigated at ~500 m depth: (a) an unfractured section (intact rock), (b) a section with non-conductive fractures, and (c) a section with hydraulically conductive fractures. Pressure, injection flow rate, and borehole wall displacement were simultaneously measured during the tests. In the present study, the geometry of the induced fracture and deformation of existing fractures at different time stages of the tests are determined based on a hydrologic model by using the measured pressure and flow data during each time stage of the experiment. A numerical model for the fluid flow within the fracture and the packed-off borehole interval is implemented within COMSOL Multiphysics. By matching model simulations with observed data for all three sections, estimates of the induced and propagated fractures' radius and aperture at successive time stages have been obtained in each case. We could also determine the non-linear relationship between fracture aperture and pressure for values above fracture opening pressures. The model results provide insights for the understanding of pressure-induced fracture initiation and propagation in crystalline rock.

58 GEOSCIENCES↗
DOE OSTI2023

A giant umbrella-like stellar stream around the tidal ring galaxy NGC 922

Tidal ring galaxies (TRGs) are rarely observed in the local universe due to their intrinsically transient nature. The tidal ring structures are the result of strong interactions between gas-rich stellar disks and smaller galactic systems, and do not last longer than ~500 Myr. Therefore, these are perfect scenarios in which to look for the debris of recently accreted dwarf galactic systems. Our goal is to study the low surface brightness stellar structures around the TRG NGC 922 and to revise the hypothesis of its formation in light of these new data. We present new deep images of the TRG NGC 922 and its surroundings from the DESI Legacy survey data and from our observations with an amateur telescope. These observations are compared with results from high-resolution N-body simulations that were designed to reproduce an alternative formation scenario for this peculiar galaxy. Our new observations unveil that the low surface brightness stellar tidal structures around NGC 922 are much more complex than reported in previous works. In particular, the formerly detected tidal spike-like structure at the north-east of the central galaxy disk is not connected with the dwarf companion galaxy PGC 3080368, which has been suggested as the intruder triggering the ring formation of NGC 922. The deep images reveal that this tidal structure mainly has a fainter giant umbrella-like shape, and thus it was formed from the tidal disruption of a different satellite. Using the broad-band g, r, and z DESI LS images, we measured the photometric properties of this stellar stream, estimating a total absolute magnitude in the r band of M r = -17.0 ± 0.03 mag and a total stellar mass for the stream of between 6.9 and 8.5×10 8 M ⊙ . We performed a set of N-body simulations to reproduce the observed NGC922-intruder interaction, suggesting a new scenario for the formation of its tidal ring from the infall of a gas-rich satellite around 150 Myr ago. Finally, our deep images also reveal a tidal shell around the dwarf galaxy PGC 3080368, a possible fossil of a recent merger with a smaller satellite, which may suggest it is in its first infall towards NGC 922.

79 ASTRONOMY AND ASTROPHYSICS↗