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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Deep Modeling: Circuit Characterization Using Theory Based Models in a Data Driven Framework

Analog computational circuits have been demonstrated to provide substantial improvements in power and speed relative to digital circuits, especially for applications requiring extreme parallelism but only modest precision. Deep machine learning is one such area and stands to benefit greatly from analog and mixed-signal implementations. However, even at modest precisions, offsets and non-linearity can degrade system performance. Furthermore, in all but the simplest systems, it is impossible to directly measure the intermediate outputs of all sub-circuits. The result is that circuit designers are unable to accurately evaluate the non-idealities of computational circuits in-situ and are therefore unable to fully utilize measurement results to improve future designs. In this paper we present a technique to use deep learning frameworks to model physical systems. Recently developed libraries like TensorFlow make it possible to use back propagation to learn parameters in the context of modeling circuit behavior. Offsets and scaling errors can be discovered even for sub-circuits that are deeply embedded in a computational system and not directly observable. The learned parameters can be used to refine simulation methods or to identify appropriate compensation strategies. We demonstrate the framework using a mixed-signal convolution operator as an example circuit.

Bolme, David S↗
NASA NTRS1983

Orbit determination of highly elliptical Earth orbiters using VLBI and delta VLBI measurements

The feasibility of using very long baseline interferometric (VLBI) data acquired by the deep space network to navigate highly elliptical Earth orbiting satellites was shown. The navigation accuracy improvements achievable with VLBI and delta VLBI data types are determined for comparison with the Doppler capability. The sensitivity of the VLBI navigation accuracy to the baseline orientation relative to the orbit plane and the effects of major error sources such as gravitational harmonics and atmospheric are examined. It is found that VLBI measurements perform as well as strategies using conventional Doppler, while substantially reducing the required antenna support.

Frauenholz, R. B.↗
NASA NTRS2020

Machine Learning in Heliophysics and space weather forecasting: a white paper of finding and recommendations

The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology,Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers,expert modelers, and computer/data scientists. Their objective was to discuss critical developments and prospects of the application of machine and/or deep learning techniques for data analysis, modeling and forecasting in Heliophysics, and to shape a strategy for further developments in the field. The workshop combined a set of plenary sessions featuring invited introductory talks interleaved with a set of open discussion sessions. The outcome of the discussion is encapsulated in this white paper that also features a top-level list of recommendations agreed by participants

HSR↗
NASA NTRS2016

A Multi-Center Space Data System Prototype Based on CCSDS Standards

Deep space missions beyond earth orbit will require new methods of data communications in order to compensate for increasing RF propagation delay. The Consultative Committee for Space Data Systems (CCSDS) standard protocols Spacecraft Monitor & Control (SM&C), Asynchronous Message Service (AMS), and Delay/Disruption Tolerant Networking (DTN) provide such a method. The maturity level of this protocol set is, however, insufficient for mission inclusion at this time. This prototype is intended to provide experience which will raise the Technical Readiness Level (TRL) of these protocols..

Rich, Thomas M.↗
NASA NTRS1986

Convective scale interaction: Arc cloud lines and the development and evolution of deep convection

Information is used from satellite data and research aircraft data to provide new insights concerning the mesoscale development and evolution of deep convection in an atmosphere typified by weak synoptic-scale forcing. The importance of convective scale interaction in the development and evolution of deep convection is examined. This interaction is shown to manifest itself as the merger and intersection of thunderstorm outflow boundaries (arc cloud lines) with other convective lines, areas or boundaries. Using geostationary satellite visible and infrared data convective scale interaction is shown to be responsible for over 85 percent of the intense convection over the southeast U.S. by late afternoon, and a majority of that area's afternoon rainfall. The aircraft observations provided valuable information concerning critically important regions of the arc cloud line: (1) the cool outflow region, (2) the density surge line interface region; and (3) the sub-cloud region above the surge line. The observations when analyzed with rapid scan satellite data, helped in defining the arc cloud line's life cycle as 3 evolving stages.

Purdom, James Francis Whitehurst↗
NASA NTRS2003

Cost and Performance Comparison of an Earth-Orbiting Optical Communication Relay Transceiver and a Ground-Based Optical Receiver Subnet

Optical communications can provide high-data-rate telemetry from deep-space probes with subsystems that have lower mass, consume less power, and are smaller than their radio frequency (RF) counterparts. However, because optical communication is more a.ected by weather than is RF communication, it requires groundstation site diversity to mitigate the adverse e.ects of inclement weather on the link. An optical relay satellite is not a.ected by weather and can provide 24-hour coverage of deep-space probes. Using such a relay satellite for the deep-space link and an 8.4-GHz (X-band) link to a ground station would support high-data-rate links from small deep-space probes with very little link loss due to inclement weather. We have reviewed past JPL-funded work on RF and optical relay satellites, and on proposed clustered and linearly dispersed optical subnets. Cost comparisons show that the life cycle costs of a 7-m optical relay station based on the heritage of the Next Generation Space Telescope is comparable to that of an 8-station subnet of 10- m optical ground stations. This makes the relay link an attractive option vis- a-vis a ground-station network.

Wilson, K. E.↗
DOE OSTI2020

A Non-Invasive Approach for Elucidating the Spatial Distribution of In Situ Stress in Deep Subsurface Geologic Formations Considered for CO 2 Storage (Task 2 Report Extracting Stress Data from Seismic Data)

This report describes research accomplishments achieved with funding provided through Department of Energy (DOE) Contract DE-FE0031686. The purpose of this DOE funding is to develop methods that can provide key information about stress fields that act on deep rocks without invading the earth to acquire that information. The research objective was to demonstrate methods that extract the azimuth directions of SHmax and SHmin stress in deep rocks from traditional seismic reflection data like the data that are used to explore for deep oil and gas reservoirs. Battelle performed two seismic investigations and achieved estimates of SHmax and SHmin azimuths in both efforts that agreed with local, non-seismic, ground-truth measurements of SHmax orientation. The first procedure utilized vertical seismic profiling (VSP) data; the second effort utilized three-dimensional (3D) seismic data.

58 GEOSCIENCES↗
DOE OSTI2021

Towards data-driven constitutive modelling for granular materials via micromechanics-informed deep learning

The analytical description of path-dependent elastic-plastic responses of a granular system is highly complicated because of continuously evolving microstructures and strain localisation within the system undergoing deformation. This study offers an alternative to the current analytical paradigm by developing micromechanics-informed machine-learning based constitutive modelling approaches for granular materials. A set of critical variables associated with the constitutive behaviour of granular materials are identified through an incremental stress-strain relationship analysis. Depending on the strategy to exploit the priori micromechanical knowledge, three different training strategies are explored. The first model uses only the measurable external variables to make stress predictions; the second model utilises a directed graph to link all the external strain sequences and internal microstructural evolution variables into a single prediction model comprised of a series of sub-mappings, and the third model explicitly integrates the physically important non-temporal properties with external strain paths into training through an enhanced Gated Recurrent Unit (GRU). These three models show satisfactory agreement with unseen test specimens based on multi-directional loading cases. The basic features and potential applications of each model are explained. Lastly, the key factors for constitutive training and limitations of the current work are also discussed in detail.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A data-driven global soil heterotrophic respiration dataset and the drivers of its inter-annual variability

Soil heterotrophic respiration (SHR), one of the primary carbon fluxes from terrestrial ecosystems to the atmosphere, is important for carbon-climate feedbacks because of its sensitivity to available litter and soil carbon, climatic conditions, and nutrient availability. However, until recently limited SHR data were available, and most published global SHR estimates have either a short time span, coarse spatial resolution, or reply on overly-simple model formulations. To better understand and quantify the global distribution of SHR and its sensitivity to climate variability, we produced a new global SHR dataset using Random Forest algorithms, up-scaling 455 point data from the Global Soil Respiration Database (SRDB 4.0) with gridded fields of climatic, edaphic and productivity as explanatory variables. We estimated a global total SHR of 46.8 Pg C yr-1 over 1985-2013 (95% confidence interval: 38.6-56.3 Pg C yr-1), with a significant increasing trend of 0.03 Pg C yr-2 during this period. We found that the choice of soil moisture datasets contributes more to the difference among these data-driven SHR members rather than that of productivity, temperature and precipitation data sources. We also analyzed the influence of climatic variables on the inter-annual variability (IAV) of our SHR product. Water availability was the dominant driver of IAV at global scales, although the inferred sensitivity depends on the choice of the soil moisture gridded dataset. At the ecosystem scale, temperature strongly controls the IAV of SHR in tropical forests, while water availability dominates in extra-tropical forest and semi-arid regions. Our machine-learning gridded SHR dataset and outputs from process-based land surface models (TRENDYv6) show agreement for a strong association between water variability and SHR IAV at the global scale, but the two approaches lead to different temporal trend globally and different controlling variables for IAV at the ecosystem scale. Our study provides evidence for the pervasive and important role of water availability in driving SHR, indicating both a direct effect limiting decomposition rates and an indirect effect through the amount of fresh organic matter made available to SHR from productivity. In consideration of potential limitations and uncertainties remaining in our data-driven SHR datasets, we call for a more scientifically designed observation network for SHR, more observation data compilation, and increased use of deep learning methods making maximum use of observation data in hand. This will benefit process-based models, and improve our understanding of SHR response to future anomalous environmental conditions.

Yao, Yitong↗
DOE OSTI2016

Deeply Virtual Pseudoscalar Meson Production at Jefferson Lab and Transversity GPDs

Transverse-momentum dependent parton distribution functions (TMDs) provide a description of nucleon structure in terms of the parton transverse momentum and its transverse spin. At leading twist there are eight TMDs, each offering a unique feature of quarks in a polarized or an unpolarized nucleon. The Sivers distribution is one of the most interesting TMD due to its non-universality. It has been extracted using the data from semi-inclusive deep-inelastic scattering (SIDIS), but there is no data yet from spin-dependent Drell-Yan (DY) process. Such measurement will provide a crucial test of TMD formalism which predicts an equal magnitude and opposite sign for the Sivers function extracted from SIDIS and DY process. We will discuss key future measurements of TMDs using both SIDIS and DY process with a focus on Hall A SoLID SIDIS program at Jefferson Lab.

Kubarovsky, Valery P.↗
DOE OSTI2023

Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data

High-energy large-scale particle colliders produce data at high speed in the order of 1 terabytes per second in nuclear physics and petabytes per second in high energy physics. Developing real-time data compression algorithms to reduce such data at high throughput to fit permanent storage has drawn increasing attention. Specifically, at the newly constructed sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC), a time projection chamber is used as the main tracking detector, which records particle trajectories in a volume of three-dimensional (3D) cylinder. The resulting data are usually very sparse with occupancy around 10.8%. Such sparsity presents a challenge to conventional learning-free lossy compression algorithms, such as SZ, ZFP, and MGARD. The 3D convolutional neural network (CNN)-based approach, Bicephalous Convolutional Autoencoder (BCAE), outperforms traditional methods both in compression rate and reconstruction accuracy. BCAE can also utilize the computation power of graphical processing units suitable for deployment in a modern heterogeneous highperformance computing environment. This work introduces two BCAE variants: BCAE++ and BCAE-2D. BCAE++ achieves a 15% better compression ratio and a 77% better reconstruction accuracy measured in mean absolute error compared with BCAE. BCAE-2D treats the radial direction as the channel dimension of an image, resulting in a 3× speedup in compression throughput. In addition, we demonstrate an unbalanced autoencoder with a larger decoder can improve reconstruction accuracy without significantly sacrificing throughput. Lastly, we observe both the BCAE++ and BCAE-2D can benefit more from using half-precision mode in throughput (76 - 79% increase) without loss in reconstruction accuracy. The source code and links to data and pretrained models can be found at https://github.com/BNL-DAQ-LDRD/NeuralCompression_v2

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1994

Economical ground data delivery

Data delivery in the Deep Space Network (DSN) involves transmission of a small amount of constant, high-priority traffic and a large amount of bursty, low priority data. The bursty traffic may be initially buffered and then metered back slowly as bandwidth becomes available. Today both types of data are transmitted over dedicated leased circuits. The authors investigated the potential of saving money by designing a hybrid communications architecture that uses leased circuits for high-priority network communications and dial-up circuits for low-priority traffic. Such an architecture may significantly reduce costs and provide an emergency backup. The architecture presented here may also be applied to any ground station-to-customer network within the range of a common carrier. The authors compare estimated costs for various scenarios and suggest security safeguards that should be considered.

Markley, Richard W.↗
DOE OSTI2022

CANShield: Signal-based Intrusion Detection for Controller Area Networks

Modern vehicles rely on complex cyber-physical systems made up of hundreds of electronic control units (ECUs) connected through controller area network (CAN) buses. However, the CAN bus attack surface is increasing due to advanced features in automobiles, making it prone to injection attacks. The ordinary injection attacks disrupt the typical timing properties of the CAN data stream, and the rule-based intrusion detection systems (IDS) can easily detect them. However, advanced attackers can inject false data to the signal level, maintaining the regular pattern/frequency of the CAN messages. Such attacks can bypass the rule-based IDS or any anomaly-based IDS built on binary payload data. To make the vehicles robust against such intelligent attacks, we propose CANShield, a signal-based intrusion detection framework for the CAN bus that consists of three modules. A data preprocessing module handles the high-dimensional CAN data stream at the signal level and make them suitable for any machine learning model. A data analyzer module consists of multiple deep autoencoder networks, each analyzing the time series data from a different perspective. Finally, an attack detection module uses an ensemble method to make the final decision. Evaluation results on a standard signal-based dataset show the effectiveness of the CANShield in detecting five advanced attacks.

Shahriar, Md Hasan↗
DOE OSTI2017

Advanced Gas Hydrate Reservoir Modeling Using Rock Physics

Prospecting for high saturation gas hydrate deposits can be greatly aided with improved approaches to seismic interpretation and especially if sets of seismic attributes can be shown as diagnostic or direct hydrocarbon indicators for high saturation gas hydrates in sands that would be of most interest for gas hydrate production. A large 3D seismic data set in the deep water Eastern Gulf of Mexico was screened for gas hydrates using a set of techniques and seismic signatures that were developed and proven in the Central deepwater Gulf of Mexico in the DOE Gulf of Mexico Joint Industry Project JIP Leg II in 2009 and recently confirmed with coring in 2017. A large gas hydrate deposit is interpreted in the data where gas has migrated from one of the few deep seated faults plumbing the Jurassic hydrocarbon source into the gas hydrate stability zone. The gas hydrate deposit lies within a flat-lying within Pliocene Mississippi Fan channel that was deposited outboard in a deep abyssal environment. The uniform architecture of the channel aided the evaluation of a set of seismic attributes that relate to attenuation and thin-bed energy that could be diagnostic of gas hydrates. Frequency attributes derived from spectral decomposition also proved to be direct hydrocarbon indicators by pseudo-thickness that could be only be reconciled by substituting gas hydrate in the pore space. The study emphasizes that gas hydrate exploration and reservoir characterization benefits from a seismic thin bed approach.

03 NATURAL GAS↗
DOE OSTI2023

The large-area hybrid-optics CLAS12 RICH: First years of data-taking

The CLAS12 deep-inelastic scattering experiment at the upgraded 12 GeV continuous electron beam accelerator facility of Jefferson Lab conjugates luminosity and wide acceptance to study the 3D nucleon structure in the yet poorly explored valence region, and to perform precision measurements in hadron spectroscopy. A large area ring-imaging Cherenkov detector has been designed to achieve the required hadron identification in the momentum range from 3 GeV/c to 8 GeV/c, with the kaon rate about one order of magnitude lower than the rate of pions and protons. The adopted solution comprises aerogel radiator and composite mirrors in a novel hybrid optics design, where either direct or reflected light could be imaged in a high-packed and high-segmented photon detector. The first RICH module was assembled during the second half of 2017 and installed at the beginning of January 2018, in time for the start of the experiment. The second RICH module, planned with the goal to be ready for the beginning of the operation with polarized targets, has been timely built despite the complications caused by the pandemic crisis and successfully installed in June 2022. Finally, the detector performance is here discussed with emphasis on the operation and stability during the data-taking, calibration and alignment procedures, reconstruction and pattern recognition algorithms, and particle identification.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2019

Feature Learning for Multispectral Satellite Imagery Classification Using Neural Architecture Search

Automated classification of remote sensing data is an integral tool for earth scientists, and deep learning has proven very successful at solving such problems. However, building deep learning models to process the data requires expert knowledge of machine learning. We introduce DELTA, a software toolkit to bridge this technical gap and make deep learning easily accessible to earth scientists. Visual feature engineering is a critical part of the machine learning lifecycle, and hence is a key area that will be automated by DELTA. Hand-engineered features can perform well, but require a cross functional team with expertise in both machine learning and the specific problem domain, which is costly in both researcher time and labor. The problem is more acute with multispectral satellite imagery, which requires considerable computational resources to process. In order to automate the feature learning process, a neural architecture search samples the space of asymmetric and symmetric autoencoders using evolutionary algorithms. Since denoising autoencoders have been shown to perform well for feature learning, the autoencoders are trained on various levels of noise and the features generated by the best performing autoencoders evaluated according to their performance on image classification tasks. The resulting features are demonstrated to be effective for Landsat-8 flood mapping, as well as benchmark datasets CIFAR10 and SVHN.

Robert Campbell↗
DOE OSTI2015

Retrieving 4-dimensional atmospheric boundary layer structure from surface observations and profiles over a single station

Most routine measurements from climate study facilities, such as the Department of Energy’s ARM SGP site, come from individual sites over a long period of time. While single-station data are very useful for many studies, it is challenging to obtain 3-dimensional spatial structures of atmospheric boundary layers that include prominent signatures of deep convection from these data. The principal objective of this project is to create realistic estimates of high-resolution (~ 1km × 1km horizontal grids) atmospheric boundary layer structure and the characteristics of precipitating convection. These characteristics include updraft and downdraft cumulus mass fluxes and cold pool properties over a region the size of a GCM grid column from analyses that assimilate surface mesonet observations of wind, temperature, and water vapor mixing ratio and available profiling data from single or multiple surface stations. The ultimate goal of the project is to enhance our understanding of the properties of mesoscale convective systems and also to improve their representation in analysis and numerical simulations. During the proposed period (09/15/2011–09/14/2014) and the no-cost extension period (09/15/2014–09/14/2015), significant accomplishments have been achieved relating to the stated goals. Efforts have been extended to various research and applications. Results have been published in professional journals and presented in related science team meetings and conferences. These are summarized in the report.

54 ENVIRONMENTAL SCIENCES↗