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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

EVIDENCE FOR REDUCED SPECIFIC STAR FORMATION RATES IN THE CENTERS OF MASSIVE GALAXIES AT z = 4

We perform the first spatially resolved stellar population study of galaxies in the early universe ( z = 3.5–6.5), utilizing the Hubble Space Telescope Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey imaging data set over the GOODS-S field. We select a sample of 418 bright and extended galaxies at z = 3.5–6.5 from a parent sample of ∼8000 photometric-redshift-selected galaxies from Finkelstein et al. We first examine galaxies at 3.5 ≲ z ≲ 4.0 using additional deep K -band survey data from the HAWK-I UDS and GOODS Survey which covers the 4000 Å break at these redshifts. We measure the stellar mass, star formation rate, and dust extinction for galaxy inner and outer regions via spatially resolved spectral energy distribution fitting based on a Markov Chain Monte Carlo algorithm. By comparing specific star formation rates (sSFRs) between inner and outer parts of the galaxies we find that the majority of galaxies with high central mass densities show evidence for a preferentially lower sSFR in their centers than in their outer regions, indicative of reduced sSFRs in their central regions. We also study galaxies at z ∼ 5 and 6 (here limited to high spatial resolution in the rest-frame ultraviolet only), finding that they show sSFRs which are generally independent of radial distance from the center of the galaxies. This indicates that stars are formed uniformly at all radii in massive galaxies at z ∼ 5–6, contrary to massive galaxies at z ≲ 4.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Adaptive Neuron Apoptosis for Accelerating Deep Learning on Large Scale Systems

Machine Learning and Data Mining (MLDM) algorithms are becoming ubiquitous in {\em model learning} from the large volume of data generated using simulations, experiments and handheld devices. Deep Learning algorithms -- a class of MLDM algorithms -- are applied for automatic feature extraction, and learning non-linear models for unsupervised and supervised algorithms. Naturally, several libraries which support large scale Deep Learning -- such as TensorFlow and Caffe -- have become popular. In this paper, we present novel techniques to accelerate the convergence of Deep Learning algorithms by conducting low overhead removal of redundant neurons -- {\em apoptosis} of neurons -- which do not contribute to model learning, during the training phase itself. We provide in-depth theoretical underpinnings of our heuristics (bounding accuracy loss and handling apoptosis of several neuron types), and present the methods to conduct adaptive neuron apoptosis. We implement our proposed heuristics with the recently introduced TensorFlow and using its recently proposed extension with MPI. Our performance evaluation on two difference clusters -- one connected with Intel Haswell multi-core systems, and other with nVIDIA GPUs -- using InfiniBand, indicates the efficacy of the proposed heuristics and implementations. Specifically, we are able to improve the training time for several datasets by 2-3x, while reducing the number of parameters by 30x (4-5x on average) on datasets such as ImageNet classification. For the Higgs Boson dataset, our implementation improves the accuracy (measured by Area Under Curve (AUC)) for classification from 0.88/1 to 0.94/1, while reducing the number of parameters by 3x in comparison to existing literature, while achieving a 2.44x speedup in comparison to the default (no apoptosis) algorithm.

Siegel, Charles M.↗
DOE OSTI2020

Deep XMM–Newton observations of the most distant SPT-SZ galaxy cluster

ABSTRACT We present results from a 577 ks XMM–Newton observation of SPT-CL J0459–4947, the most distant cluster detected in the South Pole Telescope 2500 square degree (SPT-SZ) survey, and currently the most distant cluster discovered through its Sunyaev–Zel’dovich effect. The data confirm the cluster’s high redshift, z = 1.71 ± 0.02, in agreement with earlier, less precise optical/IR photometric estimates. From the gas density profile, we estimate a characteristic mass of $M_{500}=(1.8\pm 0.2)\times 10^{14}\, {\rm M}_{\odot }$; cluster emission is detected above the background to a radius of $\sim \!2.2\, r_{500}$, or approximately the virial radius. The intracluster gas is characterized by an emission-weighted average temperature of 7.2 ± 0.3 keV and metallicity with respect to Solar of $Z/\, Z_{\odot }=0.37\pm 0.08$. For the first time at such high redshift, this deep data set provides a measurement of metallicity outside the cluster centre; at radii $r\gt 0.3\, r_{500}$, we find $Z/\, Z_{\odot }=0.33\pm 0.17$ in good agreement with precise measurements at similar radii in the most nearby clusters, supporting an early enrichment scenario in which the bulk of the cluster gas is enriched to a universal metallicity prior to cluster formation, with little to no evolution thereafter. The leverage provided by the high redshift of this cluster tightens by a factor of 2 constraints on evolving metallicity models, when combined with previous measurements at lower redshifts.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2008

Delay-Throughput Performance of the Deep-Space Ka-band Link

In this paper, performance of a first-in, first-out (FIFO), selective retransmission scheme for the deep-space Ka-band link is presented and compared to the performance of a comparable X-band link. In this analysis, 16 months of water vapor radiometer (WVR) and advanced water vapor radiometer (AWVR) data from the three Deep Space Network (DSN) Communication Complexes (DSCC) were used to emulate weather effects on X-band and Ka-band links from Mars. Mars Reconnaissance Orbiter (MRO) X-band and Ka-band telecommunications parameters were used for spacecraft telecommunications capabilities. One pass per week per complex was selected from MRO's Deep Space Network (DSN) schedule from April 1, 2006 to August 31, 2007 for a total of 207 passes (69 passes per complex) for this analysis. For each pass both X-band and Ka-band links were designed using at most two data rates so that the expected pass capacity would be maximized subject to a minimum availability requirement (MAR). In conjunction with the WVR/AWVR data, elevation profiles of the selected passes and models for the performance of the antennas in the DSN were used to emulate the performance of both links. It was assumed that the retransmission of the data takes place not on the same pass as the original transmission but during subsequent passes. The data collected before a pass was assumed to be a fraction of the expected capacity of the pass as calculated through the link design process. Infinite spacecraft storage was assumed to obtain an upper bound on the spacecraft storage requirement. The independent parameters of this analysis were MAR and the ratio of data collected before a pass to the expected pass capacity. Since the selected passes did not occur at regular intervals, the delay in this analysis was measured in terms of number of passes. The throughput was measured in terms of number of bits received successfully on the ground. The results indicate that reasonable delay performance could be achieved with very high throughput for relatively low MAR values for data collection to expected pass capacity ratio of around 97% for Ka-band. The results indicate that, except for very low average delay requirements, the Ka-band link provides more than twice the throughput of the X-band link for the same amount of power consumed by the spacecraft. In addition, the results indicate that the required storage onboard the spacecraft is not prohibitive and good performance could be achieved by using a buffer size less than three times the maximum amount of data collected before a pass.

Shambayati, Shervin↗
NASA NTRS2008

Delay-Throughput Performance the Deep-Space Ka-Band Link

In this paper, performance of a first-in, first-out (FIFO), selective retransmission scheme for the deep-space Ka-band link is presented and compared to the performance of a comparable X-band link. In this analysis, 16 months of water vapor radiometer (WVR) and advanced water vapor radiometer (AWVR) data from the three Deep Space Network (DSN) Communication Complexes (DSCC) were used to emulate weather effects on X-band and Ka-band links from Mars. Mars Reconnaissance Orbiter (MRO) X-band and Ka-band telecommunications parameters were used for spacecraft telecommunications capabilities. One pass per week per complex was selected from MRO's Deep Space Network (DSN) schedule from April 1, 2006 to August 31, 2007 for a total of 207 passes (69 passes per complex) for this analysis. For each pass both X-band and Ka-band links were designed using at most two data rates so that the expected pass capacity would be maximized subject to a minimum availability requirement (MAR). In conjunction with the WVR/AWVR data, elevation profiles of the selected passes and models for the performance of the antennas in the DSN were used to emulate the performance of both links. It was assumed that the retransmission of the data takes place not on the same pass as the original transmission but during subsequent passes. The data collected before a pass was assumed to be a fraction of the expected capacity of the pass as calculated through the link design process. Infinite spacecraft storage was assumed to obtain an upper bound on the spacecraft storage requirement. The independent parameters of this analysis were MAR and the ratio of data collected before a pass to the expected pass capacity. Since the selected passes did not occur at regular intervals, the delay in this analysis was measured in terms of number of passes. The throughput was measured in terms of number of bits received successfully on the ground. The results indicate that reasonable delay performance could be achieved with very high throughput for relatively low MAR values for data collection to expected pass capacity ratio of around 97% for Ka-band. The results indicate that, except for very low average delay requirements, the Ka-band link provides more than twice the throughput of the X-band link for the same amount of power consumed by the spacecraft. In addition, the results indicate that the required storage onboard the spacecraft is not prohibitive and good performance could be achieved by using a buffer size less than three times the maximum amount of data collected before a pass.

Shambayati, Shervin↗
DOE OSTI2022

Enhanced analysis of experimental x-ray spectra through deep learning

X-ray spectroscopic data from high-energy-density laser-produced plasmas has long required thorough, time-consuming analysis to extract meaningful source conditions. There are often confounding factors due to rapidly evolving states and finite spatial gradients (e.g., the existence of multi-temperature, multi-density, multi-ionization states, etc.) that make spectral measurements and analysis difficult. Here, in this paper, we demonstrate how deep learning can be applied to enhance x-ray spectral data analysis in both speed and intricacy. Neural networks (NNs) are trained on ensemble atomic physics simulations so that they can subsequently construct a model capable of extracting plasma parameters directly from experimental spectra. Through deep learning, the models can extract temperature distributions as opposed to single or dual temperature/density fits from standard trial-and-error atomic modeling at a significantly reduced computational cost compared to traditional trial-and-error methods. These NNs are envisioned to be deployed with high repetition rate x-ray spectrometers in order to provide detailed real-time analysis of experimental spectra.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Degassing fluxes in a temperate hydropower reservoir predictable by deep‐water dissolved oxygen but highly sensitive to discharge variability

Hydropower reservoirs contribute to methane (CH 4 ) and carbon dioxide (CO 2 ) emissions, like all aquatic ecosystems. Unique to hydropower reservoirs are degassing emissions that occur when deep-water intakes move water with high CH 4 and CO 2 concentrations through turbines, leading to the release of these gases. However, few studies from hydropower reservoirs have measured seasonal variability and drivers of degassing fluxes, especially in temperate systems. We measured monthly degassing emissions in temperate Douglas Reservoir (Tennessee, USA) from 2023 to 2024. We found that degassing fluxes were highest in the summertime, and deep-water CH 4 and CO 2 concentrations were predictable by deep-water dissolved oxygen (DO) concentrations. Degassing emissions accounted for 37–62% of annually estimated CH 4 emissions, outweighing ebullitive emissions during summer months. We highlight the value of using DO data to estimate deep-water CH 4 and CO 2 concentrations and degassing fluxes at higher temporal resolution to improve annualization and extrapolation of reservoir degassing emissions at broader scales.

Pilla, Rachel M. [Oak Ridge National Laboratory (O↗
NASA NTRS2011

Global Distributions of Mineral Dust Properties from SeaWiFS and MODIS: From Sources to Sinks

The impact of natural and anthropogenic sources of mineral dust has gained increasing attention from scientific communities in recent years. Indeed, these airborne dust particles, once lifted over the source regions, can be transported out of the boundary layer into the free troposphere and can travel thousands of kilometers across the oceans resulting in important biogeochemical impacts on the ecosystem. Due to the relatively short lifetime (a few hours to about a week), the distributions of these mineral dust particles vary extensively in both space and time. Consequently, satellite observations are needed over both source and sink regions for continuous temporal and spatial sampling of aerosol properties. With the launch of SeaWiFS in 1997, Terra/MODIS in 1999, and Aqua/MODIS in 2002, high quality comprehensive aerosol climatology is becoming feasible for the first time. As a result of these unprecedented satellite data records, studies of the radiative and biogeochemical effects due to dust aerosols are now possible. In this study, we will show the comparisons of satellite retrieved aerosol optical thickness using Deep Blue algorithm with data from AERONET sunphotometers over desert and semi-desert regions as well as vegetated areas. Our results indicate reasonable agreements between these two. These new satellite products will allow scientists to determine quantitatively the aerosol properties near sources using high spatial resolution measurements from Sea WiFS and MODIS-like instruments. The multiyear satellite measurements since 1997 from Sea WiFS will be compared with those retrieved from MODIS and MISR, and will be utilized to investigate the interannual variability of source, pathway, and dust loading associated with the dust outbreaks over the entire globe. Finally, the trends observed over the last decade based upon the SeaWiFS time series in the amounts of tropospheric aerosols due to natural and anthropogenic sources (such as changes in the frequency of dust storms) will be discussed.

Hsu, N. Christina↗
DOE OSTI2026

Michel Electron Selection with SPINE for DUNE Far Detector Simulation

Michel electrons are a valuable input for particle detector calibration due to their consistent kinetic energy distribution. This report details the evaluation of a Michel electron identification method's application to simulated data from the DUNE (Deep Underground Neutrino Experiment) far detector. This method, which relies on the neural network-based particle classification software SPINE (Scalable Particle Imaging with Neural Embeddings), was developed and calibrated using simulated data for the SBND (Short-Baseline Neutrino Detector) experiment before being applied to simulated DUNE data from a 1x2x6 subset of far detector modules.

Wilson, Dante [Colorado State U.]↗
DOE OSTI2024

Monitoring of In Situ Remediation Technologies with SIP

Deconvoluting the spectral induced polarization (SIP) signal is critical to developing SIP as a robust technology to monitor delivery and subsurface geochemical reactions. Therefore, the primary goal of this project is to elucidate the sensitivity of SIP to geochemical reactions occurring during subsurface remediation. This document presents progress for fiscal year (FY) 2024 toward field-scale SIP monitoring of amendment delivery and reactivity for subsurface remediation. An interdisciplinary critical review team was assembled to review historical SIP data collected under the Deep Vadose Zone program. Based on feedback from the team additional experiments were designed and initiated for the calcium citrate phosphate technology for in situ formation of apatite and additional analysis was conducted with data from sulfur modified iron experiments to consider the potential for scaling monitoring with SIP to the field. In addition, the team outlined a proposed framework for future evaluation of SIP for environmental remediation monitoring to be implemented over the next 2-3 years.

47 OTHER INSTRUMENTATION↗
NASA NTRS2009

Ultra High Power and Efficiency Space Traveling-Wave Tube Amplifier Power Combiner with Reduced Size and Mass for NASA Missions

In the 2008 International Microwave Symposium (IMS) Digest version of our paper, recent advances in high power and efficiency space traveling-wave tube amplifiers (TWTAs) for NASA s space-to-Earth communications are presented. The RF power and efficiency of a new K-Band amplifier are 40 W and 50 percent and that of a new Ka-Band amplifier are 200 W and 60 percent. An important figure-of-merit, which is defined as the ratio of the RF power output to the mass (W/kg) of a TWT, has improved by a factor of ten over the previous generation Ka-Band devices. In this extended paper, a high power, high efficiency Ka-band combiner for multiple TWTs, based on a novel hybrid magic-T waveguide circuit design, is presented. The measured combiner efficiency is as high as 90 percent. In addition, at the design frequency of 32.05 GHz, error-free uncoded BPSK/QPSK data transmission at 8 megabits per second (Mbps), which is typical for deep space communications is demonstrated. Furthermore, QPSK data transmission at 622 Mbps is demonstrated with a low bit error rate of 2.4x10(exp -8), which exceeds the deep space state-of-the-art data rate transmission capability by more than two orders of magnitude. A potential application of the TWT combiner is in deep space communication systems for planetary exploration requiring transmitter power on the order of a kilowatt or higher.

Simons, Rainee N.↗
NASA NTRSDate not supplied

Pilot Workload Rating Predictions Using Image Data and Recurrent Neural Networks

In this work, we augmented existing methods for estimating pilot workload ratings with deep neural networks trained using data from simulated flight tests in the Vertical Motion Simulator (VMS). We used an existing method, Spare Capacity Operations Estimator (SCOPE), along with a recurrent neural network and conducted comparison studies between the two methods individually, and when used together. We found that using both methods together can improve the result over using either approach alone. In our first test case, we achieved an improved linear correlation coefficient of 0.409 over that of SCOPE alone at 0.352 on the training dataset. Through cross validation, we also found that the results may be dependent on the split of training vs. validation data, and that further investigation should be conducted to understand what additional inputs to the neural network model should be made.

Image Data↗
NASA NTRSDate not supplied

Hourly Stream Heights – A Short-term Deep Learning Prediction Model

River flooding can have a detrimental impact on a community by causing loss of life, loss or damage to property, and damage to infrastructure. Having the capability to forecast flooding events can prevent the loss of life and mitigate damage to property. A programmatic, data-driven approach using deep learning to forecast a stream’s gauge height every 6 hours has been developed by NASA’s Short-term Prediction Research and Transition Center (SPoRT) and is currently in operation. Based on our end user engagement and feedback, this medium-term product has been successfully adopted by several National Weather Service (NWS) Forecast Offices and River Forecast Centers (RFC). SPoRT is currently researching and developing a short-term hourly deep learning model which will be useful in forecasting flash flood events.

Michael Antia↗
DOE OSTI2019

Evaluation of Machine Learning Approaches to Estimate Aerosol Mixing State Metrics in Atmospheric Models

Aerosol mixing state describes how aerosol compositions are distributed among atmospheric aerosol particles in a population. Oversimplified assumptions of aerosol mixing state in atmospheric modeling can introduce errors in estimations of weather and climate-relevant aerosol microphysical properties. A more comprehensive representation of the aerosol mixing state can be achieved in principle with a Particle-resolved Monte Carlo (PartMC) model but at added computational cost that may be prohibitive for direct invocation in operational numerical weather prediction or multi-year climate simulations. The aim of our research is to explore the machine learning (ML) methodologies for estimating aerosol mixing state metrics, which we define here in three different ways: with respect to (a) hygroscopicity; (b) optical properties; and (c) chemical species abundance. We adopted a data-driven approach, leveraging deep learning and statistical learning techniques, to take advantage of massive PartMC model simulations. First, we performed particle-resolved simulations by PartMC to create a series of scenarios considering a range of global environmental conditions. Each scenario consists of aerosol populations and corresponding mixing state metrics. The gas concentration, aerosol mass concentration, environmental variables and mixing state metrics of each population constitute the datasets for machine learning implementations. We have adopted and evaluated various configurations of machine learning methodologies in this investigation, embracing deep learning, Extreme Gradient Boosting (XGBoost) algorithm, and the ensemble approaches. After a rigorous model selection process, we identified an appropriate model to derive estimates of aerosol mixing state metrics. We used the computational and data resources of the Oak Ridge Leadership Computing Facility (OLCF) and the ORNL Compute and Data Environment for Science (CADES). The NVIDIA DGX-1 hardware was used for the prototyping of the ML models. Our approach has allowed us to gain a new understanding of how machine learning methodologies can be applied to improve the representation of aerosol mixing state in atmospheric models and benefit the atmospheric research community. Next, we plan to extend our research and methodology to quantify some of the aerosol-related uncertainties in the E3SM Atmospheric Model (EAM) Version 1.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1980

DSN Monitor and Control System, Mark III-80

The Deep Space Network monitor and control system, Mark III-80 is described. The major implementations required to evolve from the Mark III-78 to the Mark III-80 configuration are identified. The affected facilities are the deep space stations and the network operations control center (NOCC). At the deep space stations a stand-alone host processor is implemented. Included are software (host software) changes which provide downline loading to the stand-alone host processor from a disc unit of any idle data system computer. At deep space stations with a 34 m antenna, the microwave subsystem is provided with an interface which allows remote configuration selection at the central monitor and control operator's position. In the NOCC, software changes are implemented to provide precision power monitor and GCF monitor displays.

Leflang, J. G.↗
NASA NTRS1981

Ground-to-ground communications for mission support

The objective of the NASA Communications Division is to provide operational communications in support of NASA projects and mission activities. Nascom is a generic term referring collectively to a global system of circuits and switching and terminal facilities established and operated by NASA to provide longhaul operational communications support for all NASA projects. Aspects of network evolution are discussed, taking into account the integrated Nascom network. A description is presented of the present Nascom network, giving attention to the Spaceflight Tracking and Data Network (STDN), the Deep Space Network (DSN), the voice network, and the Teletypewriter (TTY) Network. Concepts and techniques for the 1980's are also considered. It is pointed out that the Nascom Network will extend the Tracking and Data Relay Satellite System (TDRSS) forward- and return-link services to users of the TDRSS by providing ground-to-ground data communications links between the NASA Ground Terminal at White Sands, New Mexico, and major user spacecraft control centers and data capture/data processing facilities.

Dickinson, W. B.↗
DOE OSTI2019

Artificial Intelligence

Artificial intelligence is the study of intelligence agents as demonstrated by machines. It is an interdisciplinary field involving computer science as well as, various kinds of engineering and science, for example, robotics, bio-medical engineering, that accentuates automation of human acts and intelligence through machines. AI represents state-of-the-art use of machines to bring about algorithmic computation and understanding of tasks that include learning, problem solving, mapping, perception, and reasoning. Given the data and a description of its properties and relations between objects of interest, AI methods can perform the aforementioned tasks. Widely applied AI capabilities, e.g. learning, are now achievable at large scale through machine learning (ML), large volumes of data and specialized computational machines. ML encompasses learning without any kind of supervision (unsupervised learning) and learning with full supervision (supervised learning). Widely applied supervised learning techniques include deep learning and other machine learning methods that require less data than deep learning e.g. support vector machines, random forests. Unsupervised learning examples include dictionary learning, independent component analysis, and autoencoders. For application tasks with less labeled data, both supervised and unsupervised techniques can be adapted in a semi-supervised manner to produce accurate models and to increase the size of the labeled training data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Multi-fidelity information fusion with concatenated neural networks

Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.

42 ENGINEERING↗