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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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We are using existing deep learning modeling architectures and data representation techniques, but we are consolidating the methods into a single set of training and evaluation scripts. Additionally, we test and evaluate the performance of the models on the largest set of solubility measurements available to date.
The seismic noise recorded by the Interior Exploration using Seismic Investigations, Geodesy, and Heat Transport (InSight) seismometer (Seismic Experiment for Interior Structure [SEIS]) has a strong daily quasi-periodicity and numerous transient microevents, associated mostly with an active Martian environment with wind bursts, pressure drops, in addition to thermally induced lander and instrument cracks. That noise is far from the Earth’s microseismic noise. Quantifying the importance of nonstochasticity and identifying these microevents is mandatory for improving continuous data quality and noise analysis techniques, including autocorrelation. Cataloging these events has so far been made with specific algorithms and operator’s visual inspection. We investigate here the continuous data with an unsupervised deep-learning approach built on a deep scattering network. This leads to the successful detection and clustering of these microevents as well as better determination of daily cycles associated with changes in the intensity and color of the background noise. We first provide a description of our approach, and then present the learned clusters followed by a study of their origin and associated physical phenomena. We show that the clustering is robust over several Martian days, showing distinct types of glitches that repeat at a rate of several tens per sol with stable time differences. We show that the clustering and detection efficiency for pressure drops and glitches is comparable to or better than manual or targeted detection techniques proposed to date, noticeably with an unsupervised approach. Finally, here we discuss the origin of other clusters found, especially glitch sequences with stable time offsets that might generate artifacts in autocorrelation analyses. We conclude with presenting the potential of unsupervised learning for long-term space mission operations, in particular, for geophysical and environmental observatories.
MicroBooNE is a large liquid argon time projection chamber (LArTPC) located at Fermilab. One of the experiments primary aims is to investigate the CCQE-like excess of ν ϵ events observed by MiniBooNE at low energy. A novel approach to analyzing LArTPC data using an amalgam of Deep Learning and traditional algorithms has been developed and is capable of end to end reconstruction and selection. We report here an overview and progress to event selection which yields greater than 99.999% rejection of backgrounds. Statistical, flux, and some cross section systematics have also been evaluated. Detector systematics and additional cross section systematics are still under study.
The ability to train deep neural networks on large data sets have made significant impacts onto artificial intelligence, but consume significant amounts of energy due to the need to move information from memory to logic units. In-memory "neuromorphic" computing presents an alternative framework that processes information directly on memory elements. In-memory computing has been limited by the poor performance of the analogue information storage element, often phase-change memory or memristors. To solve this problem, we developed two types of "redox transistors" using TiO 2 (anatase) which stores analogue information states through the electrochemical concentration of dopants in the crystal. The first type of redox transistor uses lithium as the electrochemical dopant ion, and its key advantage is low operating voltage. The second uses oxygen vacancies as the dopant, which is CMOS compatible and can retain state even when scaled to nanosized dimensions. Both devices offer significant advantages in terms of predictable analogue switching over conventional filamentary-based devices, and provide a significant advance in developing materials and devices for neuromorphic computing.
Spectroscopy is powerful and non-destructive technique that can be used to study a wide range of materials. Detailed data analysis is key to deep understanding
A workshop on artificial intelligence and machine learning (AI/ML) for advanced reactors (AR) was held October 5-6, 2021. The workshop was to be attended in-person at ANL but COVID restrictions forced the workshop to go virtual. The objectives of the workshop were to identify the most promising AI/ML opportunities for improving advanced reactor design, optimizing plant performance, and enhancing economic competitiveness and to develop an understanding of the scientific, engineering and licensing challenges facing their application. The workshop planning committee included GAIN, EPRI and NEI and members of three national laboratories (ANL, INL, and ORNL). The workshop was attended by more than 200 individuals representing academic and scientific institutions and the nuclear power industry. The definition put forth for an AI/ML system was one that perceives its environment and takes actions that maximize its chance of achieving its goals. In this report AI/ML refers to next generation algorithms that include deep learning, statistical analysis and data analytics and associated scientific computing and their potential application to the design, licensing, operation and maintenance of ARs. These methods typically incorporate models built from process data and may also include data generated by simulations that represent the behavior of a system. The workshop was organized in response to the growing interest in application of AI/ML for improving the economic competitiveness of nuclear energy. Increasingly more resources are being allocated to investigating the benefits of AI/ML methods. The DOE created the Artificial Intelligence & Technology Office to promote their development. And within the Office of Nuclear Energy, resources have been allocated to explore and understand the potential benefits of AI/ML. Additionally, the national laboratories are strategically positioned with DOE computing facilities such as Summit, Perlmutter, Aurora and Frontier that support large-scale simulations, hybrid HPC models with AI surrogates, and the exploration of new types of generative models emerging from multi-model data streams and sources. The workshop was organized with members of the AR community to understand the effort and to identify the level of interest and progress in this emerging technology. The workshop discussions focused on identifying opportunities for AI/ML across diverse areas of the nuclear industry and identifying current scientific and engineering challenges for advanced reactors that might be addressed through transformational uses of AI/ML. Discussion panels focused on four high-interest technical domains for advanced reactors: design, maintenance and operations, energy storage, and materials. The results of those discussions are summarized in this report. This includes opportunities that were identified for exploiting AI techniques and methods to improve the efficacy and efficiency of reactor analysis and to improve the operation and optimization of advanced reactors. Advanced reactor developers expressed an interest in learning more about AI/ML methods and their application. This included understanding whether ML methods can provide an advantage over existing nonlinear data regression methods for collapsing high-fidelity simulation results into faster running models. A consensus emerged that AR advances planned for the next decade will benefit from the use of AI/ML tools. The need exists to understand and model complex systems across length scales and modalities. AI/ML is a tool for discovery that can yield a set of engineering principles for use by nuclear engineers, licensing bodies, and operators to solve problems in plant design, safety analyses, autonomous operation, and predictive maintenance. While AI/ML represents a new set of tools, an awareness by the nuclear community of the full potential is still in the early stages so there is a need to increase awareness. It appears that the wide-spread adoption of AI/ML tools for ARs would be facilitated by future educational workshops that describe foundational methods and capabilities and describe successful applications.
This project developed methodologies to measure the in-situ principal stress in the deep subsurface through use of multiple independent, but complementary, seismic methods, laboratory verification, and development of theoretical frameworks. By leveraging existing regional and local datasets we developed, tested, and refined a set of diagnostic tools for determining the in-situ stress state with reduced uncertainty at and below reservoir depths (1.5-6 km). A set of novel tools was produced that are scale independent, such that their utility is equivalent on regional, field scale, and near borehole monitoring of principal stresses in reservoir underburden for carbon storage projects. During a 4-year Department of Energy (DOE) and Southern Company funded project, carried out by the Electric Power Research Institute (EPRI), Lawrence Livermore National Laboratory (LLNL), the University of Oklahoma (OU), and the U.S. Geological Survey (USGS), the project team developed methodologies to measure the far-field in-situ principal stress in the deep subsurface, leveraging induced seismicity data from waste-water disposal projects. These methodologies consisted in the use of well-established and technically advanced seismic processing methods, such as virtual seismometer method-moment tensor (VSM-MT) and shear wave splitting (SWS), that are adept at recovering the stress orientation and certain components of the stress tensor. These methods were applied to robust seismicity catalogs created with matched filter techniques near sites of active fluid disposal—a proxy for carbon storage sites where such datasets are more limited. Estimates of the stress orientation made with seismic processing tools were considered along with laboratory acoustic emission experiments conducted on rock samples from the region of interest. Stress orientations in the studied region do not vary significantly across distances of ~100 km, nor are they found to rotate through time as a consequence of local wastewater disposal, as previously speculated. Finally, the project team investigated the trade-offs among the different seismic methods and evaluated the range of uncertainty that is generated with these methodologies, which led to a practical use and refinement of the VSM-MT technique when it is applied to field datasets. Understanding the trade-offs between these different methods highlighted the potential benefits of improved quantification of uncertainties on stress field estimations.
Extended X-ray absorption fine structure (EXAFS) spectroscopy is crucial for determining the coordination environment of impurities and dopants; however, it requires difficult measurements. X-ray absorption near edge structure (XANES) spectroscopy and X-ray emission spectroscopy (XES) can be obtained easily but cannot be converted to determine structures. In this work we develop tools to map measured XANES to the EXAFS signal through machine learning, thereby facilitating the use of EXAFS structural-determination analyses on XANES data. Through the use of Deep Operator Networks (DeepONets), we are able to accurately predict the EXAFS spectrum between 6 and 14 Å -1 from the first 6 Å -1 (~100 eV) of the absorption spectrum of Cu 2+ substitutional defects in the Fe 3+ mineral hematite (a-Fe 2 O 3 ). This surprising finding implies that theoretical analyses of X-ray absorption spectra could be implemented that extract the same conclusions as high-quality EXAFS studies from spectra collected over a much smaller range of photon energies. To encourage similar efforts, the simulated x-ray spectra, machine learning, and fitting code is made publicly available.
We present studies of multiplicities and the cosf, and cos2f moments, in charged pion production in semi-inclusive deep inelastic scattering (SIDIS). The data set used was the E1-f run from the CEBAF Large Acceptance Spectrometer (CLAS) at Jefferson Lab which ran in 2003. The run used a 5.498 GeV longitudinally polarized electron beam and an unpolarized liquid hydrogen target. Two pion channels (\pi+ and \pi-) were studied over a broad kinematical range (x = 0.1-0.6, Q2 = 1.0-4.5 GeV2, z = 0.0-1.0, PT^2 = 0.0-1.0 GeV^2, and -180 < \phi_h < 180). These measurements give insights into the transverse momentum dependence of parton distribution functions (PDFs) describing the dynamics of quarks and gluons inside of the proton.
A variety of methods have been proposed to define and to quantify galaxy environments. While these techniques work well in general with spectroscopic redshift samples, their application to photometric redshift surveys remains uncertain. To investigate whether galaxy environments can be robustly measured with photo-z samples, we quantify how the density measured with the nearest-neighbor approach is affected by photo-z uncertainties by using the Durham mock galaxy catalogs in which the 3D real-space environments and the properties of galaxies are known exactly. Furthermore, we present an optimization scheme in the choice of parameters used in the 2D projected measurements that yield the tightest correlation with respect to the 3D real-space environments. By adopting the optimized parameters in the density measurements, we show that the correlation between the 2D projected optimized density and the real-space density can still be revealed, and the color–density relation is also visible out to z ∼ 0.8 even for a photo-z uncertainty (σ{sub Δ{sub z/(1+z)}}) up to 0.06. We find that at redshifts 0.3 < z < 0.5 a deep (i ∼ 25) photometric redshift survey with σ{sub Δ{sub z/(1+z)}} = 0.02 yields a performance in small-scale density measurement that is comparable to a shallower i ∼ 22.5 spectroscopic sample with ∼10% sampling rate. Finally, we discuss the application of the local density measurements to the Pan-STARRS1 Medium Deep Survey (PS-MDS), one of the largest deep optical imaging surveys. Using data from ∼5 square degrees of survey area, our results show that it is possible to measure local density and to probe the color–density relation with 3σ confidence level out to z ∼ 0.8 in the PS-MDS. The color–density relation, however, quickly degrades for data covering smaller areas.
We present galaxy stellar mass functions (GSMFs) at z = 4–8 from a rest-frame ultraviolet (UV) selected sample of ∼4500 galaxies, found via photometric redshifts over an area of ∼280 arcmin{sup 2} in the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS)/Great Observatories Origins Deep Survey (GOODS) fields and the Hubble Ultra Deep Field. The deepest Spitzer/IRAC data to date and the relatively large volume allow us to place a better constraint at both the low- and high-mass ends of the GSMFs compared to previous space-based studies from pre-CANDELS observations. Supplemented by a stacking analysis, we find a linear correlation between the rest-frame UV absolute magnitude at 1500 Å (M{sub UV}) and logarithmic stellar mass (logM{sub ∗}) that holds for galaxies with log(M{sub ∗}/M{sub ⊙})≲10. We use simulations to validate our method of measuring the slope of the logM{sub ∗}–M {sub UV} relation, finding that the bias is minimized with a hybrid technique combining photometry of individual bright galaxies with stacked photometry for faint galaxies. The resultant measured slopes do not significantly evolve over z = 4–8, while the normalization of the trend exhibits a weak evolution toward lower masses at higher redshift. We combine the logM{sub ∗}–M {sub UV} distribution with observed rest-frame UV luminosity functions at each redshift to derive the GSMFs, finding that the low-mass-end slope becomes steeper with increasing redshift from α=−1.55{sub −0.07}{sup +0.08} at z = 4 to α=−2.25{sub −0.35}{sup +0.72} at z = 8. The inferred stellar mass density, when integrated over M{sub ∗}=10{sup 8}–10{sup 13} M {sub ⊙}, increases by a factor of 10{sub −2}{sup +30} between z = 7 and z = 4 and is in good agreement with the time integral of the cosmic star formation rate density.
We combine wide and deep galaxy number-count data from the Galaxy And Mass Assembly, COSMOS/G10, Hubble Space Telescope (HST) Early Release Science, HST UVUDF, and various near-, mid-, and far-IR data sets from ESO, Spitzer, and Herschel. The combined data range from the far UV (0.15 μm) to far-IR (500 μm), and in all cases the contribution to the integrated galaxy light (IGL) of successively fainter galaxies converges. Using a simple spline fit, we derive the IGL and the extrapolated IGL in all bands. We argue that undetected low-surface-brightness galaxies and intracluster/group light are modest, and that our extrapolated-IGL measurements are an accurate representation of the extragalactic background light (EBL). Our data agree with most earlier IGL estimates and with direct measurements in the far IR, but disagree strongly with direct estimates in the optical. Close agreement between our results and recent very high-energy experiments (H.E.S.S. and MAGIC) suggests that there may be an additional foreground affecting the direct estimates. The most likely culprit could be the adopted model of zodiacal light. Finally we use a modified version of the two-component model to integrate the EBL and obtain measurements of the cosmic optical background (COB) and cosmic infrared background of 24{sub −4}{sup +4} nW m{sup −2} sr{sup −1} and 26{sub −5}{sup +5} nW m{sup −2} sr{sup −1} respectively (48%:52%). Over the next decade, upcoming space missions such as Euclid and the Wide Field Infrared Space Telescope will have the capacity to reduce the COB error to <1%, at which point comparisons to the very high-energy data could have the potential to provide a direct detection and measurement of the reionization field.
Although there has been much progress in understanding how galaxies evolve, we still do not understand how and when they stop forming stars and become quiescent. We address this by applying our galaxy spectral energy distribution models, which incorporate physically motivated star formation histories (SFHs) from cosmological simulations, to a sample of quiescent galaxies at 0.2<z<2.1. A total of 845 quiescent galaxies with multi-band photometry spanning rest-frame ultraviolet through near-infrared wavelengths are selected from the Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey (CANDELS) data set. We compute median SFHs of these galaxies in bins of stellar mass and redshift. At all redshifts and stellar masses, the median SFHs rise, reach a peak, and then decline to reach quiescence. At high redshift, we find that the rise and decline are fast, as expected, because the universe is young. At low redshift, the duration of these phases depends strongly on stellar mass. Low-mass galaxies (log(M{sub ∗}/M{sub ⊙})∼9.5) grow on average slowly, take a long time to reach their peak of star formation (≳4 Gyr), and then the declining phase is fast (≲2 Gyr). Conversely, high-mass galaxies (log(M{sub ∗}/M{sub ⊙})∼11) grow on average fast (≲2 Gyr), and, after reaching their peak, decrease the star formation slowly (≳3). These findings are consistent with galaxy stellar mass being a driving factor in determining how evolved galaxies are, with high-mass galaxies being the most evolved at any time (i.e., downsizing). The different durations we observe in the declining phases also suggest that low- and high-mass galaxies experience different quenching mechanisms, which operate on different timescales.
We present deep g- and r-band Magellan/Megacam photometry of two dwarf galaxy candidates discovered in the Dark Energy Survey (DES), Grus i and Indus ii (DES J2038–4609). For the case of Grus i, we resolved the main sequence turn-off (MSTO) and ~2 mags below it. The MSTO can be seen at g 0 ~24 with a photometric uncertainty of 0.03 mag. We show Grus i to be consistent with an old, metal-poor (~13.3 Gyr, [Fe/H] ~ -1.9) dwarf galaxy. We derive updated distance and structural parameters for Grus i using this deep, uniform, wide-field data set. We find an azimuthally-averaged halflight radius more than two times larger (~151 +21 -31 pc; ~$4\buildrel{\,\prime}\over{.} {16}_{-0.74}^{+0.54}$) and an absolute V-band magnitude ~-4.1 that is ~1 magnitude brighter than previous studies. We obtain updated distance, ellipticity, and centroid parameters that are in agreement with other studies within uncertainties. Although our photometry of Indus ii is ~2–3 magnitudes deeper than the DES Y1 public release, we find no coherent stellar population at its reported location. The original detection was located in an incomplete region of sky in the DES Y2Q1 data set and was flagged due to potential blue horizontal branch member stars. The best-fit isochrone parameters are physically inconsistent with both dwarf galaxies and globular clusters. We conclude that Indus ii is likely a false positive, flagged due to a chance alignment of stars along the line of sight.
Abstract. Evapotranspiration (ET) is critical in linking global water, carbon and energy cycles. However, direct measurement of global terrestrial ET is not feasible. Here, we first reviewed the basic theory and state-of-the-art approaches for estimating global terrestrial ET, including remote-sensing-based physical models, machine-learning algorithms and land surface models (LSMs). We then utilized 4 remote-sensing-based physical models, 2 machine-learning algorithms and 14 LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the ensemble means of annual global terrestrial ET estimated by these three categories of approaches agreed well, with values ranging from 589.6 mm yr-1 (6.56×104 km3 yr-1) to 617.1 mm yr-1 (6.87×10 4 km3 yr -1 ). For the period from 1982 to 2011, both the ensembles of remote-sensing-based physical models and machine-learning algorithms suggested increasing trends in global terrestrial ET (0.62 mm yr -2 with a significance level of p<0.05 and 0.38 mm yr -2 with a significance level of p<0.05, respectively). In contrast, the ensemble mean of the LSMs showed no statistically significant change (0.23 mm yr -2 , p>0.05), although many of the individual LSMs reproduced an increasing trend. Nevertheless, all 20 models used in this study showed that anthropogenic Earth greening had a positive role in increasing terrestrial ET. The concurrent small interannual variability, i.e., relative stability, found in all estimates of global terrestrial ET, suggests that a potential planetary boundary exists in regulating global terrestrial ET, with the value of this boundary being around 600 mm yr -1 . Uncertainties among approaches were identified in specific regions, particularly in the Amazon Basin and arid/semiarid regions. Improvements in parameterizing water stress and canopy dynamics, the utilization of new available satellite retrievals and deep-learning methods, and model–data fusion will advance our predictive understanding of global terrestrial ET.
Solar array as primary electrical power source of Pioneer deep space probes, comparing performance data with test and design data