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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Pegasus V/Andromeda XXXIV–a newly discovered ultrafaint dwarf galaxy on the outskirts of Andromeda

ABSTRACT We report the discovery of an ultrafaint dwarf in the constellation of Pegasus. Pegasus V (Peg V)/Andromeda XXXIV was initially identified in the public imaging data release of the DESI Legacy Imaging Surveys and confirmed with deep imaging from Gemini/GMOS-N. The colour–magnitude diagram shows a sparse red giant branch (RGB) population and a strong overdensity of blue horizontal branch stars. We measure a distance to Peg V of $D=692^{+33}_{-31}$ kpc, making it a distant satellite of Andromeda with MV = −6.3 ± 0.2 and a half-light radius of rhalf = 89 ± 41 pc. It is located ∼260 kpc from Andromeda in the outskirts of its halo. The RGB is well fitted by a metal-poor isochrone with [Fe/H] = −3.2, suggesting it is very metal poor. This, combined with its blue horizontal branch, could imply that it is a reionization fossil. This is the first detection of an ultrafaint dwarf outside the deep Pan-Andromeda Archaeological Survey area, and points to a rich, faint satellite population in the outskirts of our nearest neighbour.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15

Abstract Estimating the accuracy of quaternary structural models of protein complexes and assemblies (EMA) is important for predicting quaternary structures and applying them to studying protein function and interaction. The pairwise similarity between structural models is proven useful for estimating the quality of protein tertiary structural models, but it has been rarely applied to predicting the quality of quaternary structural models. Moreover, the pairwise similarity approach often fails when many structural models are of low quality and similar to each other. To address the gap, we developed a hybrid method (MULTICOM_qa) combining a pairwise similarity score (PSS) and an interface contact probability score (ICPS) based on the deep learning inter‐chain contact prediction for estimating protein complex model accuracy. It blindly participated in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 and performed very well in estimating the global structure accuracy of assembly models. The average per‐target correlation coefficient between the model quality scores predicted by MULTICOM_qa and the true quality scores of the models of CASP15 assembly targets is 0.66. The average per‐target ranking loss in using the predicted quality scores to rank the models is 0.14. It was able to select good models for most targets. Moreover, several key factors (i.e., target difficulty, model sampling difficulty, skewness of model quality, and similarity between good/bad models) for EMA are identified and analyzed. The results demonstrate that combining the multi‐model method (PSS) with the complementary single‐model method (ICPS) is a promising approach to EMA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Simulated Precipitation Diurnal Variation With a Deep Convective Closure Subject to Shallow Convection in Community Atmosphere Model Version 5 Coupled With CLUBB

In order to improve the physical consistency between shallow and deep convection, we modify the deep convective closure in the Community Atmosphere Model version 5 (CAM5) coupled with a third-order turbulence closure parameterization (i.e., Cloud Layers Unified by Binormals; CLUBB). The revised closure reserves a portion of the total convective available potential energy for shallow convection via utilizing the heating and moistening profiles from CLUBB to distribute moisture and energy between shallow and deep convection. Simulations at two resolutions (i.e. 2° and 0.5°, respectively) are conducted to investigate the impacts of convective closure on the simulated precipitation diurnal variations. Results from low-resolution simulations show that the revised closure suppresses deep convection until the lower troposphere is sufficiently moistened by shallow convection, which improves the precipitation diurnal variation simulations compared to the default closure, with the precipitation diurnal peak over tropical lands delayed from 12LST to 19LST. The revised closure better simulates the diurnal variations for precipitation over the Asian monsoon region, such as the delayed precipitation onset, but still fails to well capture the nighttime peak for precipitation there. This deficiency is alleviated to some extent when applying the revised closure in highresolution simulations, but nighttime precipitation is still underestimated probably because key processes responsible for nighttime convection are missing. Altogether, our results indicate that establishing the consistency between shallow and deep convection is critical for the precipitation diurnal cycle simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

DASEventNet: AI‐Based Microseismic Detection on Distributed Acoustic Sensing Data From the Utah FORGE Well 16A (78)‐32 Hydraulic Stimulation

Abstract Distributed acoustic sensing (DAS) has emerged as a promising seismic technology for monitoring microearthquakes (MEQs) with high spatial resolution. Efficient algorithms are needed for processing large DAS data volumes. This study introduces a deep learning (DL) model based on a Residual Convolutional Neural Network (ResNet) for detecting MEQs using DAS data, named as DASEventNet. The test data were collected from the Utah FORGE 16A (78)‐32 hydraulic stimulation experiments conducted in April 2022. The DASEventNet model achieves a remarkable accuracy of 100% when discriminating MEQs from noise in the raw test set of 260 examples. Surprisingly, the model identified weak MEQ signatures that have been manually categorized as noise. The decision‐making process with the model is decoded by the classic activation map, which illuminates learning features of the DASEventNet model. These features provide clear illustrations of weak MEQs and varied noise types. Finally, we apply the trained model to the entire period (∼7 days) of continuous DAS recordings and find that it discovers >5,700 new MEQs, previously unregistered in the public Silixa DAS catalog. The DASEventNet model significantly outperforms the traditional seismic method Short‐Term Average/Long‐Term Average (STA/LTA), which detected only 1,307 MEQs. The DASEventNet detection threshold is M w −1.80 compared to the minimum magnitude of M w −1.14 detected by STA/LTA. The spatiotemporal distribution of the newly identified MEQs defines an extensive stimulation zone and more accurately characterizes fracture geometry. Our results highlight the potential of DL for long‐term, real‐time microseismic monitoring that can improve enhanced geothermal systems and other activities that include subsurface hydraulic fracturing.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Fine-scale contemporary recombination variation and its fitness consequences in adaptively diverging stickleback fish

Despite deep evolutionary conservation, recombination rates vary greatly across the genome and among individuals, sexes and populations. Yet the impact of this variation on adaptively diverging populations is not well understood. Here we characterized fine-scale recombination landscapes in an adaptively divergent pair of marine and freshwater populations of threespine stickleback from River Tyne, Scotland. Through whole-genome sequencing of large nuclear families, we identified the genomic locations of almost 50,000 crossovers and built recombination maps for marine, freshwater and hybrid individuals at a resolution of 3.8 kb. We used these maps to quantify the factors driving variation in recombination rates. We found strong heterochiasmy between sexes but also differences in recombination rates among ecotypes. Hybrids showed evidence of significant recombination suppression in overall map length and in individual loci. Recombination rates were lower not only within individual marine–freshwater-adaptive loci, but also between loci on the same chromosome, suggesting selection on linked gene ‘cassettes’. Through temporal sampling along a natural hybrid zone, we found that recombinants showed traits associated with reduced fitness. Our results support predictions that divergence in cis-acting recombination modifiers, whose functions are disrupted in hybrids, may play an important role in maintaining differences among adaptively diverging populations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Deep learning methods for obtaining photometric redshift estimations from images

ABSTRACT Knowing the redshift of galaxies is one of the first requirements of many cosmological experiments, and as it is impossible to perform spectroscopy for every galaxy being observed, photometric redshift (photo-z) estimations are still of particular interest. Here, we investigate different deep learning methods for obtaining photo-z estimates directly from images, comparing these with ‘traditional’ machine learning algorithms which make use of magnitudes retrieved through photometry. As well as testing a convolutional neural network (CNN) and inception-module CNN, we introduce a novel mixed-input model that allows for both images and magnitude data to be used in the same model as a way of further improving the estimated redshifts. We also perform benchmarking as a way of demonstrating the performance and scalability of the different algorithms. The data used in the study comes entirely from the Sloan Digital Sky Survey (SDSS) from which 1 million galaxies were used, each having 5-filtre (ugriz) images with complete photometry and a spectroscopic redshift which was taken as the ground truth. The mixed-input inception CNN achieved a mean squared error (MSE) =0.009, which was a significant improvement ($30{{\ \rm per\ cent}}$) over the traditional random forest (RF), and the model performed even better at lower redshifts achieving a MSE = 0.0007 (a $50{{\ \rm per\ cent}}$ improvement over the RF) in the range of z < 0.3. This method could be hugely beneficial to upcoming surveys, such as Euclid and the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), which will require vast numbers of photo-z estimates produced as quickly and accurately as possible.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

A Data-Driven Framework for Power System Event Type Identification via Safe Semi-Supervised Techniques

Herein this paper investigates the use of phasor measurement unit (PMU) data with deep learning techniques to construct real-time event identification models for transmission networks. Increasing penetration of distributed energy resources represents a great opportunity to achieve decarbonization, as well as challenges in systematic situational awareness. When high-resolution PMU data and sufficient manually recorded event labels are available, the power event identification problem is defined as a statistical classification problem that can be solved by numerous cutting-edge classifiers. However, in real grids, collecting tremendous high-quality event labels is quite expensive. Utilities frequently have a large number of event records without in-depth details (i.e., unlabeled events). To bridge this gap, we propose a novel semi-supervised learning-based method to improve the performance of event classifiers trained with a limited number of labeled events by exploiting the information from massive unlabeled events. In other words, compared to existing data-driven methods, our method requires only a small portion of labeled data to achieve a similar level of accuracy. Meanwhile, this work discusses and addresses the performance degradation caused by class distribution mismatch between the training set and the real applications. Based on the proposed safe learning mechanism, our model does not directly use all unlabeled events during model training, but selectively uses them through a comprehensive evaluation procedure. Numerical studies on a sizable PMU dataset have been used to validate the performance of the proposed method.

42 ENGINEERING↗
DOE OSTI2020

DiHydrogen

DiHydrogen is the second version of the Hydrogen fork of the well-known distributed linear algebra library, Elemental. DiHydrogen is a GPU-accelerated distributed multilinear algebra interface with a particular emphasis on the needs of the scalable distributed deep learning training and inference. DiHydrogen is part of the Livermore Big Artificial Neural Network (LBANN) software stack.

Maruyama, Naoya↗
DOE OSTI2021

Diverse Super-Resolution (diversity_SR) [SWR-21-60]

Deep learning tools for enhancing the spatial resolution of wind data. The software is developed in Python using the TensorFlow deep learning package. Models for diversity super-resolution is provided. Included in the package are pretrained models with example code/data to perform the super-resolution as well as tools of training models for different enhancement- or data-types. The super-resolution is an inherently ill-conditioned problem, with multiple high-resolution fields plausibly mapping to the same coarse field. Considitional GANs provide a framework for generating a distribution of high-resolution realizations from a given low-resolution input. Stochastic estimation is used to inform the network of the expected degree and location of sub-grid diversity. The package includes a pretrained network to generate distributions of 10x-enhanced fields of wind data.

Glaws, Andrew↗
DOE OSTI2024

Optical potential uncertainties on exclusive tens-of-MeV Ar cross sections

Large liquid argon time projection chamber (LArTPC) neutrino detectors, such as those planned for the Deep Underground Neutrino Experiment (DUNE), show considerable promise as a platform for next-generation measurements of supernova neutrinos. Thanks to the neutron excess in $^{40}$Ar as well as the detailed tracking possible with LArTPCs, these detectors are expected to be uniquely capable of measuring supernova electron neutrinos with high statistics and minimal backgrounds. However, these technological advantages come at the price of complexities in data interpretation; reconstruction of the incident energies of supernova neutrinos in a future LArTPC-based analysis will be subject to a variety of systematic uncertainties related to nuclear interaction modeling. In this talk, we present a study of a subset of these uncertainties as implemented in the MARLEY event generator used by DUNE and other LArTPC neutrino experiments. Variations to the optical potential used in the MARLEY nuclear de-excitation model are applied to the calculation of exclusive tens-of-MeV neutrino-nucleus cross sections for the first time, and the impact on observables of interest for supernova neutrino detection is examined. We also discuss prospects for quantifying other sources of uncertainty on the MARLEY neutrino interaction model.

El-Haj, Luca Abu↗
DOE OSTI2025

Developing Drag Models for Non-Spherical Particles through Machine Learning

The overarching goal of this project is to produce comprehensive experimental and numerical datasets for gas-solid flows in well-controlled settings to understand the aerodynamic drag of non-spherical particles in the dense regime. The datasets and the gained knowledge will be utilized to train deep neural networks in TensorFlow to formulate a general drag model for use directly in NETL MFiX-DEM module in order to help to advance the accuracy and prediction fidelity of the computational tools that will be used in designing and optimizing fluidized beds and chemical looping reactors.

42 ENGINEERING↗
DOE OSTI2026

Improving Marine Energy Production Through Commercialization of a Low Cost, Drag-Reducing Slippery Coating (CRADA 679 Abstract)

Marine energy capture systems offer great promise for providing clean energy, but they operate in a challenging and dynamic environment and must be optimized to the highest extent possible. Computational studies predict that drag reduction on marine energy and blue economy systems will result in meaningful improvements in energy efficiency. Based on extensive coating experience, PNNL is proposing to bring to market a new drag-reducing coating called Superhydrophobic Lubricant-Infused Drag-Efficient Coating – SLIDE-Coat. PNNL has a deep knowledge base regarding this class of slippery coatings, and we have a group of enthusiastic industry partners who have committed to partnering, conducting field testing, and providing well over 50% cost share. In addition to validating the technology, the team will create a commercialization roadmap to ensure the commercial success of the technology after the government-sponsored two-year program is complete. The main technical goal is to design, manufacture, and experimentally validate a new coating that can reduce hydrodynamic skin friction drag by 20%. The main commercialization goal is to develop a roadmap that identifies activities needed to complete SLIDE-Coat’s development after the conclusion of this project. Year 1 will focus on adaptation and modification of the existing SLIC coating system to optimize drag reduction and assessment and quantification of drag reduction on relevant materials in a laboratory setting. Year 2 will focus on optimizing and demonstrating drag reduction with preferred coatings. The manufacturability, ease of application, adhesion to relevant surfaces, and consistency will be assessed. Quantification of drag reduction on prototype materials in a relevant marine field setting will be executed.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

Plateau de Bure High-z Blue Sequence Survey 2 (PHIBSS2): Search for Secondary Sources, CO Luminosity Functions in the Field, and the Evolution of Molecular Gas Density through Cosmic Time

We report on the results of a search for serendipitous sources in CO emission in 110 cubes targeting CO(2 − 1), CO(3 − 2), and CO(6 − 5) at z ∼ 1–2 from the second Plateau de Bure High-z Blue Sequence Survey (PHIBSS2). The PHIBSS2 observations were part of a 4 yr legacy program at the IRAM Plateau de Bure Interferometer aimed at studying early galaxy evolution from the perspective of molecular gas reservoirs. We present a catalog of 67 candidate secondary sources from this search, with 45 of the 110 data cubes showing sources in addition to the primary target that appear to be field detections, unrelated to the central sources. This catalog includes redshifts, line widths, and fluxes, as well as an estimation of their reliability based on their false-positive probability. We perform a search in the 3D Hubble Space Telescope/CANDELS catalogs for the secondary CO detections and tentatively find that ∼64% of these have optical counterparts, which we use to constrain their redshifts. Finally, we use our catalog of candidate CO detections to derive the CO(2 − 1), CO(3 − 2), CO(4 − 3), CO(5 − 4), and CO(6 − 5) luminosity functions over a range of redshifts, as well as the molecular gas mass density evolution. Despite the different methodology, these results are in very good agreement with previous observational constraints derived from blind searches in deep fields. They provide an example of the type of “deep-field” science that can be carried out with targeted observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The Cypress Sandstone Seal System

The Cypress Sandstone is the youngest and shallowest unit in the Illinois Basin that was featured in the United States Carbon Utilization and Storage Atlas IV as a target for saline carbon storage with an estimated 0.2 to 2.3 GT of storage potential. Additional research on a residual oil zone (ROZ) developed within the Cypress Sandstone has delineated 27 prospects with approximately 290.8 million m3 (1.8 billion barrels) of oil in place. 21 to 31 million m 3 (144 to 196 million barrels) of oil is estimated to be recoverable using carbon dioxide enhanced oil recovery (CO 2 -EOR). Storage of CO 2 associated with EOR in these ROZ prospects alone, not accounting for associated main pay zones (MPZs), underlying brine formation, or intervals adjacent to or between prospects, is estimated to be up to 10.4 billion tonnes. The Cypress Sandstone is thus well understood to be a CO 2 injection target, both for EOR and associated storage. However, the seal system overlying the Cypress Sandstone is poorly understood. Unlike deeper formations such as the Mt. Simon Sandstone or the St. Peter Sandstone which are either in use as a CO 2 sink or being characterized for prospective storage, respectively, the Cypress is not overlain by hundreds of feet of impermeable shale. Rather, the Cypress is overlain by a lithologically variable interval that is composed generally of shales and limestones with some sandstone in the part of the Basin where the Cypress is deep enough to facilitate CO 2 storage. Also, due to its status as one of the shallowest and most prolific oil reservoirs in the Basin, the seal system overlying the Cypress Sandstone has a relatively high number of legacy well penetrations. The purpose of this report is to characterize the Cypress Sandstone seal system using well logs and available core. Gross thickness, lithology (facies), and mineralogy of seals is described and mapped across the Basin. The column height of CO 2 that can be held is calculated using capillary pressure data from a representative core.

02 PETROLEUM↗
DOE OSTI2025

Cartesian equivariant representations for learning and understanding molecular orbitals

Qualitative and quantitative orbital properties such as bonding/antibonding character, localization, and orbital energies are critical to how chemists understand reactivity, catalysis, and excited-state behavior. Despite this, representations of orbitals in deep learning models have been very underdeveloped relative to representations of molecular geometries and Hamiltonians. Here, we apply state-of-the-art equivariant deep learning architectures to the task of assigning global labels to orbitals, namely energies characterizations, given the molecular coefficients from Hartree–Fock or density functional theory. The architecture we have developed, the Cartesian Equivariant Orbital Network (CEONET), shows how molecular orbital coefficients are readily featurized as equivariant node features common to all graph-based machine-learned potentials. We find that CEONET performs well at predicting difficult quantitative labels such as the orbital energy and orbital entropy. Furthermore, we find that the CEONET representation provides an intuitive latent space for differentiating orbital character for the qualitative assignment of e.g. bonding or antibonding character. In addition to providing a useful representation for further integrating deep learning with electronic structure theory, we expect CEONET to be useful for automatizing and interpreting the results of advanced electronic structure methods such as complete active space self-consistent field theory. In particular, the ability of CEONET to infer multireference character via the orbital entropy paves the way toward the machine-learned selection of active spaces.

chemical reactions↗
DOE OSTI2021

High-resolution radiation detection using Ni/SiO 2 /n-4H-SiC vertical metal-oxide-semiconductor capacitor

In this article, we demonstrate the radiation detection performance of vertical metal-oxide-semiconductor (MOS) capacitors fabricated on 20 μm thick n-4H-SiC epitaxial layers with the highest energy resolution ever reported. The 100 nm SiO2 layer was achieved on the Si face of n-4H-SiC epilayers using dry oxidation in air. The Ni/SiO2/n-4H-SiC MOS detectors not only demonstrated an excellent energy resolution of 0.42% (ΔE/E 100) for 5.48 MeV alpha particles but also caused a lower enhancement in the electronic noise components of the spectrometer compared with that observed for the best high-resolution Schottky barrier detectors. The MOS detectors also exhibited a high charge collection efficiency (CCE) of 96% at the optimized operating bias despite the presence of the oxide layer. A drift-diffusion model applied to the CCE vs gate bias voltage data revealed a minority (hole) carrier diffusion length of 24 μm. Capacitance mode deep level transient spectroscopy (C-DLTS) scans in the temperature range 84–800 K were carried out to identify the resolution limiting electrically active defects. Interestingly, the C-DLTS spectra revealed both positive and negative peaks, indicating the simultaneous presence of electron (majority) and hole (minority) trap centers. It has been inferred that at the steady-state bias for the C-DLTS measurement, the MOS detector operates in the inversion mode at certain device temperatures, causing holes to populate the minority trap centers and, hence, manifests minority carrier peaks as well.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Training calibration-based counterfactual explainers for deep learning models in medical image analysis

The rapid adoption of artificial intelligence methods in healthcare is coupled with the critical need for techniques to rigorously introspect models and thereby ensure that they behave reliably. This has led to the design of explainable AI techniques that uncover the relationships between discernible data signatures and model predictions. In this context, counterfactual explanations that synthesize small, interpretable changes to a given query while producing desired changes in model predictions have become popular. This under-constrained, inverse problem is vulnerable to introducing irrelevant feature manipulations, particularly when the model’s predictions are not well-calibrated. Hence, in this paper, we propose the TraCE (training calibration-based explainers) technique, which utilizes a novel uncertainty-based interval calibration strategy for reliably synthesizing counterfactuals. Given the wide-spread adoption of machine-learned solutions in radiology, our study focuses on deep models used for identifying anomalies in chest X-ray images. Using rigorous empirical studies, we demonstrate the superiority of TraCE explanations over several state-of-the-art baseline approaches, in terms of several widely adopted evaluation metrics. Our findings show that TraCE can be used to obtain a holistic understanding of deep models by enabling progressive exploration of decision boundaries, to detect shortcuts, and to infer relationships between patient attributes and disease severity.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Computer Model Calibration with Time Series Data Using Deep Learning and Quantile Regression

Computer models play a key role in many scientific and engineering problems. One major source of uncertainty in computer model experiments is input parameter uncertainty. Computer model calibration is a formal statistical procedure to infer input parameters by combining information from model runs and observational data. The existing standard calibration framework suffers from inferential issues when the model output and observational data are high-dimensional dependent data, such as large time series, due to the difficulty in building an emulator and the nonidentifiability between effects from input parameters and data-model discrepancy. To overcome these challenges, we, in this work, propose a new calibration framework based on a deep neural network (DNN) with long short-term memory layers that directly emulates the inverse relationship between the model output and input parameters. Adopting the “learning with noise” idea, we train our DNN model to filter out the effects from data-model discrepancy on input parameter inference. We also formulate a new way to construct interval predictions for DNN using quantile regression to quantify the uncertainty in input parameter estimates. Through a simulation study and real data application with the Weather Research and Forecasting Model Hydrological modeling system (WRF-Hydro), we show our approach can yield accurate point estimates and well-calibrated interval estimates for input parameters.

97 MATHEMATICS AND COMPUTING↗