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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 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Orenia metallireducens sp. nov. strain Z6, a Novel Metal-reducing [member of the Phylum] Firmicute from the Deep Subsurface

A novel halophilic and metal-reducing bacterium, Orenia metallireducens strain Z6, was isolated from briny groundwater extracted from a 2.02 km-deep borehole in the Illinois Basin, IL. This organism shared 96% 16S rRNA gene similarity with Orenia marismortui but demonstrated physiological properties previously unknown for this genus. In addition to exhibiting a fermentative metabolism typical of the genus Orenia , strain Z6 reduces various metal oxides [Fe(III), Mn(IV), Co(III), and Cr(VI)], using H 2 as the electron donor. Strain Z6 actively reduced ferrihydrite over broad ranges of pH (6 to 9.6), salinity (0.4 to 3.5 M NaCl), and temperature (20 to 60°C). At pH 6.5, strain Z6 also reduced more crystalline iron oxides, such as lepidocrocite (γ-FeOOH), goethite (α-FeOOH), and hematite (α-Fe 2 O 3 ). Analysis of X-ray absorption fine structure (XAFS) following Fe(III) reduction by strain Z6 revealed spectra from ferrous secondary mineral phases consistent with the precipitation of vivianite [Fe 3 (PO 4 ) 2 ] and siderite (FeCO 3 ). The draft genome assembled for strain Z6 is 3.47 Mb in size and contains 3,269 protein-coding genes. Unlike the well-understood iron-reducing Shewanella and Geobacter species, this organism lacks the c -type cytochromes for typical Fe(III) reduction. Strain Z6 represents the first bacterial species in the genus Orenia (order Halanaerobiales ) reported to reduce ferric iron minerals and other metal oxides. This microbe expands both the phylogenetic and physiological scopes of iron-reducing microorganisms known to inhabit the deep subsurface and suggests new mechanisms for microbial iron reduction. In conclusion, these distinctions from other Orenia spp. support the designation of strain Z6 as a new species, Orenia metallireducens sp. nov.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Microbial sensor variation across biogeochemical conditions in the terrestrial deep subsurface

ABSTRACT Microbes can be found in abundance many kilometers underground. While microbial metabolic capabilities have been examined across different geochemical settings, it remains unclear how changes in subsurface niches affect microbial needs to sense and respond to their environment. To address this question, we examined how microbial extracellular sensor systems vary with environmental conditions across metagenomes at different Deep Mine Microbial Observatory (DeMMO) subsurface sites. Because two-component systems (TCSs) directly sense extracellular conditions and convert this information into intracellular biochemical responses, we expected that this sensor family would vary across isolated oligotrophic subterranean environments that differ in abiotic and biotic conditions. TCSs were found at all six subsurface sites, the service water control, and the surface site, with an average of 0.88 sensor histidine kinases (HKs) per 100 genes across all sites. Abundance was greater in subsurface fracture fluids compared with surface-derived fluids, and candidate phyla radiation (CPR) bacteria presented the lowest HK frequencies. Measures of microbial diversity, such as the Shannon diversity index, revealed that HK abundance is inversely correlated with microbial diversity ( r 2 = 0.81). Among the geochemical parameters measured, HK frequency correlated most strongly with variance in dissolved organic carbon ( r 2 = 0.82). Taken together, these results implicate the abiotic and biotic properties of an ecological niche as drivers of sensor needs, and they suggest that microbes in environments with large fluctuations in organic nutrients (e.g., lacustrine, terrestrial, and coastal ecosystems) may require greater TCS diversity than ecosystems with low nutrients (e.g., open ocean). IMPORTANCE The ability to detect extracellular environmental conditions is a fundamental property of all life forms. Because microbial two-component sensor systems convert information about extracellular conditions into biochemical information that controls their behaviors, we evaluated how two-component sensor systems evolved within the deep Earth across multiple sites where abiotic and biotic properties vary. We show that these sensor systems remain abundant in microbial consortia at all subterranean sampling sites and observe correlations between sensor system abundances and abiotic (dissolved organic carbon variation) and biotic (consortia diversity) properties. These results suggest that multiple environmental properties may drive sensor protein evolution and highlight the need for further studies of metagenomic and geochemical data in parallel to understand the drivers of microbial sensor evolution.

response regulator↗
DOE OSTI2013

Photoelectric and luminescence properties of GaSb-Based nanoheterostructures with a deep Al(As)Sb/InAsSb/Al(As)Sb quantum well grown by metalorganic vapor-phase epitaxy

The luminescence and photoelectric properties of heterostructures with a deep Al(As)Sb/InAsSb/Al(As)Sb quantum well grown on n-GaSb substrates by metalorganic vapor-phase epitaxy are investigated. Intense superlinear luminescence and increased optical power as a function of the pump current in the photon energy range of 0.6-0.8 eV are observed at temperatures of T = 77 and 300 K. The photoelectric, current-voltage, and capacitance characteristics of these heterostructures are studied in detail. The photosensitivity is examined with photodetectors operating in the photovoltaic mode in the spectral range of 0.9-2.0 {mu}m. The sensitivity maximum at room temperature is observed at a wavelength of 1.55 {mu}m. The quantum efficiency, detectivity, and response time of the photodetectors were estimated. The quantum efficiency and detectivity at the peak of the photosensitivity spectrum are as high as {eta} = 0.6-0.7 and D{sub {lambda}max}{sup *} = (5-7) Multiplication-Sign 10{sup 10} cm Hz{sup 1/2} W{sup -1}, respectively. The photodiode response time determined as the rise time of the photoresponse pulse from 0.1 to the level 0.9 is 100-200 ps. The photodiode transmission bandwidth is 2-3 GHz. Photodetectors with a deep Al(As)Sb/InAsSb/Al(As)Sb quantum well grown on n-GaSb substrates are promising foruse in heterodyne detection systems and in information technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Effect of electrostatic shielding on the photoelectric properties of heterostructures with deep QWs

The effect of the electrostatic potential induced by charge carriers of the same sign, localized in a deep quantum well, on the current–voltage characteristics of photodetector heterostructures is theoretically analyzed. It is shown for the example of a p–i–n structure with a single deep quantum well in the i-type region that the shielding of an external electric field makes the differential photoconductivity of the heterostructure higher than that in a p–i–n structure without an intermediate 2D layer.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Field study of group effects on the pullout capacity of “deep” helical piles in sand

This paper presents the results of a field load test program used to investigate group effects on the pullout capacity of single-helix “deep” helical piles/anchors in sand. The high tensile capacity and silent installation of helical piles has given them serious consideration as an alternative to conventional deep foundations and anchors for offshore renewable energy structures. New offshore applications may consider the use of groups of helical piles to resist structural loads. Group interaction effects are known to occur in helical piles, but there is a scarcity of field data on groups in sands under tensile loading. This study involved the installation and load testing of single-helix 152mm diameter round shaft piles and pile groups embedded in sand to depths of 12 and 18 helix diameters below the ground surface. The study was designed to explore the effects of close pile spacing, group configuration (i.e., number of piles), and soil strength (i.e., friction angle) as interpreted from cone penetration test (CPT) resistance. The results showed group efficiencies ranging from about 0.6 to 1.0 at a horizontal spacing of 2 to 3 times the helix diameter in sands with friction angles of about 39° to 44°. The data from this study may also be useful for validation of numerical models for analysis of helical pile group interactions.

Engineering↗
DOE OSTI2024

Deep quantum circuit simulations of low-energy nuclear states

Numerical simulation is an important method for verifying the quantum circuits used to simulate low-energy nuclear states. However, real-world applications of quantum computing for nuclear theory often generate deep quantum circuits that place demanding memory and processing requirements on conventional simulation methods. Here, we present advances in high-performance numerical simulations of deep quantum circuits to efficiently verify the accuracy of low-energy nuclear physics applications. Our approach employs novel methods for accelerating the numerical simulation including management of simulated mid-circuit measurements to verify projection based state preparation circuits. In this study, we test these methods across a variety of high-performance computing systems and our results show that circuits up to 21 qubits and more than 115,000,000 gates can be efficiently simulated.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning

Using deep convolutional neural network (CNN), the nature of the QCD transition can be identified from the final-state pion spectra from hybrid model simulations of heavy-ion collisions that combines a viscous hydrodynamic model with a hadronic cascade “after-burner”. Two different types of equations of state (EoS) of the medium are used in the hydrodynamic evolution. The resulting spectra in transverse momentum and azimuthal angle are used as the input data to train the neural network to distinguish different EoS. Different scenarios for the input data are studied and compared in a systematic way. A clear hierarchy is observed in the prediction accuracy when using the event-by-event, cascade-coarse-grained and event-fine-averaged spectra as input for the network, which are about 80%, 90% and 99%, respectively. A comparison with the prediction performance by deep neural network (DNN) with only the normalized pion transverse momentum spectra is also made. High-level features of pion spectra captured by a carefully-trained neural network were found to be able to distinguish the nature of the QCD transition even in a simulation scenario which is close to the experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Prospects for beyond the Standard Model physics searches at the Deep Underground Neutrino Experiment: DUNE Collaboration

Abstract The Deep Underground Neutrino Experiment (DUNE) will be a powerful tool for a variety of physics topics. The high-intensity proton beams provide a large neutrino flux, sampled by a near detector system consisting of a combination of capable precision detectors, and by the massive far detector system located deep underground. This configuration sets up DUNE as a machine for discovery, as it enables opportunities not only to perform precision neutrino measurements that may uncover deviations from the present three-flavor mixing paradigm, but also to discover new particles and unveil new interactions and symmetries beyond those predicted in the Standard Model (SM). Of the many potential beyond the Standard Model (BSM) topics DUNE will probe, this paper presents a selection of studies quantifying DUNE’s sensitivities to sterile neutrino mixing, heavy neutral leptons, non-standard interactions, CPT symmetry violation, Lorentz invariance violation, neutrino trident production, dark matter from both beam induced and cosmogenic sources, baryon number violation, and other new physics topics that complement those at high-energy colliders and significantly extend the present reach.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Anomalous electroweak physics unraveled via evidential deep learning

The ever-growing ecosystem of beyond standard model (BSM) calculations and parametrizations has motivated the development of systematic methods for making quantitative cross-comparisons over the wide range of possible models, especially with controllable uncertainties. In this setting, the language of uncertainty quantification (UQ) furnishes useful metrics for assessing statistical overlaps and discrepancies among BSM and related models. In this study, we leverage recent machine learning (ML) developments in evidential deep learning (EDL) for UQ to separate data (aleatoric) and knowledge (epistemic) uncertainties in a model-discrimination setting. We construct several potentially BSM-motivated scenarios for the anomalous electroweak interaction (AEWI) of neutrinos with nucleons in deep inelastic scattering ( v DIS). These scenarios are then quantitatively mapped, as a demonstration, alongside Monte Carlo replicas of the CT18 PDFs used to calculate the $\varDelta \chi ^{2}$ statistic for a typical multi-GeV v DIS experiment, CDHSW. Our framework effectively highlights areas of model agreement and provides a classification of out-of-distribution (OOD) samples. By offering the opportunity to quantitatively understand model overlaps, the approach presented in this work can help facilitate efficient BSM model exploration and exclusion for future New Physics searches.

AI↗
DOE OSTI2025

Efficient Training of Deep Neural Operator Networks via Randomized Sampling

Neural operators (NOs) employ deep neural networks to learn the mappings between infinitedimensional function spaces. Deep operator network (DeepONet), a popular NO architecture, has demonstrated success in the real-time prediction of complex dynamics across various scientific and engineering applications. In this work, we introduce a random sampling technique to be adopted during the training of DeepONet, aimed at improving the generalization ability of the model, while significantly reducing the computational time. The proposed approach targets the trunk network of the DeepONet model that outputs the basis functions corresponding to the spatiotemporal locations of the bounded domain on which the physical system is defined. While constructing the loss function, DeepONet training traditionally considers a uniform grid of spatiotemporal points at which all the output functions are evaluated for each iteration. This approach leads to a larger batch size, resulting in poor generalization and increased memory demands, due to the limitations of the stochastic gradient descent (SGD) optimizer. The proposed random sampling over the inputs of the trunk net mitigates these challenges, improving generalization and reducing the memory requirements during training, resulting in significant computational gains. We validate our hypothesis through three benchmark examples, demonstrating substantial reductions in training time while achieving comparable or lower overall test errors relative to the traditional training approach. Our results indicate that incorporating randomization in the trunk network inputs during training enhances the efficiency and robustness of DeepONet, offering a promising avenue for improving the framework’s performance in modeling complex physical systems.

Karumuri, Sharmila [Department of Civil & Systems ↗
DOE OSTI2015

Optimizing deep learning hyper-parameters through an evolutionary algorithm. In: MLHPC '15 Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments, Article No. 4

There has been a recent surge of success in utilizing Deep Learning (DL) in imaging and speech applications for its relatively automatic feature generation and, in particular for convolutional neural networks (CNNs), high accuracy classification abilities. While these models learn their parameters through data-driven methods, model selection (as architecture construction) through hyper-parameter choices remains a tedious and highly intuition driven task. To address this, Multi-node Evolutionary Neural Networks for Deep Learning (MENNDL) is proposed as a method for automating network selection on computational clusters through hyper-parameter optimization performed via genetic algorithms.

Young, Steven R.↗
DOE OSTI2022

Deep reinforcement learning with online data augmentation to improve sample efficiency for intelligent HVAC control

Deep Reinforcement Learning (DRL) has started showing success in real-world applications such as building energy optimization. Much of the research in this space utilized simulated environments to train RL-agent in an offline mode. Very few research have used DRL-based control in real-world systems due to two main reasons: 1) sample efficiency challenge---DRL approaches need to perform a lot of interactions with the environment to collect sufficient experiences to learn from, which is difficult in real systems, and 2) comfort or safety related constraints---user's comfort must never or at least rarely be violated. In this work, we propose a novel deep Reinforcement Learning framework with online Data Augmentation (RLDA) to address the sample efficiency challenge of real-world RL. We used a time series Generative Adversarial Network (TimeGAN) architecture as a data generator. We further evaluated the proposed RLDA framework using a case study of an intelligent HVAC control. With a ≈28% improvement in the sample efficiency, RLDA framework lays the way towards increased adoption of DRL-based intelligent control in real-world building energy management systems.

Kurte, Kuldeep↗
DOE OSTI2026

EPOC Deep Dive Retrospective: A Brief Overview of 7 years of Science Engagement Discussions

Understanding the appropriate ways cyberinfrastructure can be designed, implemented, and executed for scientific use cases requires a deep understanding of the way that researchers and educators interact with technology, and how it may be best implemented to suit their needs. The Engagement and Performance Operations Center (EPOC) has conducted a series of scientific “Deep Dives” of use cases at partner institutions to better understand the requirements for modern scientific innovation across the United States research complex. The results of these activities have revealed gaps in the way that technology has been used to foster research activities. This gap in cyberinfrastructure support has impacts for the overall productivity and innovation possibilities for scientific users.

Zurawski, Jason↗
DOE OSTI2021

Altered Regional Homogeneity and Functional Connectivity during Microlesion Period after Deep Brain Stimulation in Parkinson’s Disease

Background. Patients with Parkinson’s disease (PD) undergoing deep brain electrode implantation experience a temporary improvement in motor symptoms before the electrical stimulation begins. We usually call this the microlesion effect (MLE), but the mechanism behind it is not clear. Purpose. This study aimed to assess the alterations in brain functions at the regional and whole-brain levels, using regional homogeneity (ReHo) and functional connectivity (FC), during the postoperative microlesion period after deep brain stimulation (DBS) in PD patients. Method. Resting-state functional MRI data were collected from 27 PD patients before and after the first day of DBS and 12 healthy controls (HCs) in this study. The ReHo in combination with FC analysis was used to investigate the alterations of regional brain activity in all the subjects. Results. There were increased ReHo in the basal ganglia-thalamocortical circuit (left supplementary motor area and bilateral paracentral lobule), whereas decreased ReHo in the default mode network (DMN) (left angular gyrus, bilateral precuneus), prefrontal cortex (bilateral middle frontal gyrus), and the cerebello-thalamocortical (CTC) circuit (Cerebellum_crus2/1_L) after DBS. In addition, we also found abnormal FC in the lingual gyrus, cerebellum, and DMN. Conclusion. Microlesion of the thalamus caused by electrode implantation can alter the activity of the basal ganglia-thalamocortical circuit, prefrontal cortex, DMN, and CTC circuit and induce abnormal FC in the lingual gyrus, cerebellum, prefrontal cortex, and DMN among PD patients. The findings of this study contribute to the understanding of the mechanism of MLE.

Luo, Bei↗
DOE OSTI2022

Mechanical Properties of Functionally Graded Concrete Lining for Deep Underground Structures

With the mining depth of coal resources increasing, the thickness of traditional lining for deep mines becomes large. The bearing capacity of the outer lining cannot be fully utilized, so a new type of functionally graded lining structure (FGL) with radial Young’s modulus varying in gradient is proposed. In this study, through theoretical analysis and numerical simulation, the mechanical properties of the functionally graded lining were studied, including the characteristics of the elastic working state and the ultimate bearing state. The influences of the structural parameters of the functionally graded lining, including the inner radius, the thickness, Young’s modulus, the compressive strength of the concrete, Poisson’s ratio, and the number of layers on the mechanical properties were analyzed. The calculation formula of the ultimate bearing capacity of the lining and the calculation formula of the maximum tangential strain at the time of lining failure were put forward. The accuracy of the formula was verified by comparing with the numerical simulation results. The research results provide the basis for the construction of the design theory of a functionally graded lining structure and have great engineering significance for the construction of the kilometer-deep mines in the future.

Li, De-Chun↗
DOE OSTI2018

Deep Jointly-Informed neural networks

OJINN Is an easy-to-use deep neural network model that requires fewer user-specified hyper-parameters than traditional neural networks. The algorithm leverages decision trees trained on the data to determine an appropriate deep neural network architectures and weight Initializations. Optional functions also select the learning rate, batch size. and number of training Iterations necessary to create an accurate model.

Humbird, Kelli↗
DOE OSTI2021

Deep-Lynx-Python-Package

This software is a python package that interacts with the Application Programming Interface (API) suite provided by Deep Lynx. A python codebase may import this package in order to have access to these methods for communicating with a Deep Lynx instance.

Browning, JerenM↗
DOE OSTI2020

Deep Lynx Javascript Package

This software is a JavaScript package that interacts with the Application Programming Interface (API) suite provided by Deep Lynx. A JavaScript or TypeScript codebase may import this package in order to have access to these methods for communicating with a Deep Lynx instance.

Browning, JerenM↗