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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Multiquanta flux jumps in superconducting fractal

We study the magnetic field response of millimeter scale fractal Sierpinski gaskets (SG) assembled of superconducting equilateral triangular patches. Directly imaged quantitative induction maps reveal hierarchical periodic filling of enclosed void areas with multiquanta magnetic flux, which jumps inside the voids in repeating bundles of individual flux quanta Φ 0 . The number N s of entering flux quanta in different triangular voids of the SG is proportional to the linear size s of the void, while the field periodicity of flux jumps varies as 1/s. We explain this behavior by modeling the triangular voids in the SG with effective superconducting rings and by calculating their response following the London analysis of persistent currents, J s , induced by the applied field H a and by the entering flux. With changing H a , J s reaches a critical value in the vertex joints that connect the triangular superconducting patches and allows the giant flux jumps into the SG voids through phase slips or multiple Abrikosov vortex transfer across the vertices. The unique flux behavior in superconducting SG patterns, may be used to design tunable low-loss resonators with multi-line high-frequency spectrum for microwave technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Optoelectronic properties and interband transition of La-doped BaSnO{sub 3} transparent conducting films determined by variable temperature spectral transmittance

Perovskite-structured Ba{sub 1−x}La{sub x}SnO{sub 3} (x = 0–0.10) films have been directly grown on (0001) sapphire substrates by a sol-gel method. Optical properties and bandgap energy of the films have been investigated by transmittance spectra from 10 K to 450 K. It indicates that these films exhibit a high transmission of more than 80% in the visible region. With increasing temperature, there is a significant bandgap shrinkage of about 0.5 eV for lightly La doping (x ≤ 0.04) films. For heavily La doping concentration (x ≥ 0.06), the bandgap remains nearly stable with the temperature and La composition. This is due to the fact that the lattice expansion caused by La doping is close to the saturation for the film doped with x = 0.06. Moreover, temperature dependent conductivity behavior shows a similar pattern, which suggests that the doping concentration of La-doped BaSnO{sub 3} (BLSO) films has a saturated state. The La introduction can modify the Sn 5s-O 2p antibonding state and the nonbonding O 2p orbital, which remarkably affect the electronic bandgap of the BLSO films.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

NONPARAMETRIC BAYESIAN ESTIMATION OF PERIODIC LIGHT CURVES

Many astronomical phenomena exhibit patterns that have periodic behavior. An important step when analyzing data from such processes is the problem of identifying the period: estimating the period of a periodic function based on noisy observations made at irregularly spaced time points. This problem is still a difficult challenge despite extensive study in different disciplines. This paper makes several contributions toward solving this problem. First, we present a nonparametric Bayesian model for period finding, based on Gaussian Processes (GPs), that does not make assumptions on the shape of the periodic function. As our experiments demonstrate, the new model leads to significantly better results in period estimation especially when the light curve does not exhibit sinusoidal shape. Second, we develop a new algorithm for parameter optimization for GP which is useful when the likelihood function is very sensitive to the parameters with numerous local minima, as in the case of period estimation. The algorithm combines gradient optimization with grid search and incorporates several mechanisms to overcome the high computational complexity of GP. Third, we develop a novel approach for using domain knowledge, in the form of a probabilistic generative model, and incorporate it into the period estimation algorithm. Experimental results validate our approach showing significant improvement over existing methods.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Modeling of plasma beta effects on the island divertor transport in the standard configuration of W7-X

The influence of plasma beta effects on the edge plasma transport in the Wendelstein 7-X standard configuration is studied systematically by using EMC3-EIRENE combined with a 3D equilibrium code named HINT. The magnetic topology changes induced by plasma beta effects are significantly reflected in plasma transport behaviors and heat flux patterns on divertor targets. After validating the modeling strategy by comparisons with experimental data, the extended simulations for high performance plasmas show that the threshold separatrix density for accessing the power detachment is reduced in higher beta plasmas. Compared with the vacuum field case, the impurity radiation distributions with finite beta effects are modified in the magnetic island region. The divertor heat flux is distributed more evenly along the toroidal direction on the strike line at the vertical target. The strike line on the horizontal target moves towards the pumping gap with an increase in the plasma beta. In addition, the different pressure profiles with the same central beta also result in a modified heat flux pattern on the divertor targets.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Spatial and Quantum Confinement of Si Nanoparticles Deposited by Laser Electrodispersion onto Crystalline Si

C–V characteristics and DLTS spectra of heterostructures made up of layers of closely packed amorphous Si nanoparticles deposited by laser electrodispersion onto single-crystal p-Si substrates have been examined. The patterns observed in the behavior of the C–V characteristics and DLTS spectra measured in the dark and under illumination with white light at various bias pulse voltages U{sub b} and filling pulse voltages U{sub f} suggest that the spatially localized amorphous Si nanoparticles have an average size of less than 2 nm, which is comparable with the de Broglie electron wavelength, and are characterized by quantum confinement. The ground and excited states of quantum dots are formed and exhibit the Stark effect and effects of electricdipole and controllable metastable occupancy under illumination.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

A Million Person Study Innovation: Evaluating Cognitive Impairment and other Morbidity Outcomes from Chronic Radiation Exposure Through Linkages with the Centers for Medicaid and Medicare Services Assessment and Claims Data

Here, the study of One Million U.S. Radiation Workers and Veterans, the Million Person Study (MPS), examines the health consequences, both cancer and non-cancer, of exposure to ionizing radiation received gradually over time. Recently the MPS has focused on mortality patterns from neurological and behavioral conditions, e.g., Parkinson's disease, Alzheimer's disease, dementia, and motor neuron disease such as amyotrophic lateral sclerosis. A fuller picture of radiation-related late effects comes from studying both mortality and the occurrence (incidence) of conditions not leading to death. Accordingly, the MPS is identifying neurocognitive diagnoses from fee-for-service insurance claims from the Centers for Medicare and Medicaid Services (CMS), among Medicare beneficiaries beginning in 1999 (the earliest date claims data are available). Linkages to date have identified ∼540,000 workers with available health information. Such linkages provide individual information on important co-factor and confounding variables such as smoking, alcohol consumption, blood pressure, obesity, diabetes and many other health and demographic characteristics. The total person-level set of time-dependent variables, outcomes, organ-specific dose measures, co-factors, and demographics will be massive and much too large to be evaluated with standard software. Thus, development of specialized open-source software designed for large datasets (Colossus) is nearly complete. The wealth of information available from CMS claims data, coupled with individual dose reconstructions, will thus greatly enhance the quality and precision of health evaluations for this new field of low-dose radiation and neurocognitive effects.

Dauer, Lawrence T.↗
DOE OSTI2014

Intermediate-Scale Laboratory Experiments of Subsurface Flow and Transport Resulting from Tank Leaks

Washington River Protection Solutions contracted with Pacific Northwest National Laboratory to conduct laboratory experiments and supporting numerical simulations to improve the understanding of water flow and contaminant transport in the subsurface between waste tanks and ancillary facilities at Waste Management Area C. The work scope included two separate sets of experiments: • Small flow cell experiments to investigate the occurrence of potential unstable fingering resulting from leaks and the limitations of the STOMP (Subsurface Transport Over Multiple Phases) simulator to predict flow patterns and solute transport behavior under these conditions. Unstable infiltration may, under certain conditions, create vertically elongated fingers potentially transporting contaminants rapidly through the unsaturated zone to groundwater. The types of leak that may create deeply penetrating fingers include slow release, long duration leaks in relatively permeable porous media. Such leaks may have occurred below waste tanks at the Hanford Site. • Large flow experiments to investigate the behavior of two types of tank leaks in a simple layered system mimicking the Waste Management Area C. The investigated leaks include a relatively large leak with a short duration from a tank and a long duration leak with a relatively small leakage rate from a cascade line. The main objective of the small flow cell experiments was to test the ability of the numerical simulator STOMP to predict overall plume behavior and arrival times of conservative tracers in two different porous media for a relatively large range in injection rates. The large flow cell experiments were conducted to visualize flow and transport from short duration, fast leak and from long duration, slow leak scenarios.

contaminant transport↗
DOE OSTI2015

Stochastic Operations and Planning

This document discusses PNNL's efforts to mitigate the changing patterns of electrical system behavior, how it is dispatched, and exchanges of energy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

In Situ Inference for Earth System Predictability

An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

In Situ Inference for Earth System Predictability

Focal Area: Focal Area 3: Insight gleaned from complex simulated data using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Steady Spills, Stronger Signals: Machine Learning for Slow Spill Analysis

Particle accelerator experiments rely on stable, consistent proton beams to maximize scientific discovery. This presentation introduces beam spills, duty factor, and beam stability using a meteor shower analogy before exploring how feedback control and machine learning, including recurrent neural networks (RNNs), can analyze spill data, identify patterns, and predict beam behavior. Together, these approaches support beam optimization and improve our understanding of accelerator performance for experiments such as Mu2e.

Prescott, Matthew J. [Fermilab]↗
DOE OSTI2019

Thermal Behavior During the Selective Laser Melting Process of Ti-6Al-4V Powder in the Point Exposure Scan Pattern

Currently, there are two main scan patterns, including the continuous exposure scan pattern and the point exposure scan pattern, during the selective laser melting (SLM) process. The point exposure scan pattern allows a three-dimensional (3-D) printer to build finer detail features as a static molten pool that is more stable than a dynamic one. However, there has been limited theoretical research on the thermal behavior characteristics during the process in the point exposure scan pattern. Therefore, in this study, the simulation of thermal behavior during SLM of Ti-6Al-4V powder in the point exposure scan pattern was performed. The temperature evolution behavior of different positions and the effects of exposure time on the temperature evolution behavior of different positions, temperature distributions, and dimensions of the molten pool were investigated. The results showed that the direct exposure position and unexposed position had significantly different temperature evolution behaviors under a given condition, and the changed exposure time had the greatest influence on the direct exposure position compared with unexposed position. Moreover, the thermal accumulation effect of a former exposure point on a later one decreased with increasing exposure time. In addition, with the increase of exposure time, the maximum temperature of the molten pool was enhanced and the surface morphology of the molten pool changed from an approximate ellipse to an approximate circle. Besides, the molten pool dimensions were found to increase with exposure time, which indicated that the exposure time played an important role in the stability of the molten pool and the metallurgical bonding in the process. Furthermore, the dimensions of the molten pool and metallurgical bonding in the cross-sectional view were obtained through experiments. Good agreement was obtained when comparing the simulated results with the experimental ones.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Activity Characterization for Modeling Behavioral-driven Human Mobility in Platial Networks

The population is increasingly becoming tractable as more and more people carry handheld devices as part of their everyday activities. Recent studies have shown that handheld devices' generated traffic share is now more than 50% of total global online traffic. This has created an unprecedented opportunity for modeling human mobility behavior. For example, aggregate check-ins and dwell time can reveal building level occupancies. However, there are clear limits to accurate modeling (e.g. reproducible, repeatable, and realistic), unless we decipher the underlying reason causing typical mobility patterns. We know that human behavior is a reflection of a set of activities, such as going to the gym or work, and which can be seen as a catalyst for humans to move from one location to another. This work envisions the use of activity characterization for modeling human mobility by introducing a context that maps activities to certain mobility patterns. In the end, we highlight the efficacy of the proposed approach by analyzing the impact of public policies surrounding stay at home order on human mobility.

Thakur, Gautam Malviya↗
DOE OSTI2025

A dataset for understanding self-reported patterns influencing residential energy decisions

Household occupant behavior and decision-making dynamics substantially impact technology uptake and residential building energy performance. Although significant research underscores the importance of social science in energy studies, few public data with representative samples on household energy decision-making patterns are available. The dataset (UPGRADE-E: Understanding Patterns Guiding Residential Adoption and Decisions about Energy Efficiency) presents 9,919 responses from U.S. residents of single-family and small multifamily homes. Derived from a national-scale internet survey, the dataset contains 391 variables: demographics, building characteristics, home modifications, willingness to adopt new technologies, motivations for making changes, barriers, program participation, trusted information sources, and energy scenarios. Responses were validated via internal consistency checks and comparison with other U.S. national scale datasets. UPGRADE-E advances knowledge of household energy related decision-making, tying demographics, home modifications, and self-reported cognitive drivers together at a scale and breadth that has not been previously achieved. Policymakers and researchers at local, regional, and national levels may leverage this dataset to understand drivers influencing the adoption of key technologies in U.S. homes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Understanding and Leveraging the I/O Patterns of Emerging Machine Learning Analytics

The scientific community is currently experiencing unprecedented amounts of data generated by cutting-edge science facilities. Soon facilities will be producing up to 1 PB/s which will force scientist to use more autonomous techniques to learn from the data. The adoption of machine learning methods, like deep learning techniques, in large-scale workflows comes with a shift in the workflow’s computational and I/O patterns. These changes often include iterative processes and model architecture searches, in which datasets are analyzed multiple times in different formats with different model configurations in order to find accurate, reliable and efficient learning models. This shift in behavior brings changes in I/O patterns at the application level as well at the system level. These changes also bring new challenges for the HPC I/O teams, since these patterns contain more complex I/O workloads. In this paper we discuss the I/O patterns experienced by emerging analytical codes that rely on machine learning algorithms and highlight the challenges in designing efficient I/O transfers for such workflows. We comment on how to leverage the data access patterns in order to fetch in a more efficient way the required input data in the format and order given by the needs of the application and how to optimize the data path between collaborative processes. We will motivate our work and show performance gains with a study case of medical applications.

Gainaru, Ana↗
DOE OSTI2025

Astrocyte FABP7 Modulates Seizure Activity-Dependent Protein Expression in Mouse Brain

Background/Objectives: Patients with epilepsy commonly experience patterns of seizures that change with sleep/wake behavior or diurnal rhythms. The cellular and molecular mechanisms that underlie these patterns in seizure activity are not well understood but may involve non-neuronal cells, such as astrocytes. Our previous studies show the critical importance of one specific astrocyte factor, the brain-type fatty acid binding protein Fabp7, in the regulation of time-of-day-dependent electroshock seizure threshold and neural activity-dependent gene expression in mice. Here, we examined whether Fabp7 influences differential seizure activity-dependent protein expression, by comparing Fabp7 knockout (KO) to wild-type (WT) mice under control conditions and after reaching the maximal electroshock seizure threshold (MEST). Methods: We analyzed the proteome in cortical–hippocampal extracts from MEST and SHAM groups of WT and KO mice using mass spectrometry (MS), followed by Gene Ontology (GO) and pathway analyses. GO and pathway analyses of all groups revealed a diverse set of up- and downregulated differentially expressed proteins (DEPs). Results: We identified 65 significant DEPs in the comparison of KO SHAM versus WT SHAM; 33 proteins were upregulated and 32 were downregulated. We found downregulation in mitochondrial-associated proteins in WT MEST compared to WT SHAM controls, including Slc1a4, Slc25a27, Cox7a2, Cox8a, Micos10, and Atp5mk. Several upregulated DEPs in the KO SHAM versus WT SHAM comparison were associated with the 20S proteasomal subunit, suggesting proteasomal activity is elevated in the absence of Fabp7 expression. We also observed 92 DEPs significantly altered in the KO MEST versus WT MEST, with 49 proteins upregulated and 43 downregulated. Conclusions: Together, these data suggest that the astrocyte Fabp7 regulation of time-of-day-mediated neural excitability is modulated by multiple cellular mechanisms, which include proteasomal pathways, independent of its role in activity-dependent gene expression.

Neural Excitability↗
DOE OSTI2017

Universality and critical behavior of the dynamical Mott transition in a system with long-range interactions

Here, we study numerically the voltage-induced breakdown of a Mott insulating phase in a system of charged classical particles with long-range interactions. At half-filling on a square lattice this system exhibits Mott localization in the form of a checkerboard pattern. We find universal scaling behavior of the current at the dynamic Mott insulator-metal transition and calculate scaling exponents corresponding to the transition. Our results are in agreement, up to a difference in universality class, with recent experimental evidence of a dynamic Mott transition in a system of interacting superconducting vortices.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Effect of Stereochemistry on Directed Self-Assembly of Poly(styrene-b-lactide) Films on Chemical Patterns

We demonstrated here for the first time that the stereochemistry of polylactide (PLA) blocks affected the assembly behaviors of PS-b-PLA on chemical patterns. Two PS-b-PLA block copolymers, where the PLA block is either racemic (PDLLA) or left-handed (PLLA), were synthesized and directed to assemble on chemical patterns with a wide range of L-s/L-o. PS-b-PDLLA was stretched up to 70% on chemical patterns, while PS-b-PLLA was only stretched by 20%. The assembly behavior of PS-b-PDLLA was different from AB diblock copolymer, but similar to that of ABA triblock copolymer. The high stretchability might be attributed to the formation of stereocomplexes in PDLLA blocks. Compared to ABA triblock copolymers, stereocomplexed diblock copolymers have much faster assembly kinetics. This observation provides a new concept to achieve large process windows by the introduction of specific interactions, for example, H-bonding, supramolecular interaction, and sterecomplexation, between polymer chains.

Li, Xiao↗