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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Generation of short gamma-ray pulses on electron bunches formed in intense interfering laser beams with tilted fronts

It is shown that in the interference of multiple laser pulses with a relativistic intensity, phase and amplitude fronts of which are tilted at an angle with respect to their wave vector, effective traps of charged particles, which are moving at the velocity of light, are formed. Such traps are capable of capturing and accelerating the electrons produced in the ionisation of low-density gas by means of laser radiation. The accelerated electrons in the traps form a bunch, whose dimensions in all directions are much smaller than the laser radiation wavelength. Calculations show that the energy of accelerated electrons may amount to several hundred GeV at experimentally accessible relativistic laser intensities. As a result of the inverse Compton scattering, gamma-quanta with a high energy and narrow radiation pattern are emitted when these electrons interact with a laser pulse propagating from the opposite direction. The duration of emitted gamma-ray pulses constitutes a few attoseconds. The simulation is performed by solving the relativistic equation of motion for an electron with a relevant Lorentz force. (interaction of radiation with matter)

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Recent California tree mortality portends future increase in drought-driven forest die-off

Vegetation tolerance to drought depends on an array of site-specific environmental and plant physiological factors. This tolerance is poorly understood for many forest types despite its importance for predicting and managing vegetation stress. We analyzed the relationships between precipitation variability and forest die-off in California's Sierra Nevada and introduce a new measure of drought tolerance that emphasizes plant access to subsurface moisture buffers. We applied this metric to California's severe 2012-2015 drought, and show that it predicted the patterns of tree mortality. We then examined future climate scenarios, and found that the probability of droughts that lead to widespread die-off increases threefold by the end of the 21st century. Our analysis shows that tree mortality in the Sierra Nevada will likely accelerate in the coming decades and that forests in the Central and Northern Sierra Nevada that largely escaped mortality in 2012-2015 are vulnerable to die-off.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Moiré assisted fractional quantum Hall state spectroscopy

Intra-Landau level excitations in the fractional quantum Hall regime are not accessible via optical absorption measurements. Here we point out that optical probes are enabled by the periodic potentials produced by a moire pattern. Our observation is motivated by the recent observations of fractional quantum Hall incompressible states in moire-patterned graphene on a hexagonal boron nitride substrate, and is theoretically based on f-sum rule considerations supplemented by a perturbative analysis of the influence of the moire potential on many-body states.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

TOWARDS RAPID RESPONSE UPDATES OF POPULATIONS AT RISK

Understanding population at risks has been a focus of the LandScan program through its development of population estimates. With advancements in computer vision, deep learning technologies and access to High Performance Computing (HPC) and high resolution imagery, population estimates are now modeled at the building level. However, when those patterns are disrupted, rapid updates to population distribution estimates are needed to support humanitarian aid and response. Oak Ridge National Laboratory (ORNL) recently adapted an existing deep learning building footprint extraction model in development of a scalable approach to Building Damage Assessments (BDA). This new opportunity opens the possibility of automating BDA to support rapid population distribution estimate updates for geographic areas involved in geopolitical conflicts or natural events for humanitarian aid and response or where to focus recovery efforts. In addition, incorporate social surveys to further model human behavior under conflict or other scenarios that disrupt normal patterns of life.

Urban, Marie↗
DOE OSTI2022

AICCA: AI-Driven Cloud Classification Atlas

Clouds play an important role in the Earth’s energy budget, and their behavior is one of the largest uncertainties in future climate projections. Satellite observations should help in understanding cloud responses, but decades and petabytes of multispectral cloud imagery have to date received only limited use. This study describes a new analysis approach that reduces the dimensionality of satellite cloud observations by grouping them via a novel automated, unsupervised cloud classification technique based on a convolutional autoencoder, an artificial intelligence (AI) method good at identifying patterns in spatial data. Our technique combines a rotation-invariant autoencoder and hierarchical agglomerative clustering to generate cloud clusters that capture meaningful distinctions among cloud textures, using only raw multispectral imagery as input. Cloud classes are therefore defined based on spectral properties and spatial textures without reliance on location, time/season, derived physical properties, or pre-designated class definitions. We use this approach to generate a unique new cloud dataset, the AI-driven cloud classification atlas (AICCA), which clusters 22 years of ocean images from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua and Terra instruments—198 million patches, each roughly 100 km × 100 km (128 × 128 pixels)—into 42 AI-generated cloud classes, a number determined via a newly-developed stability protocol that we use to maximize richness of information while ensuring stable groupings of patches. AICCA thereby translates 801 TB of satellite images into 54.2 GB of class labels and cloud top and optical properties, a reduction by a factor of 15,000. The 42 AICCA classes produce meaningful spatio-temporal and physical distinctions and capture a greater variety of cloud types than do the nine International Satellite Cloud Climatology Project (ISCCP) categories—for example, multiple textures in the stratocumulus decks along the West coasts of North and South America. We conclude that our methodology has explanatory power, capturing regionally unique cloud classes and providing rich but tractable information for global analysis. AICCA delivers the information from multi-spectral images in a compact form, enables data-driven diagnosis of patterns of cloud organization, provides insight into cloud evolution on timescales of hours to decades, and helps democratize climate research by facilitating access to core data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Reconstruction of global gridded monthly sectoral water withdrawals for 1971–2010 and analysis of their spatiotemporal patterns

Human water withdrawal has increasingly altered the global water cycle in past decades, yet our understanding of its driving forces and patterns is limited. Reported historical estimates of sectoral water withdrawals are often sparse and incomplete, mainly restricted to water withdrawal estimates available at annual and country scales, due to a lack of observations at seasonal and local scales. In this study, through collecting and consolidating various sources of reported data and developing spatial and temporal statistical downscaling algorithms, we reconstruct a global monthly gridded (0.5°) sectoral water withdrawal dataset for the period 1971–2010, which distinguishes six water use sectors, i.e., irrigation, domestic, electricity generation (cooling of thermal power plants), livestock, mining, and manufacturing. Based on the reconstructed dataset, the spatial and temporal patterns of historical water withdrawal are analyzed. Results show that total global water withdrawal has increased significantly during 1971–2010, mainly driven by the increase in irrigation water withdrawal. Regions with high water withdrawal are those densely populated or with large irrigated cropland production, e.g., the United States (US), eastern China, India, and Europe. Seasonally, irrigation water withdrawal in summer for the major crops contributes a large percentage of total annual irrigation water withdrawal in mid- and high-latitude regions, and the dominant season of irrigation water withdrawal is also different across regions. Domestic water withdrawal is mostly characterized by a summer peak, while water withdrawal for electricity generation has a winter peak in high-latitude regions and a summer peak in low-latitude regions. Despite the overall increasing trend, irrigation in the western US and domestic water withdrawal in western Europe exhibit a decreasing trend. Our results highlight the distinct spatial pattern of human water use by sectors at the seasonal and annual timescales. Here, the reconstructed gridded water withdrawal dataset is open access, and can be used for examining issues related to water withdrawals at fine spatial, temporal, and sectoral scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Mesoporous EU-1 zeolite with enhanced accessibility and diffusion for bulky molecular reaction

Highlights: • Mesoporous EU-1 zeolites with adjustable mesoporosity have been first prepared. • One-dimensional in microporous to three-dimensional diffusion in mesoporous EU-1. • Obviously increment of available acid sites for bulky molecules in the zeolite. • Sufficiently reaction space for isomerization and disproportionation of cumene. - Abstract: Mesoporous EU-1 zeolites with adjustable mesoporosity have been prepared by employing silanized silica into the starting synthesis gel, based on the hinder effect of organic moiety to crystal growth. The synthesized mesoporous EU-1 zeolites were characterized by XRD, NMR, SEM/TEM and N{sub 2} adsorption/desorption at 77 K, showing the persistence of inherent microporous framework structure of EU-1 zeolite and the existence of additional mesopores within the range of 3–10 nm. The generation of mesoporosities is from the intercrystalline voids of nanocrystal aggregates. The introduction of mesopores in EU-1 zeolite increased the adsorption of mesitylene with the kinetics diameter larger than the micropore size, and made it possible for the diffusion pattern of n-heptane from one-dimensional in microporous EU-1 to three-dimensional in mesoporous EU-1. This result together with the enhanced accessibility of acidic sites of mesoporous EU-1 improved greatly the catalytic activity and product yields of cumene cracking reaction involving bulky molecules.

36 MATERIALS SCIENCE↗
DOE OSTI2022

FAIRification, Quality Assessment, and Missingness Pattern Discovery for Spatiotemporal Photovoltaic Data

The ongoing growth of the photovoltaic market has pushed the demand for power forecasting and performance evaluation for a huge population of PV power plants. Through access to a large number of time series data sets from different power plants, we have found common issues that impede the modeling process. Namely, the time series data are hard to transfer between groups due to differences in variable nomenclature, and the quality of the data sets can vary. We address the issue of variable nomenclature by FAIRifying spatiotemporal PV time series data. Through the creation of a solar power plant ontology, we propose standards for the naming and structure of metadata used to describe the data from these power plants. Using the structure from this ontology, we have developed both R and Python packages for the automation of the FAIRification process. We have also developed an R package that automates the analysis of the quality of a data set through the designation of letter grades. With access to large time series data sets across many power plants, we can utilize spatiotemporal coherence between the sites in order to improve the quality of our data. To solve the issue of data missingness, we propose the use of Spatiotemporal-GNN autoencoders to detect and impute missing values from a data set by utilizing data from power plants nearby.

14 SOLAR ENERGY↗
DOE OSTI2024

Increasing Electric Vehicle Adoption Among Disadvantaged Populations: A Case Study in LA

In the shift towards 100% carbon-free energy, ensuring equitable access to clean energy benefits is crucial. The adoption of electric vehicles (EVs), particularly in the personal light-duty vehicle segment, has gained traction, driven by various incentives at the federal, state, and local levels. However, disadvantaged populations face unique challenges in embracing EVs. This paper, using Los Angeles as a case study, explores EV adoption patterns among disadvantaged population groups in both a business-as-usual scenario and an equity scenario. Modeling reveals that by 2035, over half of EV owners will be from low- to middle income backgrounds with limited access to home charging. Strategies such as increasing incentives for used EV purchase from $2500 to $4000, targeted for disadvantaged communities, can boost EV adoption among low-middle income groups by 2%, while providing a $300 annual voucher to households using public charging can further facilitate equitable EV adoption.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Mesoscopic Elastic Distortions in GaAs Quantum Dot Heterostructures

Quantum devices formed in high-electron-mobility semiconductor heterostructures provide a route through which quantum mechanical effects can be exploited on length scales accessible to lithography and integrated electronics. The electrostatic definition of quantum dots in semiconductor heterostructure devices intrinsically involves the lithographic fabrication of intricate patterns of metallic electrodes. The formation of metal/semiconductor interfaces, growth processes associated with polycrystalline metallic layers, and differential thermal expansion produce elastic distortion in the active areas of quantum devices. Understanding and controlling these distortions present a significant challenge in quantum device development. In this paper, we report synchrotron X-ray nanodiffraction measurements combined with dynamical X-ray diffraction modeling that reveal lattice tilts with a depth-averaged value up to 0.04° and strain on the order of 10 –4 in the two-dimensional electron gas (2DEG) in a GaAs/AlGaAs heterostructure. Elastic distortions in GaAs/AlGaAs heterostructures modify the potential energy landscape in the 2DEG due to the generation of a deformation potential and an electric field through the piezoelectric effect. Finally, the stress induced by metal electrodes directly impacts the ability to control the positions of the potential minima where quantum dots form and the coupling between neighboring quantum dots.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Ice rule fragility via topological charge transfer in artificial colloidal ice

Artificial particle ices are model systems of constrained, interacting particles. They have been introduced theoretically to study ice-manifolds emergent from frustration, along with domain wall and grain boundary dynamics, doping, pinning-depinning, controlled transport of topological defects, avalanches, and memory effects. Recently such particle-based ices have been experimentally realized with vortices in nano-patterned superconductors or gravitationally trapped colloids. Here we demonstrate that, although these ices are generally considered equivalent to magnetic spin ices, they can access a novel spectrum of phenomenologies that are inaccessible to the latter. With experiments, theory and simulations we demonstrate that in mixed coordination geometries, entropy-driven negative monopoles spontaneously appear at a density determined by the vertex-mixture ratio. Unlike its spin-based analogue, the colloidal system displays a “fragile ice” manifold, where local energetics oppose the ice rule, which is instead enforced through conservation of the global topological charge. The fragile colloidal ice, stabilized by topology, can be spontaneously broken by topological charge transfer.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Method for conducting in situ high-temperature digital image correlation with simultaneous synchrotron measurements under thermomechanical conditions

This work presents a novel method of obtaining in situ strain measurements at high temperature by simultaneous digital image correlation (DIC), which provides the total strain on the specimen surface, and synchrotron x-ray diffraction (XRD), which provides lattice strains of crystalline materials. DIC at high temperature requires specialized techniques to overcome the effects of increased blackbody radiation that would otherwise overexpose the images. The technique presented herein is unique in that it can be used with a sample enclosed in an infrared heater, remotely and simultaneously with synchrotron XRD measurements. The heater included a window for camera access, and the light of the heater lamps is used as illumination. High-temperature paint is used to apply a random speckle pattern to the sample to allow the tracking of displacements and the calculation of the DIC strains. An inexpensive blue theatrical gel filter is used to block interfering visible and infrared light at high temperatures. This technique successfully produces properly exposed images at 870 °C and is expected to perform similarly at higher temperatures. The average strains measured by DIC were validated by an analytical calculation of the theoretical strain. Simultaneous DIC and XRD strain measurements of Inconel 718 (IN718) tensile test specimens were performed under thermal and mechanical loads and evaluated. Finally, this approach uses the fact that with DIC, the total strain is measured, including plastic strain, while with XRD, only elastic strain is captured. The observed differences were discussed with respect to the effective deformation mechanisms.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Modeling Electrodeposition in 3D Porous Architectures for Solid-State Li-Metal Batteries

Li-metal storage in three-dimensional (3D) electrodes is considered a potential dendrite-mitigation strategy. The large surface area and high porosity of these electrodes result in reduced local Li-plating current densities. The porous topology provides a scaffold for Li-deposition and stripping, maintaining both mechanical integrity and Li accessibility. The goal of this study is to understand how characteristics, such as geometry and material properties, affect the current distribution and deposition pattern. To this end, we developed a computational method to track material growth driven by electrodeposition within a complex geometry. This method ensures that the finite-element discretization remains conforming to the moving boundary while preserving an adequate mesh quality, and thus maintains solution accuracy. Using this new computational tool, we analyze the conditions under which porous anode architectures effectively expand the surface area of the charge-transfer interface, and self-regulate current density and dendrite growth.

3D electrode architectures↗
DOE OSTI2020

Nanostructural Analysis of Enzymatic and Non-enzymatic Brown Rot Fungal Deconstruction of the Lignocellulose Cell Wall†

Brown rot (BR) decay mechanisms employ carbohydrate-active enzymes (CAZymes) as well as a unique non-enzymatic chelator-mediated Fenton (CMF) chemistry to deconstruct lignocellulosic materials. Unlike white rot fungi, BR fungi lack peroxidases for lignin deconstruction, and also lack some endoglucanase/cellobiohydrolase activities. The role that the CMF mechanism plays in “opening up” the wood cell wall structure in advance of enzymatic action, and any interaction between CMF constituents and the selective CAZyme suite that BRs possess, is still unclear. Expression patterns for CMF redox metabolites and lytic polysaccharide monooxygenase (LPMO–AA9 family) genes showed that some LPMO isozymes were upregulated with genes associated with CMF at early stages of brown rot by Gloeophyllum trabeum. In the structural studies, wood decayed by the G. trabeum was compared to CMF-treated wood, or CMF-treated wood followed by treatment with either the early-upregulated LPMO or a commercial CAZyme cocktail. Structural modification of decayed/treated wood was characterized using small angle neutron scattering. CMF treatment produced neutron scattering patterns similar to that of the BR decay indicating that both systems enlarged the nanopore structure of wood cell walls to permit enzyme access. Enzymatic deconstruction of cellulose or lignin in raw wood samples was not achieved via CAZyme cocktail or LPMO enzyme action alone. CMF treatment resulted in depolymerization of crystalline cellulose as attack progressed from the outer regions of individual crystallites. Multiple pulses of CMF treatment on raw wood showed a progressive increase in the spacing between the cellulose elementary fibrils (EFs), indicating the CMF eroded the matrix outside the EF bundles, leading to less tightly packed EFs. Peracetic acid delignification treatment enhanced subsequent CMF treatment effects, and allowed both enzyme systems to further increase spacing of the EFs. Moreover, even after a single pulse of CMF treatment, both enzymes were apparently able to penetrate the cell wall to further increase EF spacing. The data suggest the potential for the early-upregulated LPMO enzyme to work in association with CMF chemistry, suggesting that G. trabeum may have adopted mechanisms to integrate non-enzymatic and enzymatic chemistries together during early stages of brown rot decay.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

An evolving Coupled Model Intercomparison Project phase 7 (CMIP7) and Fast Track in support of future climate assessment

The Coupled Model Intercomparison Project (CMIP) coordinates community-based efforts to answer key and timely climate science questions, facilitate delivery of relevant multi-model simulations through shared infrastructure, and support national and international climate assessments. Generations of CMIP have evolved through extensive community engagement from punctuated phasing into more continuous support for the design of experimental protocols, infrastructure for data publication and access, and public delivery of climate information. We identify four fundamental research questions motivating a seventh phase of coupled model intercomparison relating to patterns of sea surface temperature change, changing weather, the water–carbon–climate nexus, and tipping points. Key CMIP7 advances include an expansion of baseline experiments, a focus on CO 2 -emissions-driven experiments, sustained support for community MIPs, periodic updating of historical forcings and diagnostics requests, and a collection of prioritized experiments, or the “Assessment Fast Track”, drawn from community MIPs to support climate research, assessment, and service goals across prediction and projection, characterization, attribution, and process understanding.

Environmental sciences↗
DOE OSTI2022

Persistent memory as an effective alternative to random access memory in metagenome assembly

Abstract Background The assembly of metagenomes decomposes members of complex microbe communities and allows the characterization of these genomes without laborious cultivation or single-cell metagenomics. Metagenome assembly is a process that is memory intensive and time consuming. Multi-terabyte sequences can become too large to be assembled on a single computer node, and there is no reliable method to predict the memory requirement due to data-specific memory consumption pattern. Currently, out-of-memory (OOM) is one of the most prevalent factors that causes metagenome assembly failures. Results In this study, we explored the possibility of using Persistent Memory (PMem) as a less expensive substitute for dynamic random access memory (DRAM) to reduce OOM and increase the scalability of metagenome assemblers. We evaluated the execution time and memory usage of three popular metagenome assemblers (MetaSPAdes, MEGAHIT, and MetaHipMer2) in datasets up to one terabase. We found that PMem can enable metagenome assemblers on terabyte-sized datasets by partially or fully substituting DRAM. Depending on the configured DRAM/PMEM ratio, running metagenome assemblies with PMem can achieve a similar speed as DRAM, while in the worst case it showed a roughly two-fold slowdown. In addition, different assemblers displayed distinct memory/speed trade-offs in the same hardware/software environment. Conclusions We demonstrated that PMem is capable of expanding the capacity of DRAM to allow larger metagenome assembly with a potential tradeoff in speed. Because PMem can be used directly without any application-specific code modification, these findings are likely to be generalized to other memory-intensive bioinformatics applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Analysis of WRF extreme daily precipitation over Alaska using self‐organizing maps

Abstract We analyze daily precipitation extremes from simulations of a polar‐optimized version of the Weather Research and Forecasting (WRF) model. Simulations cover 19 years and use the Regional Arctic System Model (RASM) domain. We focus on Alaska because of its proximity to the Pacific and Arctic oceans; both provide large moisture fetch inland. Alaska's topography also has important impacts on orographically forced precipitation. We use self‐organizing maps (SOMs) to understand circulation characteristics conducive for extreme precipitation events. The SOM algorithm employs an artificial neural network that uses an unsupervised training process, which results in finding general patterns of circulation behavior. The SOM is trained with mean sea level pressure (MSLP) anomalies. Widespread extreme events, defined as at least 25 grid points experiencing 99th percentile precipitation, are examined using SOMs. Widespread extreme days are mapped onto the SOM of MSLP anomalies, indicating circulation patterns. SOMs aid in determining high‐frequency nodes, and hence, circulations are conducive to extremes. Multiple circulation patterns are responsible for extreme days, which are differentiated by where extreme events occur in Alaska. Additionally, several meteorological fields are composited for nodes accessed by extreme and nonextreme events to determine specific conditions necessary for a widespread extreme event. Individual and adjacent node composites produce more physically reasonable circulations as opposed to composites of all extremes, which include multiple synoptic regimes. Temporal evolution of extreme events is also traced through SOM space. Thus, this analysis lays the groundwork for diagnosing differences in atmospheric circulations and their associated widespread, extreme precipitation events.

Glisan, Justin M.↗
DOE OSTI2018

Comparison of Peptide Ion Conformers Arising from Non-Helical and Helical Peptides Using Ion Mobility Spectrometry and Gas-Phase Hydrogen/Deuterium Exchange

The dominant gas-phase conformer of [M+3H]{sup 3+} ions of the model peptide acetyl-PSSSSKSSSSKSSSSKSSSSK has been examined with ion mobility spectrometry (IMS), gas-phase hydrogen deuterium exchange (HDX), and mass spectrometry (MS) techniques. The [M+3H]{sup 3+} peptide ions are observed predominantly as a relatively compact conformer type. Upon subjecting these ions to electron transfer dissociation (ETD), the level of protection for each amino acid residue in the peptide sequence is assessed. The overall per-residue deuterium uptake is observed to be relatively more efficient for the neutral residues than for the model peptide acetyl-PAAAAKAAAAKAAAAKAAAAK. In comparison, the N-terminal and C-terminal regions of the serine peptide show greater relative protection compared with interior residues. Molecular dynamics (MD) simulations have been used to generate candidate structures for collision cross section and HDX reactivity matching. Hydrogen accessibility scoring (HAS) for select structural candidates from MD simulations has been used to suggest conformer types that could contribute to the observed HDX patterns. The results are discussed with respect to recent studies employing extensive MD simulations of gas-phase structure establishment of a peptide system. .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗