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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Heterogeneous Activation, Local Structure, and Softness in Supercooled Colloidal Liquids

We experimentally characterize heterogeneous nonexponential relaxation in bidisperse supercooled colloidal liquids utilizing a recent concept called “softness”. Particle trajectory and structure data enable classification of particles into subgroups with different local environments and propensities to hop. We determine residence times t R between particle hops and show that t R derived from particles in the same softness subgroup are exponentially distributed. Using the mean residence time t¯ R for each softness subgroup, and a Kramers’ reaction rate model, we estimate the activation energy barriers E b for particle hops, and show that both t¯ R and E b are monotonic functions of softness. Lastly, we derive information about the combinations of large and small particle neighbors that determine particle softness, and we explicitly show that multiple exponential relaxation channels in the supercooled liquid give rise to its nonexponential behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Impact Analysis of Future Electric Vehicles Using Model of Real Distribution Feeders

Globally, the number of electric vehicles (EVs) continues to increase. This on-going trend poses challenges for power distribution systems. The inclusion of electric vehicle supply equipment (EVSE) should be compatible with a changing energy system, whose structure is becoming increasingly distributed. The impact of future EVs should be analyzed and considered in the system planning and upgrades. In this study, impact analysis of future EVs is conducted for a utility company of U.S. West Coast. Real data of distribution feeders is converted into the GridLAB-D model for running power flow analysis. An estimation of the additional loading of EVs in 2050 is provided by the utility company. Simple mitigation methods are tested to improve voltage profiles and complete this case study.

Xie, Jing↗
DOE OSTI2025

Extremely Scalable Distributed Computation of Contour Trees via Pre-Simplification

Contour trees offer an abstract representation of the level set topology in scalar fields and are widely used in topological data analysis and visualization. However, applying contour trees to large-scale scientific datasets remains challenging due to scalability limitations. Recent developments in distributed hierarchical contour trees have addressed these challenges by enabling scalable computation across distributed systems. Building on these structures, advanced analytical tasks—such as volumetric branch decomposition and contour extraction—have been introduced to facilitate large-scale scientific analysis. Despite these advancements, such analytical tasks substantially increase memory usage, which hampers scalability. In this paper, we propose a pre-simplification strategy to significantly reduce the memory overhead associated with analytical tasks on distributed hierarchical contour trees. We demonstrate enhanced scalability through strong scaling experiments, constructing the largest known contour tree—comprising over half a trillion nodes with complex topology—in under 15 minutes on a dataset containing 550 billion elements.

Li, Mingzhe [University of Utah]↗
DOE OSTI2020

Capture and Decomposition of the Nerve Agent Simulant, DMCP, Using the Zeolitic Imidazolate Framework (ZIF-8)

Understanding mechanisms of decontamination of chemical warfare agents (CWA) is an area of intense research aimed at developing new filtration materials to protect soldiers and civilians in case of state-sponsored or terrorist attack. In this study, we employed complementary structural, chemical, and dynamic probes and in situ data collection, to elucidate the complex chemistry, capture, and decomposition of the CWA simulant, dimethyl chlorophosphonate (DMCP). Our work reveals key details of the reactive adsorption of DMCP and demonstrates the versatility of zeolitic imidazolate framework (ZIF-8) as a plausible material for CWA capture and decomposition. Here, the in situ synchrotron-based powder X-ray diffraction (PXRD) and pair distribution function (PDF) studies, combined with Fourier transform infrared (FT-IR) spectroscopy, X-ray photoelectron spectroscopy (XPS), zinc K-edge X-ray absorption near edge structure (XANES), and Raman spectroscopies, showed that the unique structure, chemical state, and topology of ZIF-8 enable accessibility, adsorption, and hydrolysis of DMCP into the pores and revealed the importance of linker chemistry and Zn 2+ sites for nerve agent decomposition. DMCP decontamination and decomposition product(s) formation were observed by thermogravimetric analysis, FT-IR spectroscopy, and phosphorus (P) K-edge XANES studies. Differential PDF analysis indicated that the average structure of ZIF-8 (at the 30 Å scale) remains unchanged after DMCP dosing and provided information on the dynamics of interactions of DMCP with the ZIF-8 framework. Using in situ PXRD and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), we showed that nearly 90% regeneration of the ZIF-8 structure and complete liberation of DMCP and decomposition products occur upon heating.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Ion Distribution and Cation Exchange at Mica–Electrolyte Interfaces Probed with Deep Potential Molecular Dynamics

Here, we investigate the Stern layer structure and cation exchange mechanism at muscovite mica-electrolyte interfaces using nanosecond timescale molecular dynamics simulations based on deep neural network interatomic potentials trained on Density Functional Theory (DFT) data. Focusing on mica with exposed surface K + interfaced with aqueous NaCl and mica with surface Na + interfaced with KCl solution, we find that K + remains predominantly in inner-sphere configurations, while Na + exhibits notable populations in outer-sphere states. Most importantly, our simulations show that contact with an electrolyte solution results in the co-adsorption of multiple cation species, making the mica surface locally overcharged and thus reshaping the cation speciation in a manner that enhances the tendency of neighboring surface cations to desorb. These findings are consistent with recent experimental observations that co-adsorption of different cation species induces changes in cation speciation and slow kinetics of cation exchange at the muscovite-water interface, providing a basis for their detailed understanding.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The Global Structure of the Milky Way’s Stellar Halo Based on the Orbits of Local Metal-poor Stars

We analyze the global structure of the Milky Way (MW)'s stellar halo, including its dominant subcomponent, Gaia-Sausage-Enceladus (GSE). The method for reconstructing the global distribution of this old stellar component is to employ the superposition of the orbits covering the large MW's space, where each of the orbit-weighting factors is assigned following the probability that the star is located at its currently observed position. The selected local, metal-poor sample with [Fe/H] <–1, using Gaia Early Data Release 3 and Sloan Digital Sky Survey Data Release 16, shows that the global shape of the halo is systematically rounder at all radii in more metal-poor ranges, such that an axial ratio, q, is nearly 1 for [Fe/H] <–2.2 and ~0.7 for –1.4 < [Fe/H] < –1.0. It is also found that a halo in the relatively metal-rich range of [Fe/H] >–1.8 actually shows a boxy/peanut-like shape, suggesting a major merger event. The distribution of azimuthal velocities shows a disk-like flattened structure at –1.4 < [Fe/H] < –1.0, which is thought to be the metal-weak thick disk. For the subsample of stars showing GSE-like kinematics, at [Fe/H] >–1.8, its global density distribution has an axis ratio of 0.9, which is more spherical than the general halo sample, and an outer ridge at r ~ 20 kpc. This spherical shape is consistent with the features of accreted halo components, and the ridge suggests that the orbit of GSE's progenitor had an apocenter of ~20 kpc. Implications for the formation of the stellar halo are also presented.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Synthesis of observed and simulated rain microphysics to inform a new Bayesian statistical framework for microphysical parameterization in climate models (Final Scientific Report)

This project aimed to investigate a new approach to bulk microphysics schemes. We identified the need to move beyond the fixed structural assumptions and approximation of existing schemes. For example, most schemes assume some functional form for the rain drop size distribution (e.g. a gamma or exponential distribution). Most schemes then evolve some number of statistical moments of that distribution via various processes, such as evaporation, sedimentation, collision-coalescence, and collisional breakup. These microphysical processes, in turn, are typically some fixed functional form derived from either some other (more detailed) model, or using some single-particle rates that are then integrated over the size distribution. While these process rate functions may have some free parameters to adjust, in other cases doing so is impossible (e.g. when the process rates are analytical or piecewise solutions to some target function). Our goal was to build and test a scheme that assumed no size distribution form, and used a series of power laws as the basis for the process rates. Moments of the size distribution would be predicted, but no underlying size distribution would be specified. Any number or choice of size distribution moments could be used, and any number of power laws could be employed to model the process rates. Thus, our approach is flexible, and can seamlessly scale across levels of complexity, as demanded by the data. Bayesian inference then would provide the formalism to estimate the model parameters and structure, allowing for robust uncertainty quantification. We proposed testing this framework in idealized simulations using bin microphysical schemes as a data source. We also proposed using real data to inform our microphysics scheme, and also that we would integrate our scheme into WRF.

58 GEOSCIENCES↗
DOE OSTI2017

Structure in the 3D Galaxy Distribution. III. Fourier Transforming the Universe: Phase and Power Spectra

We demonstrate the effectiveness of a relatively straightforward analysis of the complex 3D Fourier transform of galaxy coordinates derived from redshift surveys. Numerical demonstrations of this approach are carried out on a volume-limited sample of the Sloan Digital Sky Survey redshift survey. The direct unbinned transform yields a complex 3D data cube quite similar to that from the Fast Fourier Transform of finely binned galaxy positions. In both cases, deconvolution of the sampling window function yields estimates of the true transform. Simple power spectrum estimates from these transforms are roughly consistent with those using more elaborate methods. The complex Fourier transform characterizes spatial distributional properties beyond the power spectrum in a manner different from (and we argue is more easily interpreted than) the conventional multipoint hierarchy. We identify some threads of modern large-scale inference methodology that will presumably yield detections in new wider and deeper surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

A multi-level anomaly detection algorithm for time-varying graph data with interactive visualization

This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating node probabilities, and these related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, this multi-scale analysis facilitates intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. Furthermore, to illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision greater than 0.786.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Effect of plasma parameters on characteristics of silicon nitride film deposited by single and dual frequency plasma enhanced chemical vapor deposition

This work investigates the deposition of hydrogenated amorphous silicon nitride films using various low-temperature plasmas. Utilizing radio-frequency (RF, 13.56 MHz) and ultra-high frequency (UHF, 320 MHz) powers, different plasma enhanced chemical vapor deposition processes are conducted in the mixture of reactive N{sub 2}/NH{sub 3}/SiH{sub 4} gases. The processes are extensively characterized using different plasma diagnostic tools to study their plasma and radical generation capabilities. A typical transition of the electron energy distribution function from single- to bi-Maxwellian type is achieved by combining RF and ultra-high powers. Data analysis revealed that the RF/UHF dual frequency power enhances the plasma surface heating and produces hot electron population with relatively low electron temperature and high plasma density. Using various film analysis methods, we have investigated the role of plasma parameters on the compositional, structural, and optical properties of the deposited films to optimize the process conditions. The presented results show that the dual frequency power is effective for enhancing dissociation and ionization of neutrals, which in turn helps in enabling high deposition rate and improving film properties.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Aerosol and Cloud Experiments in Eastern North Atlantic (ACE-ENA) Science Plan

With their extensive coverage, low clouds greatly impact global climate. Presently, low clouds are poorly represented in global climate models (GCMs), and the response of low clouds to changes in atmospheric greenhouse gases and aerosols remains the major source of uncertainty in climate simulations. The poor representations of low clouds in GCMs are in part due to inadequate observations of their microphysical and macrophysical structures, radiative effects, and the associated aerosol distribution and budget in regions where the aerosol impact is the greatest. The Eastern North Atlantic (ENA) is a region of persistent but diverse subtropical marine boundary-layer (MBL) clouds, whose albedo and precipitation are highly susceptible to perturbations in aerosol properties. Boundary-layer aerosol in the ENA region is influenced by a variety of sources, leading to strong variations in cloud condensation nuclei (CCN) concentration and aerosol optical properties. Recently a permanent ENA site was established by the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) Climate Research Facility on Graciosa Island in the Azores, providing invaluable information on MBL aerosol and low clouds. At the same time, the vertical structures and horizontal variabilities of aerosol, trace gases, cloud, drizzle, and atmospheric thermodynamics are critically needed for understanding and quantifying the budget of MBL aerosol, the radiative properties, precipitation efficiency, and lifecycle of MBL clouds, and the cloud response to aerosol perturbations. Much of this data can be obtained only through aircraft-based measurements. In addition, the interconnected aerosol and cloud processes are best investigated by a study involving simultaneous in situ aerosol, cloud, and thermodynamics measurements. Furthermore, in situ measurements are also necessary for validating and improving ground-based retrieval algorithms at the ENA site. This project is motivated by the need for comprehensive in situ characterizations of boundary-layer structure, and associated vertical distributions and horizontal variabilities of low clouds and aerosol over the Azores. ARM Aerial Facility (AAF) Gulfstream-1 (G-1) aircraft will be deployed at the ENA site during two intensive operational periods (IOPs) of early summer (June to July) of 2017 and winter (January to February) of 2018, respectively. Deployments during both seasons allow for examination of key aerosol and cloud processes under a variety of representative meteorological and cloud conditions. The science themes for the deployments include: 1) Budget of MBL CCN and its seasonal variation; 2) Effects of aerosol on cloud and precipitation; 3) Cloud microphysical and macrophysical structures, and entrainment mixing; 4) Advancing retrievals of turbulence, cloud, and drizzle; and 5) Model evaluation and processes studies. A key advantage of the deployments is the strong synergy between the measurements onboard the G-1 and the routine measurements at the ENA site, including state-of-the-art profiling and scanning radars. The 3D cloud structures provided by the scanning radars will put the detailed in situ measurements into mesoscale and cloud lifecycle contexts. On the other hand, high quality in situ measurements will enable validation and improvements of ground-based retrieval algorithms at the ENA site, leading to high-quality and statistically robust data sets from the routine measurements. The deployments, combined with the routine measurements at the ENA site, will have a long lasting impact on the research and modeling of low clouds and aerosols in the remote marine environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Pore-Scale Determination of Gas Relative Permeability in Hydrate-Bearing Sediments Using X-Ray Computed Micro-Tomography and Lattice Boltzmann Method

This work uses X-ray computed micro-tomography (μCT) to monitor xenon hydrate growth in asandpack under the excess gas condition. The μCT images give pore-scale hydrate distribution and porehabit in space and time. We use the lattice Boltzmann method to calculate gas relative permeability (k rg ) as a function of hydrate saturation (S hyd ) in the pore structure of the experimental hydrate-bearingsand retrieved from μCT data. The results suggest the krg-Shyd data fit well a new model krg=(1-S hyd )•exp(–4.95•S hyd ) rather than the simple Corey model. In addition, we calculate k rg -S hyd curves using digitalmodels of hydrate-bearing sand based on idealized grain-attaching, coarse pore-filling, and dispersed pore-filling hydrate habits. Our pore-scale measurements and modeling show that the k rg -S hyd curves are similar regardless of whether hydrate crystals develop grain-attaching or coarse pore-filling habits. The dispersed pore filling habit exhibits much lower gas relative permeability than the other two, but it is not observed in the experiment and not compatible with Ostwald ripening mechanisms. We find that a single grain-shape factor can be used in the Carman-Kozeny equation to calculate k rg -S hyd data with known porosity and average grain diameter, suggesting it is a useful model for hydrate-bearing sand.

58 GEOSCIENCES↗
DOE OSTI2021

Ultra-large-scale ab initio quantum chemical computation of bio-molecular systems: The case of spike protein of SARS-CoV-2 virus

The COVID-19 pandemic poses a severe threat to human health with an unprecedented social and economic disruption. Spike (S) glycoprotein of the SARS-CoV-2 virus is pivotal in understanding the virus anatomy, since it initiates the first contact with the ACE2 receptor in the human cell. We report results of ab initio computation of the spike protein, the largest ab initio quantum chemical computation to date on any bio-molecular system, using a divide and conquer strategy by focusing on individual structural domains. In this approach we divided the S-protein into seven structural domains: N-terminal domain (NTD), receptor binding domain (RBD), subdomain 1 (SD1), subdomain 2 (SD2), fusion peptide (FP), heptad repeat 1 with central helix (HR1-CH) and connector domain (CD). The entire Chain A has 14,488 atoms including the hydrogen atoms but excluding the amino acids with missing coordinates based on the PDB data (ID: 6VSB). The results include structural refinement, ab initio calculation of intramolecular bonding mechanism, 3- dimensional non-local inter-amino acid interaction with implications for the inter-domain interaction. Details of the electronic structure, interatomic bonding, partial charge distribution and the role played by hydrogen bond network are discussed. In the interaction among structural domains, we present new insights for crucial hinge-like movement and fusion process. Extension of such calculation to the interface between the S-protein binding domain and ACE2 receptor can provide a pathway for computational understanding of mutations and the design of therapeutic drugs to combat the COVID-19 pandemic.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Revealing the causes and temporal distribution of tree mortality in Central Amazonia

Tree mortality is a critical process in forest ecosystems, as it influences floristic composition, structure, dynamics, carbon storage, and forest nutrient cycling. However, the mechanisms behind tree death in tropical regions are still poorly characterized. This lack of information is mainly because tree mortality data come from long-interval inventories and studies that measured tree death seasonally are scarce. Here in this study, we offer novel insights into the power of fine temporal scale observations and we use a natural history approach to understand the processes and mechanism of tree mortality. We monitored tree mortality every month during one year, in 10 ha of terra-firme forest. To determine the pathways of mortality, we considered the state of the tree at the start of the investigation and the pre and post-mortem characteristics. From November 2010 to October 2011, 67 out of 5808 trees died. Despite the 2010 drought, mortality was highly correlated with monthly rainfall (r = 0.85). In total, six pathways of mortality were assessed. Storms were the main cause of mortality, killing 45% of all dead trees, followed by Biotic/abiotic factors accounting for 30% of tree mortality. The high mortality registered in the rainy season was mostly (78%) due to healthy trees dying uprooted or snapped. Lastly, we would benefit from studies that assess mortality on a monthly basis and in combination with quantitative long-term data, we can substantially improve our understanding of the mechanisms behind tree death in the tropics.

2010 drought↗
DOE OSTI2021

Deep learning for visualization and novelty detection in large X-ray diffraction datasets

Abstract We apply variational autoencoders (VAE) to X-ray diffraction (XRD) data analysis on both simulated and experimental thin-film data. We show that crystal structure representations learned by a VAE reveal latent information, such as the structural similarity of textured diffraction patterns. While other artificial intelligence (AI) agents are effective at classifying XRD data into known phases, a similarly conditioned VAE is uniquely effective at knowing what it doesn’t know: it can rapidly identify data outside the distribution it was trained on, such as novel phases and mixtures. These capabilities demonstrate that a VAE is a valuable AI agent for aiding materials discovery and understanding XRD measurements both ‘on-the-fly’ and during post hoc analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Striped Data Analysis Framework

A columnar data representation is known to be an efficient way for data storage, specifically in cases when the analysis is often done based only on a small fragment of the available data structures. A data representation like Apache Parquet is a step forward from a columnar representation, which splits data horizontally to allow for easy parallelization of data analysis. Based on the general idea of columnar data storage, working on the [LDRD Project], we have developed a striped data representation, which, we believe, is better suited to the needs of High Energy Physics data analysis. A traditional columnar approach allows for efficient data analysis of complex structures. While keeping all the benefits of columnar data representations, the striped mechanism goes further by enabling easy parallelization of computations without requiring special hardware. We will present an implementation and some performance characteristics of such a data representation mechanism using a distributed no-SQL database or a local file system, unified under the same API and data representation model. The representation is efficient and at the same time simple so that it allows for a common data model and APIs for wide range of underlying storage mechanisms such as distributed no-SQL databases and local file systems. Striped storage adopts Numpy arrays as its basic data representation format, which makes it easy and efficient to use in Python applications. The Striped Data Server is a web service, which allows to hide the server implementation details from the end user, easily exposes data to WAN users, and allows to utilize well known and developed data caching solutions to further increase data access efficiency. We are considering the Striped Data Server as the core of an enterprise scale data analysis platform for High Energy Physics and similar areas of data processing. We have been testing this architecture with a 2TB dataset from a CMS dark matter search and plan to expand it to multiple 100 TB or even PB scale. We will present the striped format, Striped Data Server architecture and performance test results.

Gutsche, Oliver↗
DOE OSTI2025

Towards an Introspective Dynamic Model of Globally Distributed Computing Infrastructures

Large-scale scientific collaborations like ATLAS, Belle II, CMS, DUNE, and others involve hundreds of research institutes and thousands of researchers spread across the globe. These experiments generate petabytes of data, with volumes soon expected to reach exabytes. Consequently, there is a growing need for computation, including structured data processing from raw data to consumer-ready derived data, extensive Monte Carlo simulation campaigns, and a wide range of end-user analysis. To manage these computational and storage demands, centralized workflow and data management systems are implemented. However, decisions regarding data placement and payload allocation are often made disjointly and via heuristic means. A significant obstacle in adopting more effective heuristic or AI-driven solutions is the absence of a quick and reliable introspective dynamic model to evaluate and refine alternative approaches. In this study, we aim to develop such an interactive system using real-world data. By examining job execution records from the PanDA workflow management system, we have pinpointed key performance indicators such as queuing time, error rate, and the extent of remote data access. The dataset includes five months of activity. Additionally, we are creating a generative AI model to simulate time series of payloads, which incorporate visible features like category, event count, and submitting group, as well as hidden features like the total computational load—derived from existing PanDA records and computing site capabilities. These hidden features, which are not visible to job allocators, whether heuristic or AI-driven, influence factors such as queuing times and data movement.

kilic, Ozgur Ozan [Brookhaven National Laboratory ↗