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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Network protection system using linkographs

A method and apparatus for managing an attack on a computer system. A computer identifies actions taken by an adversary in the computer system and links connecting the actions over time using an ontology defining linking rules for linking the actions over time. The computer creates a graph of the actions with the links connecting the actions over time. The graph shows a number of patterns of behavior for the adversary. The computer then identifies a protective action to take with respect to the computer system using the graph of the actions taken by the adversary.

Zage, David John↗
DOE OSTI2020

Phase I Closeout Report: Invoking Artificial Neural Networks to Measure Insider Threat Mitigation

Researchers from Sandia National Laboratories (Sandia) and the University of Texas at Austin (UT) conducted this study to explore the effectiveness of commercial artificial neural network (ANN) software to improve insider threat detection and mitigation (ITDM). This study hypothesized that ANNs could be "trainee to learn patterns of organizational behaviors, detect off-normal (or anomalous) deviations from these patterns, and alert when certain types, frequencies, or quantities of deviations emerge. The ReconaSense ANN system was installed at UT's Nuclear Engineering Teaching Laboratory (NETL) and collected 13,653 access control data points and 694 intrusion sensor data points over a three-month period. Preliminary analysis of this baseline data demonstrated regularized patterns of life in the facility, and that off-normal behaviors are detectable under certain situations -- even for a facility with anticipated highly non-routine, operational behaviors. Completion of this pilot study demonstrated how the ReconaSense ANN could be used to identify expected operational patterns and detect unexpected anomalous behaviors in support of a data-analytic approach to ITDM. While additional studies are needed to fully understand and characterize this system, the results of this initial study are overall very promising for demonstrating a new framework for ITDM utilizing ANNs and data analysis techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Texture and precipitation behavior of friction stir welded Inconel 825 alloy

The microstructure, texture pattern, and precipitation behavior of friction stir butt welded Inconel 825 alloy are highlighted here. The microstructure of bright regions and black-banded regions in the stir zone (SZ) is characterized by SEM, EBSD, and TEM with EDS. EBSD analysis justifies that the SZ exhibits a dynamic recrystallized structure. Grain boundary mapping corroborates the insignificant difference in the low- and high-angle grain boundaries fraction between the SZ, the thermo-mechanically affected zone, and the base metal. TEM analysis confirms the formation of nano twins and dispersion of nano-size precipitations in the grains, and at the grain boundaries as well. Grain refinement and nano-size precipitation formation in the SZ significantly enhanced both microhardness and tensile strength.

Das, Hrishikesh↗
DOE OSTI2022

Shifting temporal dynamics of human mobility in the United States

In this paper we analyze the average hourly temporal dynamics of human mobility in the United States from 2019 to 2020. We discuss how large decreases in human mobility nonuniformly effect the daily temporal dynamics of aggregate human behavior. The data used are weekly activity patterns for POIs from 2019 to 2020 in the United States, provided by SafeGraph and made openly available to academic and research institutions. We use clustering methods to create metrics describing how human activity changes throughout the day/week at the county and national levels. In response to significant mobility reductions starting March 2020, daily temporal patterns of human activity changed nonuniformly. Morning activity started later, and evening activity started earlier in 2020 compared to 2019, and temporal behavioral patterns on weekdays began to look more similar to weekends. The changes in daily temporal behavior persisted throughout the year even as total mobility levels recovered. The results provide insights on the changes in human behavior in response covid-19 policies and illustrate influences on social systems, health, and transportation networks.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Investigating the clinico-anatomical dissociation in the behavioral variant of Alzheimer disease

We previously found temporoparietal-predominant atrophy patterns in the behavioral variant of Alzheimer’s disease (bvAD), with relative sparing of frontal regions. Here, we aimed to understand the clinico-anatomical dissociation in bvAD based on alternative neuroimaging markers. We retrospectively included 150 participants, including 29 bvAD, 28 “typical” amnestic-predominant AD (tAD), 28 behavioral variant of frontotemporal dementia (bvFTD), and 65 cognitively normal participants. Patients with bvAD were compared with other diagnostic groups on glucose metabolism and metabolic connectivity measured by [ 18 F]FDG-PET, and on subcortical gray matter and white matter hyperintensity (WMH) volumes measured by MRI. A receiver-operating-characteristic-analysis was performed to determine the neuroimaging measures with highest diagnostic accuracy. bvAD and tAD showed predominant temporoparietal hypometabolism compared to controls, and did not differ in direct contrasts. However, overlaying statistical maps from contrasts between patients and controls revealed broader frontoinsular hypometabolism in bvAD than tAD, partially overlapping with bvFTD. bvAD showed greater anterior default mode network (DMN) involvement than tAD, mimicking bvFTD, and reduced connectivity of the posterior cingulate cortex with prefrontal regions. Analyses of WMH and subcortical volume showed closer resemblance of bvAD to tAD than to bvFTD, and larger amygdalar volumes in bvAD than tAD respectively. The top-3 discriminators for bvAD vs. bvFTD were FDG posterior-DMN-ratios (bvAD bvFTD, area under the curve [AUC] range 0.85–0.91, all p < 0.001). The top-3 for bvAD vs. tAD were amygdalar volume (bvAD>tAD), MRI anterior-DMN-ratios (bvAD<tAD), FDG anterior-DMN-ratios (bvAD<tAD, AUC range 0.71–0.84, all p < 0.05). Subtle frontoinsular hypometabolism and anterior DMN involvement may underlie the prominent behavioral phenotype in bvAD.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Baselining PMU Data to Find Patterns and Anomalies

This paper looks at the application of situational awareness methodologies with respect to power grid data. These methodologies establish baselines that look for typical patterns and atypical behavior in the data. The objectives of the baselining analyses are to provide: real-time analytics, the capability to look at historical trends and events, and reliable predictions of the near future state of the grid. Multivariate algorithms were created to establish normal baseline behavior and then score each moment in time according to its variance from the baseline. Detailed multivariate analytical techniques are described in this paper that produced ways to identify typical patterns and atypical behavior. In this case, atypical behavior is behavior that is unenvisioned. Visualizations were also produced to help explain the behavior that was identified mathematically. Examples are shown to help describe how to read and interpret the analyses and visualizations. Preliminary work has been performed on PMU data sets from BPA (Bonneville Power Administration) and EI (Eastern Interconnect). Actual results are not fully shown here because of confidentiality issues. Comparisons between atypical events found mathematically and actual events showed that many of the actual events are also atypical events; however there are many atypical events that do not correlate to any actual events. Additional work needs to be done to help classify the atypical events into actual events, so that the importance of the events can be better understood.

Baselining analyses↗
DOE OSTI2018

Size Effects of Micro-Pattern on Lithium Metal Surface on the Electrochemical Performance of Lithium Metal Secondary Batteries.

Two micro-patterns of different sizes (50 and 80 gm) are designed to have equivalent capacities of 1.06 and 2.44 mAh cm(-2) by building a computational battery model. After preparing two stamps each possessing a micro pattern design, the corresponding pattern is properly imprinted on the surface of 100 mu m lithium metal, which is confirmed by scanning electron microscopy. When both micro-patterned lithium metals are electrochemically reduced and oxidized up to 1 mAh cm(-2) in Li/Li symmetric cells at 1 or 2 mA cm(-2), the 80 mu m-patterned lithium shows a more stabilized lower overpotential during long-term cycling than the 50 mu m-patterned and bare lithium, probably due to the lithium anchoring effect and a larger empty volume in the patterns. Additionally, an overflow of lithium deposits is easily observed in the 50 mu m-patterned lithium metal, while the 80 mu m-patterned lithium metal holds most of the lithium deposits within the patterns. When both micro-patterned lithium metals are assembled to full cells with a LiNi0.6Co0.2Mn0.2O2 cathode of 2 mAh cm(-2), the 80 mu m-patterned lithium metal shows much better electrochemical performances with stable plating/stripping behavior within the patterns.

lithium metal↗
DOE OSTI2024

Thermal shock resistance of additively manufactured alumina

The mechanical behavior and cracking patterns of thermally-shocked additively manufactured alumina were investigated. The flexural strength of test specimens that had been heated to temperatures ranging from 200°C to 1000°C and then rapidly quenched in water was determined at ambient temperature by four-point bending. Results indicated that the surface cracking patterns had a multifractal structure and that an increase in the thermal shock temperature led to an increase in the density and uniformity of the crack network. Further, the flexural strength results were analyzed with Weibull statistics, where the Weibull moduli for most of the thermal shock conditions tested were found to be statistically indistinguishable. It was also found that a significant decrease (~50%) in flexural strength occurred for heating temperatures ≥300°C. The effect of the manufacturing method on cracking patterns is discussed, as well as the implication of the material behavior for practical applications of these materials.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Directing the phase behavior of polyelectrolyte complexes using chiral patterned peptides

Polyelectrolyte complexes (PECs) have a broad range of promising applications as soft materials due to their self-assembly and diversity of structure and chemical composition. Peptide polymer PECs are highly biocompatible and biodegradable, making them particularly useful for encapsulation of food additives and flavors, micellar drug delivery, medical and underwater adhesives, fetal membrane patches, and scaffolds for cell growth in tissue engineering. While parameters affecting PEC formation and stability in regards to charge effects are well researched, little is known about the effects of van der Waals interactions, hydrogen bonding, and secondary structure in these materials. Peptide chirality provides a unique opportunity to manipulate PEC phase to modulate the amount of solid-like (precipitate) or liquid-like (coacervate) character by influencing hydrogen bonding interactions among peptide chains. In previous work, we showed that chiral peptides form solid complexes, while complexes with even one racemic peptide were fluid. This raised the interesting question of how long a homochiral sequence must be to result in solid phase formation. In this work, we designed chiral patterned peptides of polyglutamic acid and polylysine ranging from 50 to 90% L-chiral residues with increasing numbers of sequential L-chiral residues before a chirality change. These polymers were mixed together to form PECs. We observed that 8 or more sequential L-chiral residues are necessary to achieve both the appearance of a precipitate phase and sustained beta-sheets in the complex, as determined by optical imaging and FTIR Spectroscopy. Less homochiral content results in formation of a coacervate phase. Thus, we show that chiral sequence can be used to control the phase transition of PECs. Understanding how to manipulate PEC phase using chiral sequence as presented here may enable tuning of the material properties to achieve the desired mechanical strength for coatings and polymer brushes, or the most effective molecular release kinetics for drug delivery applications, for example.

Pacalin, Naomi M.↗
DOE OSTI2023

Modeling spatial patterns of longleaf pine needle dispersal using long-term data

Abstract Background Predicting patterns of fire behavior and effects in frequent fire forests relies on an understanding of fine-scale spatial patterns of available fuels. Leaf litter is a significant canopy-derived fine fuel in fire-maintained forests. Litter dispersal is dependent on foliage production, stand structure, and wind direction, but the relative importance of these factors is unknown. Results Using a 10-year litterfall dataset collected within eighteen 4-ha longleaf pine ( Pinus palustris Mill.) plots varying in canopy spatial pattern, we compared four spatially explicit models of annual needle litter dispersal: a model based only on basal area, an overstory abundance index (OAI) model, both isotropic and anisotropic litter kernel models, and a null model that assumed no spatial relationship. The best model was the anisotropic model (R 2 = 0.656) that incorporated tree size, location, and prevailing wind direction, followed by the isotropic model (R 2 = 0.612), basal area model (R 2 = 0.488), OAI model (R 2 = 0.416), and the null model (R 2 = 0.08). Conclusions As with previous studies, the predictive capability of the litter models was robust when internally validated with a subset of the original dataset (R 2 = 0.196–0.549); however, the models were less robust when challenged with an independent dataset (R 2 = 0.122–0.319) from novel forest stands. Our model validation underscores the need for rigorous tests with independent, external datasets to confirm the validity of litter dispersal models. These models can be used in the application of prescribed fire to estimate fuel distribution and loading, as well as aid in the fine tuning of fire behavior models to better understand fire outcomes across a range of forest canopy structures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Experimental and 3D Micromechanical Analysis of Stress–Strain Behavior and Damage Initiation in Dual-Phase Steels

In the recent decade, dual-phase steel has been used extensively in the industry due to its high strength and formability. In this paper, deformation pattern and mechanical behavior of DP steel are predicted using the 2D and 3D representative volume element (RVE) and finite elements method. Also, the obtained results were compared with the experimental SEM images. The experiments are performed in three stages to obtain the stress–strain curve and failure pattern in the specimens with the different state of stress distribution at the test sections. In the 3D state of stress, the state of applied stress to the RVE is obtained from macro-modeling of the test specimens and this model was analyzed using the finite element method. Furthermore, the effects of volume fraction and mesh size in 3D simulation on the stress–strain behavior are investigated. Metallography and SEM images are also used to access the failure mechanism at the microscale. It is shown that the martensite particles remain almost in their original form and their deformations are very small for the specimens with the thickness-to-width ratio of 0.8 (3D state of stress). This phenomenon is completely different from those obtained for smaller thickness-to-width ratio that the martensite particles were pulled and its length was increased. On the other hand, SEM images show that voids are located in the phase boundaries. According to the experimental and numerical results, although the main factor in the formation and growth of 2D voids is shear stress, in the 3D state of stress, the voids grow in spherical form due to the existence of large hydrostatic stresses.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The variation in self-organized anode plasma pattern structure with solution electrolyte type in 1 atm DC glow discharge

Abstract Plasma self-organization on anode surfaces in 1 atm DC glow discharges remains poorly understood. This effort aims to elucidate the nature of self-organization through the experimental study of resulting patterns on liquid anode surfaces with 13 different electrolytes and thus improves our understanding of the underlying physical processes that give rise to self-organization by investigating electrolyte sensitivity. Self-organization pattern formation and behavior were studied as a function of discharge current, solution ionic strength, and their chemical property evaluation. The response of the patterns to variation in these parameters was measured using an imaging camera and optical emission spectroscopy. Observed pattern characteristic length scales for all of the electrolytes were ranged from 2 to 13 mm and typically increased with current over the investigated range of 20–80 mA. Complex self-organized pattern structures not reported to date were also observed. The parameters associated with pattern formation and morphology complexity are discussed and summarized.

Physics↗
DOE OSTI2021

Parallel I/O Evaluation Techniques and Emerging HPC Workloads: A Perspective

Emerging workloads such as artificial intelligence, big data analytics and complex multi-step workflows alongside future exascale applications are anticipated future HPC workloads, which will result in a more diverse I/O system workload and even less predictable I/O behavior and access patterns. Along with the ever increasing gap between the compute and storage performance capabilities, the in-depth understanding of extreme-scale I/O behavior and the I/O performance modeling and prediction are essential tools of the large-scale I/O evaluation process for addressing the needs of extreme-scale hybrid workloads. In this survey article, we focus on the state-of-the-art of the I/O behavior and performance analysis process for HPC systems in a 5-year time window and identify future research challenges.

Neuwirth, Sarah↗
DOE OSTI2016

Characteristics and Travel Patterns of New York Residents: Subpopulations of Persons with a Disability in 2009

In this study, Oak Ridge National Laboratory (ORNL) was tasked by the NYS Department of Transportation (DOT) to conduct a detailed examination of travel behaviors, and identify patterns and trends, on several NYS subpopulations, including disabled persons. Unlike other studies that concentrated on national level statistics, this research is focused on examining issues associated with travelers among NYS residents only. For each special subpopulation group, ORNL will identify differences, if any, in travel patterns that are attributable to demographic characteristics, household characteristics, modal characteristics, geographic location, and other concepts. Focus will be given to trip frequency, trip chaining, as well as travel by time of day, trip purpose, and mode choice.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Complex collective dynamics of active torque-driven colloids at interfaces

Modern self-assembly techniques aiming to produce complex structural order or functional diversity often rely on non-equilibrium conditions in the system. Light, electric, or magnetic fields are predominantly used to modify interaction profiles of colloidal particles during self-assembly or induce complex out-of-equilibrium dynamic ordering. The energy injection rate, properties of the environment are important control parameters that influence the outcome of active (dynamic) self-assembly. The current review is focused on a case of collective dynamics and self-assembly of particles with externally driven torques coupled to a liquid or solid interface. The complexity of interactions in such systems is further enriched by strong hydrodynamic coupling between particles. Unconventionally ordered dynamic self-assembled patterns, spontaneous symmetry breaking phenomena, self-propulsion, and collective transport have been reported in torque-driven colloids. Some of the features of the complex collective behavior and dynamic pattern formation in those active systems have been successfully captured in simulations.

Snezhko, Alexey↗
DOE OSTI2019

Combining Tensor Decompositions and Graph Analytics to Provide Cyber Situational Awareness at HPC Scale

This paper describes MADHAT (Multidimensional Anomaly Detection fusing HPC, Analytics, and Tensors), an integrated workflow that demonstrates the applicability of HPC resources to the problem of maintaining cyber situational awareness. MADHAT combines two high-performance packages: ENSIGN for large-scale sparse tensor decompositions and HAGGLE for graph analytics. Tensor decompositions isolate coherent patterns of network behavior in ways that common clustering methods based on distance metrics cannot. Parallelized graph analysis then uses directed queries on a representation that combines the elements of identified patterns with other available information (such as additional log fields, domain knowledge, network topology, whitelists and blacklists, prior feedback, and published alerts) to confirm or reject a threat hypothesis, collect context, and raise alerts. MADHAT was developed using the collaborative HPC Architecture for Cyber Situational Awareness (HACSAW) research environment and evaluated on structured network sensor logs collected from Defense Research and Engineering Network (DREN) sites using HPC resources at the U.S. Army Engineer Research and Development Center DoD Supercomputing Resource Center (ERDC DSRC). To date, MADHAT has analyzed logs with over 650 million entries.

Cyber security, Tensor decomposition, graph analyt↗
DOE OSTI2025

Exploring the Utility-Privacy Trade-Off: Impacts of Semantic and Visit Types Ambiguities on Human Mobility Simulation

Humans are in perpetual movement, constantly traversing buildings, cities, waters, oceans, and countries. Mobility stands out as a major driving force shaping our modern societies. Capturing and explaining human behavior in a world of eight billion distinct mobility agendas is a complex challenge. With the rise of interconnected devices and platforms, such as smartphones, wearables, and point-of-interest data, largescale behavioral data has become more accessible, enabling rich insights into mobility patterns. However, the widespread availability of such data introduces significant ethical challenges. Detailed mobility data can inadvertently reveal sensitive personal information, including individuals' locations, habits, social interactions, and even political or religious affiliations. Beyond privacy breaches, the ethical implications of uncovering and potentially manipulating underlying behavioral patterns demand attention. Striking a balance between the utility of mobility models and the protection of individual privacy is therefore paramount. This paper explores the utility-privacy trade-offs in human mobility modeling, focusing on the impacts of introducing semantic and visit type ambiguities. By systematically examining how these ambiguities affect the fidelity of simulated trajectories and privacy risks, we provide a framework for evaluating ethical and privacy-conscious modeling practices. Our findings emphasize the need for methods that safeguard privacy without undermining the usefulness of mobility models, contributing to the responsible advancement of mobility science in alignment with ethical standards and societal expectations.

Amichi, Licia [ORNL] (ORCID:0000000177631394)↗