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Results for “behavioral pattern”

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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Crushing behaviors of unidirectional carbon fiber reinforced plastic composites under dynamic bending and axial crushing loading

The dynamic crushing characteristics of unidirectional carbon fiber reinforced plastic composites under two loading types, dynamic three-point bending and axial crushing, are investigated by experiment and finite element simulation. Hat section samples with two different layup orientations are tested at various impact velocities to investigate its effects. The experiment results show that delamination plays a critical role in dynamic bending deformation. Different layup orientations lead to similar crushing bending behaviors, but remarkable variations in delamination pattern evolutions. In contrast, layup orientation exerts more significant effects on failure modes and energy absorption in axial crushing. A finite element model with multiple layers of thick shell element and cohesive element is established, and simulations are performed for both dynamic three-point bending and axial crushing. The model can capture the crushing behaviors in the two loading types reasonably well. The damage evolution and energy absorption through various mechanisms at different loading conditions are discussed.

42 ENGINEERING↗
DOE OSTI2013

Quantum ground state effect on fluctuation rates in nano-patterned superconducting structures

In this Letter, we present a theoretical model with experimental verifications to describe the abnormal behaviors of the measured fluctuation rates occurring in nano-patterned superconducting structures below the critical temperature. In the majority of previous works, it is common to describe the fluctuation rate by defining a fixed ground state or initial state level for the singularities (vortex or vortex-antivortex pairs), and then employing the well-known rate equations to calculate the liberation rates. Although this approach gives an acceptable qualitative picture, without utilizing free parameters, all the models have been inadequate in describing the temperature dependence of the rate for a fixed width or the width dependence of the rate for a fixed temperature. Here, we will show that by defining a current-controlled ground state level for both the vortex and vortex-antivortex liberation mechanisms, the dynamics of these singularities are described for a wide range of temperatures and widths. According to this study, for a typical strip width, not only is the vortex-antivortex liberation higher than the predicted rate, but also quantum tunneling is significant in certain conditions and can not be neglected.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Microseismic at HFTS2: A Story of Three Stimulated Wells

A microseismic data set was acquired at Hydraulic Fracturing Test Site 2 (HFTS2) in the Delaware Basin (Texas, USA) by the HFTS2 consortium as a part of data-collection program designed to help maximize the efficiency of hydraulic fracturing, understand the impacts of nearby depleted zones, and increase the production of hydrocarbons. Advanced methods of microseismic monitoring applied at HFTS2 include 1) native synchronization of five monitor arrays, 2) real-time location of microseismic events, 3) joint event hypocenter/ velocity-model inversion that locates microseismic events simultaneously with constructing azimuthally anisotropic velocity models, 4) high-resolution reservoir imaging, utilizing microseismic events as downhole sources. HFTS2 microseismic data set illustrated the influence of geologic complexity, variations in treatment designs, and the presence of parent wells on the efficiency of hydraulic fracturing. Three Wolfcamp wells, Boxwood 1H, 2H, and 4H, were stimulated sequentially, exhibiting very different patterns of microseismicity. When the Boxwood 1H well is stimulated, its toe stages quickly reacted to the presence of depleted Bitterroot section to the east of the Boxwood pad, extending microseismic events over 3,000 ft to the east. This behavior changes dramatically after the treatment passes the Bitterroot section at the toe, and the heel stages in 1H showed tight microseismic grouping around the stimulated well, indicative of treatment of virgin reservoir rock. The Boxwood 2H demonstrates this same pattern of behavior between the toe and heel stages of the well, suggesting that the depleted Bitterroot section remains easier to refracture than adjacent non-depleted rock. Microseismic activity associated with Boxwood 4H treatment is distinctly different from those in Boxwood wells 1H and 2H. The events generated during stimulation of 4H are hardly influenced by the depleted Bitterroot section and fill the space between the previously stimulated 1H and 2H wells. Altogether, microseismic patterns suggest that treatment fluids tend to find the weakest passages through geologic formations, and the relative well positions and treatment sequence have a significant impact on fracture geometry, as indicated by microseismic event locations.

58 GEOSCIENCES↗
DOE OSTI2023

Generating Mixed Patterns of Residential Segregation: An Evolutionary Approach

The Schelling model of residential segregation has demonstrated that even the slightest preference for neighbors of the same race can be amplified into community-wide segregation. However, these models are unable to simulate mixed, coexisting patterns of segregation and integration, which have been seen to exist in cities. Using evolutionary model discovery we demonstrate how including social factors beyond racial bias when modeling relocation behavior enables the emergence of strongly mixed patterns. Our results indicate that the emergence of mixed patterns is better explained by multiple factors influencing the decision to relocate; the most important being the interaction of nonlinear, rapidly diminishing racial bias with a recent, historical tendency to move. Additionally, preference for less isolated neighborhoods or preference for neighborhoods with longer residing neighbors may produce weaker mixed patterns. Finally, this work highlights the importance of exploring the influence of multiple hypothesized factors of decision making, and their interactions, within agent rules, when studying emergent outcomes generated by agent-based models of complex social systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Diagnostic indicators for integrated assessment models of climate policy

Integrated assessments of how climate policy interacts with energy-economic systems can be performed by a variety of models with different functional structures. This article proposes a diagnostic scheme that can be applied to a wide range of integrated assessment models to classify differences among models based on their carbon price responses. Model diagnostics can uncover patterns and provide insights into why, under a given scenario, certain types of models behave in observed ways. Such insights are informative since model behavior can have a significant impact on projections of climate change mitigation costs and other policy-relevant information. The authors propose diagnostic indicators to characterize model responses to carbon price signals and test these in a diagnostic study with 11 global models. Indicators describe the magnitude of emission abatement and the associated costs relative to a harmonized baseline, the relative changes in carbon intensity and energy intensity and the extent of transformation in the energy system. This study shows a correlation among indicators suggesting that models can be classified into groups based on common patterns of behavior in response to carbon pricing. Such a classification can help to more easily explain variations among policy-relevant model results.

Climate change economics↗
DOE OSTI2014

Structural and magnetic behavior of the cubic oxyfluoride SrFeO{sub 2}F studied by neutron diffraction

The oxyfluoride SrFeO{sub 2}F has been prepared via a low temperature route involving the infinite-layer SrFeO{sub 2} and XeF{sub 2}. SrFeO{sub 2}F crystallizes in the cubic space group Pm-3m with disordered oxygen and fluorine atoms on the anion site. Recent reports demonstrated that SrFeO{sub 2}F is antiferromagnetic at room temperature and the zero field cooled and field cooled curves diverge at ∼150 K and ∼60 K, suggesting that the material has a spin glassy magnetic state at low temperatures. In this article, variable-temperature neutron diffraction (4–723 K) was performed to clarify the magnetic behavior observed in this material. Neutron powder diffraction measurements confirmed the antiferromagnetic (AFM) ordering of the system at room temperature. Below 710(1) K, the magnetic structure is a G-type AFM structure characterized by a propagation vector k=(1/2 , 1/2 , 1/2 ). The ordered moments on Fe{sup 3+} are 4.35(6)µ{sub B} at 4 K and 4.04(5)µ{sub B} at 290 K. Our results indicate that the cubic structure is retained all the way to base temperature (4 K) in contrast to PbFeO{sub 2}F. These results are compared with those of Pb and Ba analogs which exhibit very similar magnetic behavior. Furthermore, the observation of magnetic reflections at 4 K in the diffraction pattern shows the absence of the previously proposed spin glassy behavior at low temperatures. Previous proposals to explain the ZFC/FC divergences are examined. - Graphical abstract: Variable temperature powder neutron diffraction was employed to follow the evolution of the long range antiferromagnetic state in SrFeO{sub 2}F. - Highlights: • SrFeO{sub 2}F prepared via low temperature route involving SrFeO{sub 2} and XeF{sub 2}. • The cubic structure, Pm-3m, is retained at low temperatures, 4 K. • The magnetic structure is G-type AFM with T{sub N}=710 K and Fe{sup 3+} moment of 4.35µ{sub B}. • A small volume, bulk decoupled, spin glassy domain/cluster mechanism is proposed. • Highest AFM ordering temperature compared to Pb and Ba analogs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Local Ferromagnetic Resonance Measurements of Mesoscopically Patterned Ferromagnets Using Deterministically Placed Nanodiamonds

Nitrogen-vacancy centers in diamond have recently been established as effective sensors of the magnetization dynamics in vicinal ferromagnetic materials. Here, we demonstrate sub-100-nm placement accuracy of nitrogen-vacancy-containing nanodiamonds and use these as local sensors that probe optically detected ferromagnetic resonance in mesoscopically patterned Permalloy islands. These measurements reveal variations in the ferromagnetic resonance signal at different sites on these structures with distinct behavior in the edge and the bulk of patterned features. These test measurements establish an easily implemented approach for spatially targeted measurements of spin dynamics in mesoscale ferromagnets. In principle, the methodology can also be extended to local studies of nanoscale ferromagnets, such as single magnetic nanowires and nanoparticles.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2019

Big Hill 2018 InSAR Analysis U.S. Strategic Petroleum Reserve

The historical subsidence surveys shot over the U.S. Strategic Petroleum Reserve Big Hill site, located in southeastern Texas, have indicated surface uplift since 2002. In order to better understand and substantiate the surface behavior inferred from annual elevation measurements, InSAR (interferometric synthetic aperture radar) data was acquired. InSAR involves the processing of multiple satellite synthetic aperture radar scenes acquired across the same location of the Earth's surface at different times to map surface deformation. The analysis of the data can detect millimeters of motion spanning days, months, year and decades, across specific sites. The InSAR analysis indicates the fastest subsidence rates are over the north central region of the site, specifically centered over caverns 104 and 103. Subsidence rates decrease towards both the west and east, with the western side subsiding at greater rate than the eastern edge. There is some uplift noted, off the site and off the dome to the east. Overall, the subsidence pattern is in line with subsidence behavior expected over a cavern field. In investigating the validity of the uplift measured during the ground surveys it was discovered that reference location can impact results. An exercise was conducted that took the current InSAR data and presented two varying results dependent on the reference location, either on or off the dome. The conclusion was that if the reference is located on the dome, as it has been for years for the ground surveys, the reference location is moving too, giving the appearance of uplift.

02 PETROLEUM↗
DOE OSTI2017

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

This work is the first to take advantage of recurrent neural networks to predict influenza-like-illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data [1, 2] and the state-of-the-art machine learning models [3, 4], we build and evaluate the predictive power of Long Short Term Memory (LSTMs) architectures capable of nowcasting (predicting in \real-time") and forecasting (predicting the future) ILI dynamics in the 2011 { 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, stylistic and syntactic patterns, emotions and opinions, and communication behavior. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks. Finally, we combine ILI and social media signals to build joint neural network models for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance [1], specifically for military rather than general populations [3] in 26 U.S. and six international locations. Our approach demonstrates several advantages: (a) Neural network models learned from social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than syntactic and stylistic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Microscopic piezoelectric behavior of clamped and membrane (001) PMN-30PT thin films

Bulk single-crystal relaxor-ferroelectrics, like Pb(Mg 1/3 Nb 2/3 )O- 3 -PbTiO 3 (PMN-PT), are widely known for their large piezoelectricity. This is attributed to polarization rotation, which is facilitated by the presence of various crystal symmetries for compositions near a morphotropic phase boundary. Relaxor-ferroelectric thin films, which are necessary for low-voltage applications, suffer a reduction in their piezoelectric response due to clamping by the passive substrate. To understand the microscopic behavior of this adverse phenomenon, we employ the AC electric field driven in-operando synchrotron x-ray diffraction on patterned device structures to investigate the piezoelectric domain behavior under an electric field for both a clamped (001) PMN-PT thin film on Si and a (001) PMN-PT membrane released from its substrate. In the clamped film, the substrate inhibits the field-induced rhombohedral (R) to tetragonal (T) phase transition resulting in a reversible R to Monoclinic (M) transition with a reduced longitudinal piezoelectric coefficient d 33 < 100 pm/V. Releasing the film from the substrate results in recovery of the R to T transition and results in a d 33 > 1000 pm/V. Here, using diffraction with spatial mapping, we find that lateral constraints imposed by the boundary between the active and inactive materials also inhibit the R to T transition. Phase-field calculations on both clamped and released PMN-PT thin films simulate our experimental findings. Resolving the suppression of thin film piezoelectric response is critical to their application in piezo-driven technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Investigating the Impact of Temporal and Directional Traffic Distribution on Crash Frequencies

Safety Performance Functions (SPFs) are mathematical models that establish relationships between the frequency of various crash types and site-specific characteristics, serving as essential tools for traffic safety analysis and roadway design. Traditional SPFs, however, often overlook the temporal fluctuations in traffic flow (such as peak-hour surges) and directional imbalances between opposing traffic streams. These traffic patterns can exacerbate congestion, disrupt driver behavior, and create unexpected conflict points, potentially leading to increased crash frequencies and more severe accidents. In light of this gap, this study aims to explore the potential of incorporating K-factors (representing peak-hour traffic proportions) and D-factors (reflecting the imbalance of directional traffic) into the development of SPFs to assess whether these factors can effectively represent the impact of temporal and spatial traffic distribution on roadway safety. Using crash data from Pennsylvania urban-suburban collector roadways, it is found that the D-factor plays a significant role in predicting the frequency of total crashes, fatal + injury crashes, and angle crashes, with positive coefficient signs indicating that higher directional imbalances correspond to increased crash risks. Similarly, the K-factor emerges as a critical predictor for fatal + injury crashes and rear-end crashes, with negative coefficients suggesting that a more pronounced traffic peak is associated with a reduction in expected crash frequencies. These results highlight the importance of accounting for uneven traffic distribution in both time and direction when developing SPFs, offering deeper insights into crash patterns and supporting more effective safety interventions and roadway designs.

Xu, Guanhao [ORNL] (ORCID:0000000214326357)↗
DOE OSTI2025

Laser-induced selective local patterning of vanadium oxide phases

The same elements can form different compounds with widely different physical properties. Synthesis of a single-phase material is commonly achieved by controlling experimental conditions. Synthesizing materials that incorporate multiple specific spatially distributed chemical phases is often challenging, especially if different phases must be organized into well-defined spatial patterns. Here, we present an efficient solid reaction laser annealing (SRLA) approach to directly write regions of different local chemical compositions. We demonstrate the practical utility of our approach by locally writing microscale patterns of distinct chemical phases in vanadium oxide thin films. Specifically, we achieved the controlled local recrystallization of a uniform V 2 O 3 matrix into VO 2 , V 3 O 5 , and V 4 O 7 regions exhibiting sharp 1st- and 2nd-order metal–insulator phase transitions over a wide range of critical temperatures, i.e., a characteristic feature of select vanadium oxides that is extremely sensitive to even minute structural or compositional imperfections. We utilized the local chemical phase writing to pattern spiking oscillators with distinct electrical behavior directly in the thin film sample without employing elaborate lithography fabrication. Our laser tuning local chemical composition opens a pathway to synthesize a wide range of artificially micropatterned composite materials, with precision and control unattainable in conventional material synthesis methods.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Extrusion‐Based Printing of Nanostructured Fatty Acid Gels Incorporated in Hydrogels

Soft materials with unique nanostructures such as lamellar, hexagonal, and cubic morphologies can replicate complex structures that have potential in various fields, including biomedical and industrial applications. However, a key challenge in advancing the broader applications of 3D printing for these nanostructured soft materials is insufficient mechanical properties that hinder their printability and compromise structural stability in the final product. In this study, the suitability of a fatty acid‐based lamellar gel is evaluated for direct extrusion‐based 3D printing. Here, the lamellar gel with varying water content is integrated with a photocurable hydrogel to preserve the shape and stability of the final prints. Complex 2D and 3D design patterns are used to assess extrusion behavior, structural stability, and print precision under varying pressures. Small‐angle X‐ray Scattering (SAXS) measurements reveal the formation of lamellar nanostructures and confirm their retention after photocuring in various gels. Rheological analysis confirms that these gels exhibit key properties suitable for extrusion‐based 3D printing, such as shear‐thinning behavior. Additionally, tensile testing is conducted to evaluate the mechanical properties across cured print samples. This study underscores the potential of nanostructured gels as a robust and versatile platform, facilitating the development of materials engineered for various applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Real-time tracking and analysis of gas bubble dynamics in laser powder bed fusion using in-situ X-ray characterization and machine learning

Porosity defects remain a significant challenge in the laser powder bed fusion (LPBF) process, adversely affecting the mechanical properties and reliability of additively manufactured components. Here, this study investigates the real-time formation and trajectory of gas bubbles during LPBF of Al6061 alloy using advanced in-situ X-ray characterization and machine learning. The unsupervised Gaussian mixture model and particle tracking algorithm developed are able to precisely track and quantify the properties of gas bubbles and keyhole pores. Our analysis identified five distinct types of gas bubble formation and movement patterns, emphasizing the diverse origins and behaviors of these defects. It enables precise quantification of trajectories, velocities, and morphological changes of gas bubbles, offering a granular view of the subsurface dynamics within the melt pool. Additionally, we explored keyhole-induced pore dynamics, revealing the critical role of keyhole oscillation and collapse for the formation of both large and small gas pores. It defines four different regions of gas bubble movement within the melt pool, providing a clearer understanding of how local fluid dynamics affect pore behavior. The results underscore the importance of integrating in-situ experimental observation and automated machine learning to develop a more robust predictive model for defect formation in LPBF.

In-situ X-ray imaging↗
DOE OSTI2020

Hybrid geological modeling: Combining machine learning and multiple-point statistics

Accurately modeling and constructing a geologically realistic subsurface model remains an outstanding problem as the morphology controls the flow behaviors. Particularly, one of the pattern-based methods, namely cross-correlation based simulation, has been proved to be an effective way to reconstruct a realistic model, at both small and large scales. However, conditioning to point data in the large-scale problems is still a crucial issue in these algorithms, since there is always a trade-off between the quality of the realizations and the degree of point data reproduction. Specifically, it is not practical to build a training image (TI) which includes all the possibilities and variabilities. Therefore, finding a pattern that can represent the point data and, at the same time, preserving the connectivities is difficult. This leads to producing highly-connected realizations with a significant mismatch or poor models with a reasonable degree of point data reproduction. To accurately reproduce the densely distributed hard data, pixel-based methods can also produce some unrealistic artifacts around the hard data. In this paper, to overcome this challenge, however, we use pattern-based methods as they often produce more disconnected geobodies when dealing with dense hard data, and proposed a hybrid algorithm using the pattern-based methods and convolutional neural network (CNN). The trained CNN model is utilized to improve the quality of conditioning to point data for the original realizations generated by the pattern-based algorithm. As such, the mismatch locations are identified, and the same regions are used in the training of CNN to mimic the procedure through which a missing region can be filled. To evaluate the performance of the proposed hybrid algorithm, it is tested on cases with different dimensions and different numbers of facies. Then, the newly improved realizations are compared with the initial realizations generated by the pattern-based algorithm. The comparison is also conducted by the flow simulation test. And it indicates that the proposed hybrid algorithm can better reproduce the point data, while the connectivities are better preserved.

58 GEOSCIENCES↗
DOE OSTI2019

In-situ study of the carbon gasification reaction of highly oriented pyrolytic graphite promoted by cobalt oxides and the novel nanostructures appeared after reaction

Cobalt interaction and its effects on carbon-based systems at the nanoscale have recently attracted much attention in different fields, such as catalysis of carbon nanotubes or graphene and graphite nano-patterning taking advantage of its ferromagnetic behavior. Experiments performed in our laboratories show how the re-oxidation process of two equivalent monolayers of CoO deposited on highly oriented pyrolytic graphite at 400 °C leads to the formation of nanochannels at lower temperature than using other methods. Here we present the in-situ characterization of the carbon gasification reaction that drives this process by means of near ambient pressure X-ray photoelectron spectroscopy performed at the ALBA synchrotron facility. The reason why this reaction takes place at such low temperature compared to other methods is due to the weakening of the carbon σ bonds by the initial CoO wetting layer formed at the early stages of growth on the graphite surface. Besides nanochannels, ex-situ atomic force microscopy measurements also show the appearance of two more kinds of nanostructures: nano-strips and nano-rings. The appearance of these nanostructures reveals the impressive modification of the surface after the re-oxidation process mediated by the cobalt oxide.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Polyampholyte physics: Liquid–liquid phase separation and biological condensates

Here we review the current understanding of molecular interactions that govern liquid-liquid phase separation (LLPS) of biological condensates. The connection between sequence, chain conformation, and phase separation of intrinsically disordered proteins (IDPs) and their model polyampholytes is discussed. In particular, we highlight how the charge pattern influences the conformation and phase behavior of natural IDPs. We then describe recent results from theoretical treatments of polyampholytes implementing random phase approximation, field-theoretic simulations, and transfer matrix theory that show an increase in charge segregation results in an increased tendency to phase separate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

The importance of stress percolation patterns in rocks and other polycrystalline materials

A new framework for thinking about the deformation behavior of rocks and other heterogeneous polycrystalline materials is proposed, based on understanding the patterns of stress transmission through these materials. Here, using finite element models, I show that stress percolates through polycrystalline materials that have heterogeneous elastic and plastic properties of the same order as those found in rocks. The pattern of stress percolation is related to the degree of heterogeneity in and statistical distribution of the elastic and plastic properties of the constituent grains in the aggregate. The development of these stress patterns leads directly to shear localization, and their existence provides insight into the formation of rhythmic features such as compositional banding and foliation in rocks that are reacting or dissolving while being deformed. In addition, this framework provides a foundation for understanding and predicting the macroscopic rheology of polycrystalline materials based on single-crystal elastic and plastic mechanical properties.

58 GEOSCIENCES↗