Additive Manufacturing of Ta-W Alloys: A Gradient Approach to Microstructure and Cracking Behavior
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Materials science↗
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Results for “behavioral sciences”
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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Think before you throw! An analysis of behavioral interventions targeting PET bottle recycling in the United States
The United States generates 42 Mt of plastic waste each year and is one of the biggest contributors to ocean plastic waste. Consequently, plastic has become synonymous with the linear economy, and many scholars are studying and proposing circular economy solutions to mitigate plastic pollution. Recycling has received much attention from both social sciences and engineering as a circular economy strategy, but no study has yet quantified how behavioral interventions could asymmetrically affect different populations. Here, this study combines agent-based modeling, material flow analysis, system dynamics, and life cycle assessment to assess the effect of four behavioral interventions on the collection rates of polyethylene terephthalate bottle waste, displaced virgin plastic manufacturing, and avoided greenhouse gas (GHG) emissions. Results show that, while behavioral interventions would require about 300–900 GJ of additional energy at end-of-life due to improved collection rates, they would avoid about 500–700 thousand metric tons of GHG emissions. Results also illustrate the importance of habits in disposal behaviors and show that different forms of interventions can be better adapted to particular social contexts than others. While the circular economy and its application to plastic waste should certainly not be restricted to recycling, this study demonstrates that improved collection rates and recycling technologies can contribute to reducing the amount of plastic waste polluting our oceans.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Past agricultural land use affects multiple facets of ungulate antipredator behavior
Antipredator behavior affects prey fitness, prey demography, and the strength of ecological interactions. Although predator-prey interactions increasingly occur in habitats that experience multiple forms of human-generated disturbance, it is unclear how different forms of disturbance might affect antipredator behavior. Fire is a contemporary disturbance that has dramatic effects on terrestrial habitats. Such habitats may have also experienced past disturbances, like agricultural land use, that leave lasting legacies on habitat structure (e.g., overstory and understory composition). It is unclear how these past and present disturbances affect the use of different antipredator behaviors, like temporal avoidance and vigilance. We examined whether variation in disturbance regimes generates differences in ungulate antipredator behavior by using cameras to measure white-tailed deer vigilance and activity time across 24 longleaf pine woodlands that vary in past land use and contemporary fire regime. Regardless of land-use history, woodlands with high fire frequencies had 4 times less vegetation cover than low-fire woodlands, generating riskier habitats for deer; however, deer responded to fire with different antipredator strategies depending on land-use history. In nonagricultural woodlands, fire affected deer activity time such that activity was nocturnal in low-fire woodlands and crepuscular in high-fire woodlands. In post-agricultural woodlands, fire affected vigilance and not activity time such that deer were more vigilant in high-fire woodlands than in low-fire woodlands. Furthermore, these results suggest that ungulate antipredator behavior may vary spatially depending on past land use and contemporary fire regime, and such disturbances may generate “landscapes of fear” that persist for decades after agricultural use.
60 APPLIED LIFE SCIENCES↗
Mining and Validating Social Media Data for COVID-19–Related Human Behaviors Between January and July 2020: Infodemiology Study
Background: Health authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging, is also important for minimizing long-term effects of an outbreak. Objective: We used social media data from Twitter to identify human behaviors relevant to COVID-19 transmission, as well as the perceived impacts of COVID-19 on individuals, as a first step toward real-time monitoring of public perceptions to inform public health communications. Methods: We developed a coding schema for 6 categories and 11 subcategories, which included both a wide number of behaviors as well codes focused on the impacts of the pandemic (eg, economic and mental health impacts). We used this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that performed adequately were applied to our remaining corpus, and temporal and geospatial trends were assessed. We compared the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here. Results: We applied our labeling schema to approximately 7200 tweets. The worst-performing classifiers had F1 scores of only 0.18 to 0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, were much stronger, with F1 scores of 0.64 to 0.66. We applied the social distancing classifiers to over 228 million tweets. We showed temporal patterns consistent with real-world events, and we showed correlations of up to –0.5 between social distancing signals on Twitter and ground truth mobility throughout the United States. Conclusions: Behaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior, as well as for informing public health communication strategies by describing awareness of and compliance with suggested behaviors.
60 APPLIED LIFE SCIENCES↗
Reversed Field Pinch Research in MST (Final Technical Report)
This is the Final Technical Report for the cooperative agreement DE-FC02-05ER54814 titled “Reversed Field Pinch Research in MST” spanning the funding period 4/1/2005 to 12/14/2019. It summarizes key results from MST research, major publications and presentations, and the mentoring of graduate students and postdocs. The Madison Symmetric Torus (MST) is unique within the U.S. and world fusion plasma program. As one of just five operating reversed field pinch (RFP) experiments, and the only one in the U.S., MST has had an important role in advancing the fundamental understanding of the RFP plasma configuration. The MST research program targets topics that intersect with all three goals. While MST provides a unique opportunity to advance the science and fusion potential of the RFP, as a cousin to the tokamak and stellarator plasma configurations the RFP is a valuable partner in establishing validated predictive capability for fusion science more generally. Specific combinations of the major variables in toroidal confinement, like magnetic field strength, plasma current, and shaping, define the different configurations. By exploring adjacent regions in this major variable parameter space, the RFP exposes dependencies not otherwise accessible in the tokamak and stellarator. This diversity enlarges the arena for scientific discovery, both for fusion and plasma physics. The basic science emphasis for MST research has been the self-organizing behavior of RFP plasmas. This inspired MST’s participation in the NSF Physics Frontier Center for Magnetic Self-Organization in Laboratory and Astrophysical Plasmas (CMSO), which ran from 2004-2016 and motivated a substantial fraction of MST’s research goals and capabilities. By all measures the MST Program has been remarkably successful in publications, community engagement, and mentoring of young scientists. While the DOE-FES’s proof-of-principle program for the RFP now languishes through the cessation of this cooperative agreement, operation of MST continues as one of two devices included in the new Wisconsin Plasma Physics Laboratory (WiPPL) basic science user facility. The scope of present research for MST in WiPPL is narrowed to the basic science associated with the RFP magnetic configuration and the advancement of specific fusion topics, which now includes studies of disruptions in tokamak plasmas. The world’s effort in RFP fusion research continues, despite a lack of U.S. federal support to investigate the RFP configuration for fusion application, most notably through a major upgrade to the RFX-mod2 facility in Italy [1] and the recent construction of the KTX facility at USTC in China. First plasmas for RFX-mod2 are anticipated in 2021.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Microstructural evolution in a precipitate-hardened (Fe 0.3 Ni 0.3 Mn 0.3 Cr 0.1 ) 94 Ti 2 Al 4 multi-principal element alloy during high-pressure torsion
Multi-principal element alloys demonstrate high strength, thermal stability, and irradiation resistance, making them excellent candidate materials for applications in nuclear reactors and other harsh environments. Some studies have examined the use of high-pressure torsion to strengthen MPEAs through grain size reduction and strain hardening. However, no studies have investigated the effect of HPT on secondary phases (precipitates) within an MPEA. Two alloys, (Fe 0.3 Ni 0.3 Mn 0.3 Cr 0.1 ) 94 Ti 2 Al 4 containing Ni(Ti, Al) B2 phase, and CrFe σ phase, and single-phase Fe 0.3 Ni 0.3 Mn 0.3 Cr 0.1 , were fabricated by casting and heat treatment. Both alloys were then processed with HPT to study microstructural evolution. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were used to characterize the alloys before and after HPT processing. HPT processing produced a nanocrystalline structure in both alloys, but (Fe 0.3 Ni 0.3 Mn 0.3 Cr 0.1 ) 94 Ti 2 Al 4 exhibited a significantly smaller grain size and higher dislocation density than Fe 0.3 Ni 0.3 Mn 0.3 Cr 0.1 , with corresponding higher hardness. Before HPT, the (Fe 0.3 Ni 0.3 Mn 0.3 Cr 0.1 ) 94 Ti 2 Al 4 alloy consisted of large grain (~ 400 μm) and precipitates, including B2 of ~ 38 μm average size, B2 of ~ 0.7 μm average size, and small amounts of σ of ~ 1.5 μm average size. After HPT, the larger B2 precipitates were decreased in size and volume fraction, while the smaller B2 precipitates were completely dissolved; the σ precipitates appeared unaffected by HPT, likely due to their much higher hardness. Finally, observation of the B2 precipitate distribution along radial distance indicates that the strain caused the precipitates to fracture at intermediate strain (γ = 125) and dissolve at high strain (γ = 280).
36 MATERIALS SCIENCE↗
Black Drum Fish Teeth: Built for Crushing Mollusk Shells
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Deng, Zhifei↗
Studying Open Quantum Systems Relevant to Chemistry on a Trapped-Ion Quantum Simulator (Final Technical Report)
This project advances the trapped-ion quantum simulator as a versatile platform for studying open quantum system phenomena. We aim to contribute to the emerging quantum simulation toolkits and enable simulation of nanoscale energy processes. Trapped-ion platforms offer unique capabilities: their vibrational motion can be precisely manipulated, measured, and coherently coupled to auxiliary qubits. The vibrational mode can function both as a highly sensitive quantum sensor and a programmable environment bath. Using this platform, we achieved three major outcomes. First, we demonstrated using the vibrational mode as an ultrasensitive probe for testing fundamental physics, including possible nonlinear quantum mechanics effects. Second, we established that these modes can act as controllable baths in which tunable noise and loss can enhance or modify energy-transfer dynamics, providing the experimental preparation toward studying mechanisms relevant to chemical reactions and light-harvesting systems. Third, by introducing controllable nonlinear gain and loss, we showed theoretically how simulations using trapped ions can model vibrationally-assisted energy transport in a non‐Hermitian quantum system comprising a chromophore dimer weakly coupled to a vibrational mode. Exploring the non‐Hermitian dynamics of the whole system including vibrations, we found that energy transfer accompanied by absorption of phonons from a vibrational mode can be significantly enhanced near an exceptional point. This theoretical work on simulation of energy transfer processes in driven non‐Hermitian quantum systems revealed an interesting novel path to study open quantum systems dynamics under conditions of gain and loss. We then further explored the benefits of controllable gain and loss with an experimental realization of quantum analogs of nonlinear oscillators, namely, the van der Pol oscillator. Here we observed mutual synchronization mediated by collective dissipation between two oscillators. In parallel, we explored related quantum networking protocols using the same trapped-ion platform, developing fast, high-fidelity schemes for distributing entanglement. Together, these achievements show that trapped-ion vibrational modes provide a highly programmable and high-fidelity platform for investigating complex dissipative quantum behavior, while enabling new approaches to remote quantum sensing, energy science, and nonlinear quantum dynamics.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Progress Towards a Predictive Eagle Behavior and Risk Modeling Framework: Overview and Recent Validation Efforts
This presentation summarizes progress to date of the U.S. Department of Energy project, "Development of a computational framework for modeling golden eagles (Aquila chrysaetos) near wind farms," which focuses on stochastic behavioral modeling of soaring raptors across landscape, facility, and turbine spatiotemporal scales. This publicly available, open-source modeling framework includes behavioral models based on three different underlying principles: energy minimization at landscape scale, behavioral heuristics at landscape-facility scale, and data-driven behaviors at the facility-micro-scale. We will briefly overview the key advancements in the behavioral modeling state of the art, which leverages multiple high-resolution telemetry data sources combined with high-fidelity atmospheric flow modeling insights. We then present preliminary results from a validation study in Altamont, California. This new study involves a novel application of the Stochastic Soaring Raptor Simulator (SSRS), in a new geographic locale, to understand facility scale eagle movement patterns over time scales representative of a wind project's lifetime. For this desktop analysis (that does not depend on any high-performance computing resources), SSRS simultaneously considers a variety of wind conditions and eagle approach vectors toward a project site of interest. This work demonstrates the integration of publicly available landscape-scale atmospheric datasets, our recently improved engineering updraft models (see presentation from Thedin et al.), and our energy minimization behavioral models within the SSRS framework. While we only present results from a single behavioral model, the integration of these three modeling components forms the foundation for our more sophisticated behavioral models (see presentations from Brandes et al., Sandhu et al.) that are under active development. Results are presented in the form of presence maps, which may be applied to estimate risk to wildlife, augment ground survey data, inform wind-plant operations, or incorporated into wind-plant designs.
agent-based modeling↗
Impact of Actinide Complexation on Ligand Radiolysis
Although the actinides boast many unique physical and chemical properties, their inherent susceptibility to radioactive decay are what make them truly interesting elements to study. The absorption of ionizing radiation from actinide decay leads to the formation of a variety of transient and steady-state radicals, ions, and molecular radiolysis products that can lead to significant changes in the surrounding environment, and ultimately dictate steady-state actinide redox distributions and the longevity of molecules designed for actinide complexation. Radiolysis of the latter leads to complexant destruction and the concomitant formation of degradation products that can complicate actinide studies and processes. However, the radiation chemistry of most actinide complexants have been studied in the absence of the actinides they were designed to complex, which can lead to inaccurate conclusions on longevity and degradation product distributions, as metal ion complexation has been historically shown to influence a ligand’s radiolytic behavior. Consequently, bridging this knowledge gap is important for actinide science. Here, I will discuss the impact of actinide complexation on the steady-state and time-resolved radiation-induced reactivity of a variety of complexants, including, tributyl phosphate (TBP) and N,N,N',N'-tetraoctyl diglycolamide (TODGA) .
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Superionic conduction in solid polymer electrolytes – decoupling ion transport from segmental relaxation
Solvent-free, solid polymer electrolytes (SPEs) are promising candidates for next-generation, electrochemical energy storage systems due to their potential to enhance safety and performance, enable flexible device architectures, and streamline manufacturing processes. Conventional SPEs suffer from limited ionic conductivity due to the strong coupling between ion transport and (generally slow) polymer segmental relaxation. The realization of superionic conduction in SPEs, in which ions move faster than the structural relaxation of the polymers, requires a shift in design principles to promote this type of decoupled ion motion. In this perspective, we discuss how polymer architecture, ion–ion correlations, and ion–polymer interactions can unlock superionic behavior. We highlight several key design features, such as crystallinity, bulky side groups, high molecular weight, and percolating ionic aggregation, with a focus on creating low-barrier transport pathways in various polymer systems. We also demonstrate opportunities to combine polymer chemistry and data science through high-throughput and automated screening approaches to reveal how phase behavior, ion dynamics, and ionic interactions govern transport, thereby potentially enabling data-driven discovery of superionic polymer electrolyte materials.
Yang, Mengying [Univ. of Delaware, Newark, DE (Uni↗
2019 Computing Sciences Strategic Plan
Computing has transformed nearly every aspect of scientific inquiry — across disciplines and across scales — from the behavior of subatomic particles to the formation of structures in the early universe, from the assembly of the human genome to the evolution of earth systems. Over the past two decades, computing has become an integral part of how Berkeley Lab is “Bringing Science Solutions to the World.” Advances in computing and mathematics have been key, with new mathematical models of complex physical phenomena, new methods for analyzing complex data, new algorithms for accuracy and scaling and sophisticated software systems that encapsulate these techniques into open, reusable tools. The performance of NERSC computers and the ESnet network have grown by several orders of magnitude, along with our understanding of how to map scientific computations and workflows onto these systems. From research to facility operations, the passion, talent and dedication of the Computing Sciences Area staff has been the cornerstone of our success. The plan outlined in this document describes the next step in a journey to expand the influence and impact of our efforts, building an increasingly connected global enterprise for science that places more powerful instruments in the hands of scientists, along with more powerful methods and tools for modeling, analysis and prediction.
97 MATHEMATICS AND COMPUTING↗
Burst characteristics of advanced accident-tolerant FeCrAl cladding under temperature transient testing
Here assessment of burst characteristics of accident-tolerant fuel (ATF) claddings is essential to evaluate the safety margins of nuclear reactors, and better understanding can enable accelerated licensing for reactor concepts that include the new materials. Therefore, this study investigated the burst behavior of an ATF candidate of C26M, which is an iron–chromium–aluminum alloy (FeCrAl) under transient testing like the simulated loss-of-coolant accident (LOCA) conditions, except the water-quenching phase, in light-water reactors (LWRs). The effect of LOCA specimen length was assessed in terms of post-burst tube parameters. No critical length effect was determined on the burst pressure, burst location, and burst size measurements. Burst temperature showed larger variation likely due to its measurement approach used in this study. The post-test diametral strain was identified as the critical parameter to reduce the specimen length as compared to tube burst length or width. Postmortem optical metrology and digital image correlation were employed to determine the local strain-state during the simulated LOCA, which showed the loading path was close to equibiaxial conditions in regions away from the burst location, rather than internally pressurized conditions (or “plane-strain” tension). Pst-test microstructural characterizations of the FeCrAl revealed that ductile damage was present at the edge of the outer radial surface while the rest of the material was ruptured via cleavage at LOCA burst temperatures. Furthermore, internal grain boundary cracks were observed at locations away from the tube burst region. This behavior was considered to occur during the cooling down period of the simulated LOCA test. Overall, this study aimed to provide essential data for fuel performance code development by considering micromechanics of the deformation and failure.
36 MATERIALS SCIENCE↗
Accelerating discrete dislocation dynamics simulations with graph neural networks
Discrete dislocation dynamics (DDD) is a widely employed computational method to study plasticity at the mesoscale that connects the motion of dislocation lines to the macroscopic response of crystalline materials. However, the computational cost of DDD simulations remains a bottleneck that limits its range of applicability. Here, we introduce a new DDD-GNN framework in which the expensive time-integration of dislocation motion is entirely substituted by a graph neural network (GNN) model trained on DDD trajectories. As a first application, we demonstrate the feasibility and potential of our method on a simple yet relevant model of a dislocation line gliding through an array of obstacles. We show that the DDD-GNN model is stable and reproduces very well unseen ground-truth DDD simulation responses for a range of straining rates and obstacle densities, without the need to explicitly compute nodal forces or dislocation mobilities during time-integration. Our approach opens new promising avenues to accelerate DDD simulations and to incorporate more complex dislocation motion behaviors.
36 MATERIALS SCIENCE↗
Skyrmion Spin Ice in Liquid Crystals
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Material Science↗
New mechanism and exact theory of superconductivity from strong repulsive interaction
A mechanism for pairing in purely repulsive Fermi systems is developed, leading to unconventional superconducting behaviors.
36 MATERIALS SCIENCE↗
Fracture Mechanics—Theory, Modeling and Applications
The field of fracture mechanics was developed during the throes of World War II, and since then, it has been a very active area of research. This is a very energetic and vibrant field because fracture processes are relevant for a vast range of engineering applications spanning different temporal and length scales. Depending on the application, material fracture and fragmentation may be either a desirable feature (for example, for the purpose of hydraulic fracturing operations), or something that is to be avoided at all costs (for example, in key components of mechanical systems). In some other instances, fractures are already present and must be accommodated through novel design (for example, in the case of earthquake ruptures occurring in the Earth’s crust). Because of this wide spectra of motivation, there is a continuous need for the fracture mechanics research community to gain more insight on how fractures are created, evolve, and interact with themselves and with material microstructures. In addition, there are still many remaining questions about how fracture propagation, growth, and interaction impact overall material behavior, and how to develop accurate models to predict this behavior.
36 MATERIALS SCIENCE↗
Intrinsic and environmental drivers of pairwise cohesion in wild Canis social groups
Animals within social groups respond to costs and benefits of sociality by adjusting the proportion of time they spend in close proximity to other individuals in the group (cohesion). Variation in cohesion between individuals, in turn, shapes important group-level processes such as subgroup formation and fission–fusion dynamics. Although critical to animal sociality, a comprehensive understanding of the factors influencing cohesion remains a gap in our knowledge of cooperative behavior in animals. We tracked 574 individuals from six species within the genus Canis in 15 countries on four continents with GPS telemetry to estimate the time that pairs of individuals within social groups spent in close proximity and test hypotheses regarding drivers of cohesion. Pairs of social canids (Canis spp.) varied widely in the proportion of time they spent together (5%–100%) during seasonal monitoring periods relative to both intrinsic characteristics and environmental conditions. The majority of our data came from three species of wolves (gray wolves, eastern wolves, and red wolves) and coyotes. For these species, cohesion within social groups was greatest between breeding pairs and varied seasonally as the nature of cooperative activities changed relative to annual life history patterns. Across species, wolves were more cohesive than coyotes. For wolves, pairs were less cohesive in larger groups, and when suitable, small prey was present reflecting the constraints of food resources and intragroup competition on social associations. Pair cohesion in wolves declined with increased anthropogenic modification of the landscape and greater climatic variability, underscoring challenges for conserving social top predators in a changing world. We show that pairwise cohesion in social groups varies strongly both within and across Canis species, as individuals respond to changing ecological context defined by resources, competition, and anthropogenic disturbance. Our work highlights that cohesion is a highly plastic component of animal sociality that holds significant promise for elucidating ecological and evolutionary mechanisms underlying cooperative behavior.
59 BASIC BIOLOGICAL SCIENCES↗