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At least 217 records · Page 12

Scaling and Complexity of Stress Fluctuations Associated with Smooth and Jerky Flow in FeCoNiTiAl High-Entropy Alloy

Recent observations of jerky flow in high-entropy alloys (HEA) revealed a high role of self-organization of dislocations in their plasticity. The present work reports the first results of the investigation of stress fluctuations during plastic deformation of an FeCoNiTiAl alloy, examined in a wide temperature range covering both smooth and jerky flow. These fluctuations, which accompany the overall deformation behavior representing an essentially slower stress evolution controlled by the work hardening, were processed using complementary approaches comprising Fourier spectral analysis, refined composite multiscale entropy, and multifractal formalisms. The joint analysis at distinct scales testified that even a macroscopically smooth plastic flow is accompanied by nonrandom fluctuations, disclosing the self-organized dynamics of dislocations. Qualitative changes in such a fine-scale “noise” were found with varying temperature. The observed diversity is significant for understanding the relationships between different scales of plasticity of HEAs and crystal materials in general.

36 MATERIALS SCIENCE↗

Emergence and dynamics of unconfined self-organised vortices in active magnetic roller liquids

Actively driven colloids demonstrate complex out-of-equilibrium dynamics often rivaling self-organized patterns and collective behavior observed in living systems. Recent studies revealed the emergence of steady macroscopic states with multiple interacting vortices in an unconfined environment that emerge from the coupling between microscale particle rotation and translation. Yet, insights into the microscopic behavior during the vortex emergence, growth, and formation of a multi-vortical state remain lacking. Here, we investigate in experiments and simulations how the microscale magnetic roller behavior leads to the emergence of seed vortices, their aggregation or annihilation, and the formation of stable large-scale vortical structures. We reveal that the coupling of roller-induced hydrodynamic flows guides the local self-densifications and self-organization of the micro-rollers into seed vortices. Here, the resulting multi-vortical state is sensitive to the external magnetic field amplitude and allows tuning the rollers' number density in a vortex and its characteristic size.

36 MATERIALS SCIENCE↗

An application of adaptive learning to malfunction recovery

A self-organizing controller is developed for a simplified two-dimensional aircraft model. The Controller learns how to pilot the aircraft through a navigational mission without exceeding pre-established position and velocity limits. The controller pilots the aircraft by activating one of eight directional actuators at all times. By continually monitoring the aircraft's position and velocity with respect to the mission, the controller progressively modifies its decision rules to improve the aircraft's performance. When the controller has learned how to pilot the aircraft, two actuators fail permanently. Despite this malfunction, the controller regains proficiency at its original task. The experimental results reported show the controller's capabilities for self-organizing control, learning, and malfunction recovery.

Cruz, R. E.↗

Avalanches and the distribution of solar flares

The solar coronal magnetic field is proposed to be in a self-organized critical state, thus explaining the observed power-law dependence of solar-flare-occurrence rate on flare size which extends over more than five orders of magnitude in peak flux. The physical picture that arises is that solar flares are avalanches of many small reconnection events, analogous to avalanches of sand in the models published by Bak and colleagues in 1987 and 1988. Flares of all sizes are manifestations of the same physical processes, where the size of a given flare is determined by the number of elementary reconnection events. The relation between small-scale processes and the statistics of global-flare properties which follows from the self-organized magnetic-field configuration provides a way to learn about the physics of the unobservable small-scale reconnection processes. A simple lattice-reconnection model is presented which is consistent with the observed flare statistics. The implications for coronal heating are discussed and some observational tests of this picture are given.

Lu, Edward T.↗

Charge order textures induced by non-linear couplings in a half-doped manganite

Abstract The self-organization of strongly interacting electrons into superlattice structures underlies the properties of many quantum materials. How these electrons arrange within the superlattice dictates what symmetries are broken and what ground states are stabilized. Here we show that cryogenic scanning transmission electron microscopy (cryo-STEM) enables direct mapping of local symmetries and order at the intra-unit-cell level in the model charge-ordered system Nd 1/2 Sr 1/2 MnO 3 . In addition to imaging the prototypical site-centered charge order, we discover the nanoscale coexistence of an exotic intermediate state which mixes site and bond order and breaks inversion symmetry. We further show that nonlinear coupling of distinct lattice modes controls the selection between competing ground states. The results demonstrate the importance of lattice coupling for understanding and manipulating the character of electronic self-organization and that cryo-STEM can reveal local order in strongly correlated systems at the atomic scale.

42 ENGINEERING↗

Rapidly Viable Sustained Grid

Rapid recovery of power flow, possibly after a blackout, is a crucial need arising in scenarios that are increasingly becoming more frequent; here, solutions for rapid viability of power while the grid is being restored are urgently needed to keep critical infrastructure (CI) online. Increasingly, after the initial recovery phase, sustenance of reliable power requires assistive services to the grid for long periods of time. Even though the need is urgent, there is only sparse effort present toward a comprehensive framework/strategy for making power rapidly viable with an emphasis on sustained grid ancillary services; which is the focus of this proposal. The proposed concept envisions four phases. In the first phase, when a large portion of power is disrupted (see Figure 1(a)), emphasis is on bringing CI online with the objective of maximizing the time horizon of power viability using resources available at the CI. In the second phase (Figure 1(b)), neighborhood resources are tightly coordinated that forms the CI’s central-core (CC) to provide guaranteed viability of CI over a longer horizon. In the third phase, self-organizing power networks are expanded in a distributed layer supporting the central-core (Figure 1(c)). In the fourth phase, separate CI-networks coalesce and are controlled in a coordinated fashion to provide grid ancillary services (AS), such as primary frequency control and enhancing grid resiliency. With time, system efficiencies and penetration of renewables increase, while the time-horizon of guaranteed sustenance of CI is maximized. Under proposed work, the concept will be instantiated with a focus on medical centers as CI. Comprehensive power hardware in the loop strategies and emulated field tests will guide and validate devised solutions. A strong T2M effort to commercialize resulting technology is outlined. The proposed technology will be transformative for the grid. It will fundamentally change the way large contingencies are managed where power systems and critical infrastructure transition from being fragile to being robust using intelligent, self-organizing control for coordinating resources, enhanced resiliency and use of sustainable energy sources.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Molecular Simulations in Astrobiology

One of the main goals of astrobiology is to understand the origin of cellular life. In the absence of any record of the earliest ancestors of contemporary cells, protocells, the most direct way to test our understanding of their characteristics is to construct laboratory models of protocells. Such efforts, currently underway in the NASA Astrobiology Program, are accompanied by computational studies aimed at explaining self-organization of simple molecules into ordered structures and developing designs of molecules that are capable of performing protocellular functions. Many of these functions, such as importing nutrients, capturing and storing energy, and responding to changes in the environment, are carried out by proteins bound to membranes. We use computer simulations to address the following, questions about these proteins: (1) How do small proteins (peptides) organize themselves into ordered structures at water-membrane interfaces and insert into membranes? (2) How do peptides aggregate to form membrane-spannin(y structures (e.g., channels)? (3) By what mechanisms do such aggregates perform their functions? The simulations are performed using the molecular dynamics (MD) method. In this method, Newton's equations of motion for each atom in the system are solved iteratively. At each time step, the forces exerted on each atom by the remaining atoms are evaluated by dividing them into two parts. Short-range forces are calculated directly in real space while long-range forces are evaluated in reciprocal space, usually using a particle-mesh algorithm which is of order O(NlnN). Currently, a time step of 2 femtoseconds is typically used, thereby making studies of problems occurring on multi-nanosecond time scales (10(exp 6) - 10(exp 8) time steps) accessible. To address a broader range of problems, simulations need to be extended by three orders of magnitude. Such an extension requires both algorithmic improvements and codes scalable to a large number of parallel processors. Work in this direction is in progress. Two specific series of simulations that demonstrate how peptides self-organize and function in membranes are discussed. In one series of simulations, it was shown that nonpolar peptides, disordered in water, translocate to the nonpolar interior of the membrane and, simultaneously, fold into two different helical structures, which remain in equilibrium. Once in the membrane, the peptides can readily change their orientation, especially in response to local electric fields. This structural and orientational flexibility of peptides with changing conditions may have provided a mechanism of transmitting signals between the environment and the interior of the protocell. In another series of simulations, the mechanism by which a simple protein channel efficiently mediates proton transport across membranes was investigated. This process is a key step in cellular bioenergetics. In the channel under study, proton transport is gated by four histidines that occlude the channel pore. The simulations demonstrate that protons move through the gate by a "shuttle" mechanism, wherein one histidine is protonated on the extracellular side and, subsequently, the proton bound on the opposite side is released.

Pohorille, Andrew↗

Range and strength of mechanical interactions of force dipoles in elastic fiber networks

Mechanical forces generated by myosin II molecular motors drive diverse cellular processes, most notably shape change, division and locomotion. These forces may be transmitted over long range through the cytoskeletal medium – a disordered, viscoelastic network of biopolymers. The resulting cell size scale force chains can in principle mediate mechanical interactions between distant actomyosin units, leading to self-organized structural order in the cell cytoskeleton. Inspired by such force transmission through elastic structures in the cytoskeleton, we consider a percolated fiber lattice network, where fibers are represented as linear elastic elements that can both bend and stretch, and the contractile activity of myosin motors is represented by force dipoles. Then, by using a variety of metrics, we show how two such contractile force dipoles interact with each other through their mutual mechanical deformations of the elastic fiber network. As a prelude to two-dipole interactions, we quantify how forces propagate through the network from a single anisotropic force dipole by analyzing clusters of nodes connected by highly strained bonds, as well as through the decay rate of strain energy with distance from a force dipole. We show that predominant fiber bending screens out force propagation, resulting in reduced and strongly network configuration-dependent dipole interactions. On the other hand, stretching-dominated networks support longer-ranged inter-dipole interactions that recapitulate the predictions of linear elasticity theory. By characterizing the differences between tensile and compressive force propagation in the fiber network, we show how inter-dipole interaction depends on the dipoles’ mutual separation and orientation. The resulting elastic interaction energy may mediate a force between multiple distant dipoles, leading to their self-organization into ordered configurations. In conclusion, this provides a potential pathway for active mechanical force-driven structural order in elastic biopolymer networks.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Characterizing Mesoscale Cellular Convection in Marine Cold Air Outbreaks With a Machine Learning Approach

Abstract During marine cold‐air outbreaks (MCAOs), when cold polar air moves over warmer ocean, a well‐recognized cloud pattern develops, with open or closed mesoscale cellular convection (MCC) at larger fetch over open water. The Cold‐Air Outbreaks in the Marine Boundary Layer Experiment provided a comprehensive set of ground‐based in situ and remote sensing observations of MCAOs at a coastal location in northern Norway. MCAO periods that unambiguously exhibit open or closed MCC are determined. Individual cells observed with a profiling Ka‐band radar are identified using a watershed segmentation method. Using self‐organizing maps (SOMs), these cells are then objectively classified based on the variability in their vertical structure. The SOM nodes contain some information about the location of the cell transect relative to the center of the MCC. This adds classification noise, requiring numerous cell transects to isolate cell dynamical information. The SOM‐based classification shows that comparatively intense convection occurs only in open MCC. This convection undergoes an apparent lifecycle. Developing cells are associated with stronger updrafts, large spectrum width, larger amounts of liquid water, lower surface precipitation rates, and lower cloud tops than mature and weakening cells. The weakening of these cells is associated with the development of precipitation‐induced cold pools. The SOM classification also reveals less intense convection, with a similar lifecycle. More stratiform vertical cloud structures with weak vertical motions are common during closed MCC periods and are separated into precipitating and non‐precipitating stratiform cores. Convection is observed only occasionally in the closed MCC environment.

Meteorology & Atmospheric Sciences↗

The Features of Self-Assembling Organic Bilayers Important to the Formation of Anisotropic Inorganic Materials in Microgravity Conditions

There is a growing need for inorganic anisotropic particles in a variety of materials science applications. Structural, optical, and electrical properties can be greatly augmented by the fabrication of composite materials with anisotropic microstructures or with anisotropic particles uniformly dispersed in an isotropic matrix. Examples include structural composites, magnetic and optical recording media, photographic film, certain metal and ceramic alloys, and display technologies including flat panel displays. While considerable progress has been made toward developing an understanding of the synthesis of powders composed of monodispersed, spherical particles, these efforts have not been transferred to the synthesis of anisotropic nanoparticles. The major objective of the program is to develop a fundamental understanding of the growth of anisotropic particles at organic templates, with emphasis on the chemical and structural aspects of layered organic assemblies that contribute to the formation of anisotropic inorganic particles.

Talham, Daniel R.↗

The features of self-assembling organic bilayers important to the formation of anisotropic inorganic materials in microgravity conditions

Materials with directional properties are opening new horizons in a variety of applications including chemistry, electronics, and optics. Structural, optical, and electrical properties can be greatly augmented by the fabrication of composite materials with anisotropic microstructures or with anisotropic particles uniformly dispersed in an isotropic matrix. Examples include structural composites, magnetic and optical recording media, photographic film, certain metal and ceramic alloys, and display technologies including flat panel displays. The new applications and the need for model particles in scientific investigations are rapidly out-distancing the ability to synthesize anisotropic particles with specific chemistries and narrowly distributed physical characteristics (e.g. size distribution, shape, and aspect ratio).

Talham, Daniel R.↗

On principles of emergent organization

After more than a century of concerted effort, physics still lacks basic principles of spontaneous organization. To appreciate why, we first state the problem, outline historical approaches, and survey the present state of the physics of self-organization. This frames the particular challenges arising from mathematical intractability and the resulting need for computational approaches, as well as those arising from a chronic failure to define structure. Then an overview of two modern mathematical formulations of organization—intrinsic computation and evolution operators—lays out a way to overcome these challenges. Together, the vantage point they afford shows how to account for the emergence of structured states via a statistical mechanics of systems arbitrarily far from equilibrium. The result is a constructive path forward to principles of organization that builds on mathematical identification of structure.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

The role of UC inclusions in the development of fission gas bubble superlattice neutron-irradiated monolithic U-10Mo fuels

Uranium Carbide (UC) inclusions are the most prevalent impurities in Uranium-Molybdenum (U-Mo) fuel and are considered undesirable because they could potentially affect fuel performance. This work revealed that, like grain boundaries (gBs), UC inclusions could help facilitate the formation of the fission gas bubble superlattice (GBS). The GBS is a highly organized complex defect structure that can effectively store fission gases, thereby inhibiting fuel swelling. Transmission electron microscopy (TEM) showed that GBS self-organization can initiate at the UC/U-Mo interfaces in U-10Mo fuel irradiated to low fission density. Furthermore, this study also revealed that the UC boundary in U-10Mo irradiated to low fission density is wavy and periodic in morphology and that GBS formation is semi-coherent with the UC boundary. Because the initiation of GBS occurs at gBs and UC/U-Mo interfaces, the fission gas inventory is always highest at those regions compared to the U-Mo grain interior. Consequently, at higher fission densities, it is likely that high burnup structure (HBS) development via grain refinement will begin at gBs and UC/U-Mo interfaces.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Influence of Carbon-Nitride Dot-Emitting Species and Evolution on Fluorescence-Based Sensing and Differentiation

Carbon dots have attracted widespread interest for sensing applications based on their low cost, ease of synthesis, and robust optical properties. We investigate structure–function evolution on multiemitter fluorescence patterns for model carbon-nitride dots (CNDs) and their implications on trace-level sensing. Hydrothermally synthesized CNDs with different reaction times were used to determine how specific functionalities and their corresponding fluorescence signatures respond upon the addition of trace-level analytes. Archetype explosives molecules were chosen as a testbed due to similarities in substituent groups or inductive properties (i.e., electron withdrawing), and solution-based assays were performed using ratiometric fluorescence excitation–emission mapping (EEM). Analyte-specific quenching and enhancement responses were observed in EEM landscapes that varied with the CND reaction time. We then used self-organizing map models to examine EEM feature clustering with specific analytes. Finally, the results reveal that interactions between carbon-nitride frameworks and molecular-like species dictate response characteristics that may be harnessed to tailor sensor development for specific applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Toward Multiscale, Multimaterial 3D Printing

Biological materials and organisms possess the fundamental ability to self-organize, through which different components are assembled from the molecular level up to hierarchical structures with superior mechanical properties and multifunctionalities. These complex composites inspire material scientists to design new engineered materials by integrating multiple ingredients and structures over a wide range. Additive manufacturing, also known as 3D printing, has advantages with respect to fabricating multiscale and multi-material structures. The need for multifunctional materials is driving 3D printing techniques toward arbitrary 3D architectures with the next level of complexity. In this paper, the aim is to highlight key features of those 3D printing techniques that can produce either multiscale or multimaterial structures, including innovations in printing methods, materials processing approaches, and hardware improvements. Several issues and challenges related to current methods are discussed. Ultimately, the authors also provide their perspective on how to realize the combination of multiscale and multimaterial capabilities in 3D printing processes and future directions based on emerging research.

36 MATERIALS SCIENCE↗

Fog Intermittency and Critical Behavior

The intermittency of fog occurrence (the switching between fog and no-fog) is a key stochastic feature that plays a role in its duration and the amount of moisture available. Here, fog intermittency is studied by using the visibility time series collected during the month of July 2022 on Sable Island, Canada. In addition to the visibility, time series of air relative humidity and turbulent kinetic energy, putative variables akin to the formation and breakup conditions of fog, respectively, are also analyzed in the same framework to establish links between fog intermittency and the underlying atmospheric variables. Intermittency in the time series is quantified with their binary telegraph approximations to isolate clustering behavior from amplitude variations. It is shown that relative humidity and turbulent kinetic energy bound many stochastic features of visibility, including its spectral exponent, clustering exponent, and the growth of its block entropy slope. Although not diagnostic, the visibility time series displays features consistent with Pomeau–Manneville Type-III intermittency in its quiescent phase duration PDF scaling (−3/2), power spectrum scaling (−1/2), and signal amplitude PDF scaling (−2). The binary fog time series exhibits properties of self-organized criticality in the relation between its power spectrum scaling and quiescent phase duration distribution.

54 ENVIRONMENTAL SCIENCES↗

Programs and Code for Geothermal Exploration Artificial Intelligence

The scripts below are used to run the Geothermal Exploration Artificial Intelligence developed within the "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning" project. It includes all scripts for pre-processing and processing, including: - Land Surface Temperature K-Means classifier - Labeling AI using Self Organizing Maps (SOM) - Post-processing for Permanent Scatterer InSAR (PSInSAR) analysis with SOM - Mineral marker summarizing - Artificial Intelligence (AI) Data splitting: creates data set from a single raster file - Artificial Intelligence Model: creates AI from a single data set, after splitting in Train, Validation and Test subsets - AI Mapper: creates a classification map based on a raster file

15 GEOTHERMAL ENERGY↗