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Results for “self-organization”

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 OSTI2022

Staircase formation by resonant and non-resonant transport of potential vorticity

The E × B staircase is a quasi-periodic pattern of pressure profile corrugations. In this work, we present a new mechanism for E × B staircase formation that involves resonant transport versus non-resonant transport. We start from a potential vorticity evolution system and use quasi-linear theory, a model dispersion relation, and a bi-Lorentzian spectrum approximation, to construct the relation between the fluxes and the profiles. With these fluxes, we close the profile evolution equations and the extended turbulence intensity evolution equation, which together constitute a turbulence-profile evolution system. In this system, the Doppler effect from the E × B mean flow can cause resonance between trapped ion precession motion and the trapped ion mode, which drives a resonant transport contribution to the fluxes. The profiles will be flattened where the resonant transport is switched on. In contrast, for the regions of non-resonant transport, profiles are steeper. A quasi-periodic pattern of profile corrugation (the E × B staircase) spontaneously emerges in this system, which is the two states mentioned above, arranged as alternating layers in space. The feedback processes during the staircase pattern formation are identified. An estimate of the critical value of the boundary heat flux is obtained, above which the staircase formation will be triggered. An estimate scaling of the step size in the staircase pattern is obtained. The resonant turbulent transport is also a mechanism for collisionless saturation of zonal flow. Furthermore, this work is related to internal transport barrier formation and suggests some new scenarios, such as an enhanced confined L mode.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Microscopic Imprints of Learned Solutions in Tunable Networks

In physical networks trained using supervised learning, physical parameters are adjusted to produce desired responses to inputs. An example is an electrical contrastive local learning network of nodes connected by edges that adjust their conductances during training. When an edge conductance changes, it upsets the current balance of every node. In response, physics adjusts the node voltages to minimize the dissipated power. Learning in these systems is therefore a coupled double-optimization process, in which the network descends both a cost landscape in the high-dimensional space of edge conductances and a physical landscape—the power dissipation—in the high-dimensional space of node voltages. Because of this coupling, the physical landscape of a trained network contains information about the learned task. Here, we derive a structure-function relation for trained tunable networks and demonstrate that all the physical information relevant to the trained input-output relation can be captured by a tuning susceptibility, an experimentally measurable quantity. We supplement our theoretical results with simulations to show that the tuning susceptibility is correlated with functional importance and that we can extract physical insight into how the system performs the task from the conductances of highly susceptible edges. Our analysis is general and can be applied directly to mechanical networks, such as networks trained for protein-inspired function such as allostery.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Compositional patterning in irradiated alloys: Effective potentials and effective interfacial energy

Compositional patterning (CP) in binary alloys during energetic particle irradiation is studied using a kinetic model that considers two competing kinetic processes, a thermally activated one promoting macroscopic phase separation (MPS) of the concentration field c(r,t) and a forced one resulting in finite-range random atomic mixing. The forced mixing is modeled by a Gaussian relocation distribution with a characteristic distance R. A series of approximate kinetic models are introduced by expanding the mixing function into a series of n terms, thus replacing the non-local evaluations of the concentration field c(r'-r,t) by local derivatives of c(r,t). This approach makes it possible to obtain exact effective potentials and build steady-state diagrams for each order-n model. Phase field (PF) simulations using these order-n models reveal that near the onset of patterning, phase evolution is accurately described using an order-3 model, which changes smoothly from an extended Cahn-Hilliard free energy in the MPS regime to a one-mode Swift-Hohenberg functional in the CP regime. Deeper into the patterning regime, higher-order models are required to achieve convergence, yielding square-like concentration profiles characteristic of a strong segregation regime. These higher-order effective free energies are analogous to multimodal Swift-Hohenberg functionals. Here, a new definition for the effective interfacial energy is proposed in the CP regime, since the interfacial area is no longer an excess quantity in that regime, precluding the use of the standard thermodynamic definition of interfacial energy.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Universality class for loopless invasion percolation models and a percolation avalanche burst model for hydraulic fracturing

Invasion percolation is a model that was originally proposed to describe growing networks of fractures. In this work, we describe a loopless algorithm on random lattices, coupled with an avalanche-based model for bursts. The model reproduces the characteristic b-value seismicity and spatial distribution of bursts consistent with earthquakes resulting from hydraulic fracturing (“fracking”). We test models for both site invasion percolation and bond invasion percolation. These have differences on the scale of site and bond lengths l. But since the networks are characterized by their large-scale behavior, l << L, we find small differences between scaling exponents. Though data may not differentiate between models, our results suggest that both models belong to different universality classes.

58 GEOSCIENCES↗
DOE OSTI2021

Entropy production fluctuations encode collective behavior in active matter

We derive a general lower bound on distributions of entropy production in interacting active matter systems. The bound is tight in the limit that interparticle correlations are small and short-ranged, which we explore in four canonical active matter models. In all models studied, the bound is weak where collective fluctuations result in long-ranged correlations, which subsequently links the locations of phase transitions to enhanced entropy production fluctuations. We develop a theory for the onset of enhanced fluctuations and relate it to specific phase transitions in active Brownian particles. Furthermore, we also derive optimal control forces that realize the dynamics necessary to tune dissipation and manipulate the system between phases. In so doing, we uncover a general relationship between entropy production and pattern formation in active matter, as well as ways of controlling it.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Observations of heteroclinic bifurcations in resistive magnetohydrodynamic simulations of the plasma response to resonant magnetic perturbations

A class of topological magnetic island bifurcations that has not previously been observed in toroidal plasmas is described. Increasing an externally applied three-dimensional magnetic field in resistive magnetohydrodynamic simulations results in the asymmetric elongation of resonant island flux surfaces followed by a sequence of heteroclinic bifurcations. Here, these bifurcations produce new sets of hyperbolic-elliptic fixed points as predicted by the Poincaré-Birkoff fixed point theorem. Field line calculations verify that the new fixed points do not connect to those of the prebifurcated islands as required for heteroclinic bifurcations on a torus with winding numbers composed of common integer factors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Mechanism of endosomal escape by p H -responsive nucleic-acid vectors

Successful intracellular delivery of nucleic acids (NAs) hinges on many factors, one of them being NAs’ efficacious escape from endosomes. As competent NA vectors, pH-responsive gemini surfactants (GSs) might achieve high efficacy by facilitating endosomal escape. However, how the GSs assist the escape remains debated as many proposed mechanisms still lack experimental support, which hinders replication and further improvement of the efficient delivery. Here, via UV, fluorescence spectroscopy, and small-angle neutron scattering (SANS), we examined a pH-responsive GS's and a pH-unresponsive GS's capabilities to compact DNA and withstand binding competition, and their interactions with model endosomal and lysosomal membranes, at varied pHs. Acidification-driven enhancement of DNA-compaction capability and of stability against binding competition were found specific to the pH-responsive GS. Alongside the pH-responsive GS's structural perturbation to the membranes as observed with SANS, the features suggest that pH-responsive GSs facilitate endosomal escape by releasing excess GS molecules from DNA-GS complexes upon acidification in endosome maturation, with the released GS molecules disrupting endosomal and lysosomal membranes and thereby assisting the escape. Here a general design principle for NA vectors is proposed on the basis of this experimental finding.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Universal behavior in fragmenting brittle, isotropic solids across material properties

A bonded particle model is used to explore how variations in the material properties of brittle, isotropic solids affect critical behavior in fragmentation. To control material properties, a model is proposed which includes breakable two- and three-body particle interactions to calibrate elastic moduli and mode I and mode II fracture toughnesses. In the quasistatic limit, fragmentation leads to a power-law distribution of grain sizes which is truncated at a maximum grain mass that grows as a nontrivial power of system size. In the high-rate limit, truncation occurs at a mass that decreases as a power of increasing rate. A scaling description is used to characterize this behavior by collapsing the mean-square grain mass across rates and system sizes. Consistent scaling persists across all material properties studied, although there are differences in the evolution of grain size distributions with strain as the initial number of grains at fracture and their subsequent rate of production depend on Poisson's ratio. Importantly, this evolving granular structure is found to induce a unique rheology where the ratio of the shear stress to pressure, an internal friction coefficient, decays approximately as the logarithm of increasing strain rate. The stress ratio also decreases at all rates with increasing strain as fragmentation progresses and depends on elastic properties of the solid.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Motility-induced phase separation and frustration in active matter swarmalators

Here we introduce a two dimensional system of active matter swarmalators composed of elastically interacting run-and-tumble active disks with an internal parameter Φ i . The disks experience an additional attractive or repulsive force with neighboring disks depending upon their relative difference in Φ i , making them similar to swarmalators used in robotic systems. In the absence of the internal parameter, the system forms a motility-induced phase separated (MIPS) state, but when the swarmalator interactions are present, a wide variety of other active phases appear depending upon whether the interaction is attractive or repulsive and whether the particles act to synchronize or ant-synchronize their internal parameter values. These phases include a gas-free gel regime, arrested clusters, a labyrinthine state, a regular MIPS state, a frustrated MIPS state for attractive antisynchronization, and a superlattice MIPS state for attractive synchronization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Random Close Packing as a Dynamical Phase Transition

Sphere packing is an ancient problem. The densest packing is known to be a face-centered cubic (FCC) crystal, with space-filling fraction Φ FCC = π / √ 18 ≈ 0.74 . The densest “random packing,” random close packing (RCP), is yet ill defined, although many experiments and simulations agree on a value Φ RCP ≈ 0.64 . We introduce a simple absorbing-state model, biased random organization (BRO), which exhibits a Manna class dynamical phase transition between absorbing and active states that has as its densest critical point Φ cmax ≈ 0.64 ≈ Φ RCP and, like other Manna class models, is hyperuniform at criticality. The configurations we obtain from BRO appear to be structurally identical to RCP configurations from other protocols. This leads us to conjecture that the highest-density absorbing state for an isotropic biased random organization model produces an ensemble of configurations that characterizes the state conventionally known as RCP.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Critical Scaling of Solid Fragmentation at Quasistatic and Finite Strain Rates

Here, using two-dimensional simulations of sheared, brittle solids, we characterize the resulting fragmentation and explore its underlying critical nature. Under quasistatic loading, a power-law distribution of fragment masses emerges after fracture which grows with increasing strain. With increasing strain rate, the maximum size of a grain decreases and a shallower distribution is produced. We propose a scaling theory for distributions based on a fractal scaling of the largest mass with system size in the quasistatic limit or with a correlation length that diverges as a power of rate in the finite-rate limit. Critical exponents are measured using finite-size scaling techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Unraveling the discontinuous plastic flow of a Co-Cr-Fe-Ni-Mo multiprincipal-element alloy at deep cryogenic temperatures

We report an analysis of the discontinuous plastic flow of a multiprincipal-element alloy, Co 17.5 Cr 12.5 Fe 55 Ni 10 Mo 5 (atomic percent, at. %), in the temperature range of 0.5–4.2 K showing serrated deformation curves. Using the analytical techniques, we studied the statistics of the stress drops associated with the unstable plastic flow. The analysis showed that the complexity and heterogeneity of a discontinuous plastic flow were reduced when the temperature was lowered. This behavior was associated with the effects of dynamic recovery and adiabatic heating on the dislocation-density evolution.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Structured ground states of randomly interacting bosons

Bosonic degrees of freedom and their emergence as part of complex quantum many-body dynamics, symmetries, collective behavior, clustering, and phase transitions play an important role in modern studies of quantum systems. In this paper, we present a systematic study of many-boson systems governed by random interactions. Our findings show that ground states of randomly interacting bosons are not random, being dominated by a few collective configurations containing condensates of clusters.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Inferring plant–plant interactions using remote sensing

Rapid technological advancements and increasing data availability have improved the capacity to monitor and evaluate Earth's ecology via remote sensing. However, remote sensing is notoriously ‘blind’ to fine-scale ecological processes such as interactions among plants, which encompass a central topic in ecology. Here, we discuss how remote sensing technologies can help infer plant–plant interactions and their roles in shaping plant-based systems at individual, community and landscape levels. At each of these levels, we outline the key attributes of ecosystems that emerge as a product of plant–plant interactions and could possibly be detected by remote sensing data. We review the theoretical bases, approaches and prospects of how inference of plant–plant interactions can be assessed remotely. At the individual level, we illustrate how close-range remote sensing tools can help to infer plant–plant interactions, especially in experimental settings. At the community level, we use forests to illustrate how remotely sensed community structure can be used to infer dominant interactions as a fundamental force in shaping plant communities. At the landscape level, we highlight how remotely sensed attributes of vegetation states and spatial vegetation patterns can be used to assess the role of local plant–plant interactions in shaping landscape ecological systems. Synthesis . Remote sensing extends the domain of plant ecology to broader and finer spatial scales, assisting to scale ecological patterns and search for generic rules. Robust remote sensing approaches are likely to extend our understanding of how plant–plant interactions shape ecological processes across scales—from individuals to landscapes. Combining these approaches with theories, models, experiments, data-driven approaches and data analysis algorithms will firmly embed remote sensing techniques into ecological context and open new pathways to better understand biotic interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Root‐Pore Interactions, the Underestimated Driver for Rhizosphere Structure and Rhizosheath Development

Physical characteristics of rhizosphere and rhizosheath, that is, root-adhering soil, are crucial for plant performance. Yet, the drivers of the rhizosphere's structural properties and their relationships with rhizosheath development remain unclear. We used X-ray computed micro-tomography (i) to explore two drivers of rhizosphere porosity: root-induced changes vs. preferential root growth into soil with certain pore characteristics and (ii) to estimate their contributions to rhizosphere macroporosity gradients and rhizosheath formation. Rhizosheath development was assessed in relation to rhizosphere macroporosity and rhizodeposition after ¹⁴C labeling. Our results confirmed that both root-induced changes and growth preferences shape rhizosphere structure, with their relative significance depending on the inherent macropore availability. In intact soils, growth preferences were the dominant factor, while in sieved soils the root-induced changes became equally important. Rhizosheath formation was associated with roots compacting their surrounding and releasing carbon. However, no correlation was found between rhizosheath formation and the actual rhizosphere, that is, the volume of soil adjacent to the roots. The study offers new process-level understanding of rhizosphere porosity gradients, while emphasizing caution in interpreting root growth data from sieved soil studies. Similarly, traditional destructively sampled rhizosheath may not fully capture the true characteristics of the actual rhizosphere, underscoring importance of intact-soil analyses.

macroporosity gradients↗
DOE OSTI2021

LL18-Signal Analysis-NDD2Ad (Final Report)

Under favorable circumstances, seismic waveforms corresponding to an ensemble of events related by a common, spatially-distributed process collectively exhibit a regular, signal-space geometry. When events in the ensemble have a common, or nearly common, source mechanism, this geometry is a distorted image of the distribution of events in the source region. The signal space image can be visualized using a relatively simple waveform alignment and projection operation. Ensemble waveform correlation measurements can be inverted to estimate the distribution of the events in the source region, up to an arbitrary rotation, reflection, and scaling with residual distortion. We demonstrate these concepts with synthetic waveforms and with observations of long-wall mining induced seismicity for which substantial ground truth information is available. Our experience with these data has implications for location, correlation detection and machine learning, and possible application to studies of repeating events in induced, volcanic, and glacial seismicity. Our results place limits on the widely-held assumption that waveform correlation is a useful measure of event separation. We suggest that the constraints on event separation need to be evaluated in the context of a population of related events, whose waveforms sample the signal space image of the source region. A better indicator of event separation is the length of the shortest path in signal space along the image.

58 GEOSCIENCES↗