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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Generation and Control of Self-Organized Nonlinear Kinetic Structures in High Energy Density Plasmas in the Presence of Intense Magnetic Fields and Ultrashort Laser Pulses

In the project we investigated mechanisms to sculpt phase space by controlling plasma instabilities. We found that linear theory, which applies in the initial growth phase of an instability, allows for initial conditions that can suppress instabilities. This provides a possible mechanism to release the energy available to an instability on demand. We developed various computational techniques that can reduce computational cost of simulations while maintaining or improving physical fidelity. We considered the Buneman instability in the presence of a ponderomotively driven plasma wave to examine the interplay between an instability and steady-state plasma response.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Eruption and Self-Organization of Free-Surface, Arched, and Line-Tied Magnetic Flux Ropes (Final Technical Report)

Coronal mass ejections (CMEs) are some of the most energetic and violent events in our solar system. This investigation uses laboratory experiments to study the onset threshold and nonlinear consequences of ideal MHD instabilities of magnetic flux ropes where are regarded to be related CMEs, and has obtained qualitative and quantitative understanding the dynamics of magnetic flux ropes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Self-Propulsion Enhances Polymerization

Self-assembly is a spontaneous process through which macroscopic structures are formed from basic microscopic constituents (e.g., molecules or colloids). By contrast, the formation of large biological molecules inside the cell (such as proteins or nucleic acids) is a process more akin to self-organization than to self-assembly, as it requires a constant supply of external energy. Recent studies have tried to merge self-assembly with self-organization by analyzing the assembly of self-propelled (or active) colloid-like particles whose motion is driven by a permanent source of energy. Here we present evidence that points to the fact that self-propulsion considerably enhances the assembly of polymers: self-propelled molecules are found to assemble faster into polymer-like structures than non self-propelled ones. The average polymer length increases towards a maximum as the self-propulsion force increases. Beyond this maximum, the average polymer length decreases due to the competition between bonding energy and disruptive forces that result from collisions. The assembly of active molecules might have promoted the formation of large pre-biotic polymers that could be the precursors of the informational polymers we observe nowadays.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Observations of a plectonemic configuration in a stable magnetized plasma jet

Astrophysical jets are collimated high-speed outflows emerging from spinning and accreting matter around celestial objects and may spontaneously result from self-organized processes. Magnetic self-organization is commonly observed in laboratory plasma physics experiments; however, they require close-fitting flux conservers to constrain and stabilize the toroidal or cylindrical structures. Here we report the first observations of a long, stable, free-boundary plasma jet far from chamber walls, embedding a double-helix magnetic structure resembling a force-free plectonemic Taylor state. The jets arise from an experimental setup that mimics an accretion disk and has no close-fitting solid flux conserver. The results support the hypothesis that self-organization could be a universal, intrinsic explanation for jet formation, collimation, and stability and may help explain double-helix features in celestial observations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

A procedure for rule extraction from a Self-Organising plasma disruption predictor for JET

In a previous paper, a Self-Organizing Map had proven to be able to identify the regions of the plasma operative space characterizing the pre-disruptive phase at JET without relying on any a priori information. One of the strengths of this disruption predictor lies in its inherent self-organization capability. The Self-Organizing Map discovers non-trivial relationships and captures the complicated interplay of device diagnostics on the internal plasma states directly from the experimental data. Moreover, the provided model allows the visualization of high-dimensional plasma parameters and facilitates easy interrogation of the model to understand the reasons behind its correlations. In this paper, an additional step is taken towards the interpretability of models for predicting disruptions by training a Decision Tree to classify the plasma states according to the interpretation provided by the Self-Organizing Map (stable or at high risk of disruptions). The Decision tree provides a set of rules which describe the transition of the plasma towards the pre-disruptive phase as visualized in the Self-Organizing Map. The obtained rules for the database explored in the study identify four regions in the map, two of which are at risk of disruption. These regions correspond to partitions of a 3D space based on the peaking factors of the core and divertor radiation, as well as the Locked Mode. The agreement between the Self-Organizing Map answers and the rules supplied by the Decision Tree is confirmed by the comparison of the performance exhibited by the two models in the prediction of disruptions.

Setzu, Samuele [Univ. of Cagliari, Monserrato, Cag↗
DOE OSTI2024

A collision operator for describing dissipation in noncanonical phase space

The phase space of a noncanonical Hamiltonian system is partially inaccessible due to dynamical constraints (Casimir invariants) arising from the kernel of the Poisson tensor. When an ensemble of noncanonical Hamiltonian systems is allowed to interact, dissipative processes eventually break the phase space constraints, resulting in a thermodynamic equilibrium described by a Maxwell–Boltzmann distribution. However, the time scale required to reach Maxwell–Boltzmann statistics is often much longer than the time scale over which a given system achieves a state of thermal equilibrium. Examples include diffusion in rigid mechanical systems, as well as collisionless relaxation in magnetized plasmas and stellar systems, where the interval between binary Coulomb or gravitational collisions can be longer than the time scale over which stable structures are self-organized. Here, we focus on self-organizing phenomena over spacetime scales such that particle interactions respect the noncanonical Hamiltonian structure, but yet act to create a state of thermodynamic equilibrium. We derive a collision operator for general noncanonical Hamiltonian systems, applicable to fast, localized interactions. This collision operator depends on the interaction exchanged by colliding particles and on the Poisson tensor encoding the noncanonical phase space structure, is consistent with entropy growth and conservation of particle number and energy, preserves the interior Casimir invariants, reduces to the Landau collision operator in the limit of grazing binary Coulomb collisions in canonical phase space, and exhibits a metriplectic structure. We further show how thermodynamic equilibria depart from Maxwell–Boltzmann statistics due to the noncanonical phase space structure, and how self-organization and collisionless relaxation in magnetized plasmas and stellar systems can be described through the derived collision operator.

Boltzmann equation↗
DOE OSTI2022

Guiding Self-Assembly of Active Colloids by Temporal Modulation of Activity

Self-organization phenomena in ensembles of self-propelled particles open pathways to the synthesis of new dynamic states not accessible by traditional equilibrium processes. The challenge is to develop a set of principles that facilitate the control and manipulation of emergent active states. In this study, we report that dielectric rolling colloids energized by a pulsating electric field self-organize into alternating square lattices with a lattice constant controlled by the parameters of the field. We combine experiments and simulations to examine spatiotemporal properties of the emergent collective patterns and investigate the underlying dynamics of the self-organization.We reveal the resistance of the dynamic lattices to compression and expansion stresses leading to a hysteretic behavior of the lattice constant. The general mechanism of pattern synthesis and control in active ensembles via temporal modulation of activity can be applied to other active colloidal systems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Unsupervised discovery of extreme weather events using universal representations of emergent organization

Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal representations that identify and describe these key objects remain elusive. Here, we introduce a theoretically grounded framework for describing emergent organization that, via data-driven algorithms, is constructive in practice. Its building blocks are spacetime lightcones that embody how information propagates across a system through local interactions. We show that predictive equivalence classes of lightcones—local causal states—capture organized behaviors in complex spatiotemporal systems. Employing an unsupervised physics-informed machine learning algorithm and a high-performance computing implementation, we demonstrate automatically discovering organized structures in two real-world domain science problems. We show that local causal states identify vortices and track their power-law decay behavior in two-dimensional fluid turbulence. We then show how to detect and track familiar extreme weather events—hurricanes and atmospheric rivers—and discover other novel structures associated with precipitation extremes in high-resolution climate data at the grid-cell level.

Rupe, Adam [Pacific Northwest National Laboratory ↗
DOE OSTI2025

Nanotubes Growth by Self-Assembly of DNA Strands at Room Temperature

Artificial biomolecular nanotubes are a promising approach to building materials mimicking the capacity of the cellular cytoskeleton to grow and self-organize dynamically. Nucleic acid nanotechnology has demonstrated a variety of self-assembling nanotubes with programmable, robust features and morphological similarities to actual cytoskeleton components. However, their production typically requires thermal annealing, which not only poses a general constraint on their potential applications but is also incompatible with physiological conditions. Here, we demonstrate that DNA nanotubes can self-assemble from a simple mixture of five short DNA strands at constant room temperature, growing for extended periods of time in bulk conditions as well as under confinement. Assembly is achieved using a monovalent salt buffer, which ensures a faithful nanoscale arrangement and avoids nanotube aggregation. We observe the formation of individual nanotubes up to 20 days with a diameter of 22 ± 4 nm and length of several tens of micrometers. We finally encapsulate the strands in microsized compartments, such as water-in-oil microdroplets and giant unilamellar vesicles serving as simple cell models. Notably, nanotubes not only isothermally self-assemble directly inside the microcompartments but also self-organize into dynamic higher-order structures resembling rings and dynamic networks. Our study provides an advantageous method for in situ assembly of programmable biomolecular scaffolds and materials using synthetic DNA strands without requirements of thermal treatment.

Chemistry↗
DOE OSTI2026

All-organic self-separating three-dimensionally nanoarchitected electrochemical energy storage devices

This work realizes a three-dimensionally (3D) nanoarchitected, all organic, "self-separating" lithium-ion electrochemical energy storage (EES) device that is cycled as a solid-state full cell. The device is enabled by a monolithic carbon anode with a co-continuous pore network, derived from the structure direction of resols by an ultra-large molar mass block copolymer (BCP), poly(styrene-block-2-dimethylaminoethyl methacrylate) (SA). Electropolymerization of a single-phase conductive and redox-active material, poly((2,3-dihydrothieno[3,4-b][1,4]dioxin-2-yl)methyl 9,10-dioxo-9,10-dihydroanthracene-2-carboxylate) (PAQEDOT), into the pore space provides the cathode of the cell. The device is electronically contacted to the relevant electrode network enabled by the co-continuous nature of each electrode. Electrochemical processing via cycling against external lithium in an electrolyte generates a solid electrolyte interphase (SEI) as a separator and lithiates the cell electrodes, after which the EES device is cycled in the solid state. While the full cell does not demonstrate high cyclability, the best full cell demonstrates a discharge capacity of 267 milliamp hours per gram (mAh/g). This work marks, to the best of knowledge of the authors, the first example of an all-organic materials derived 3D nanoarchitected EES device, as well as the first design of "self-separating" cell fabrication. Furthermore, generalization of the design to another co-continuous carbon form factor is demonstrated.

FOS: Physical sciences↗
DOE OSTI2022

Characterizing vertical upper ocean temperature structures in the European Arctic through unsupervised machine learning

In-situ observations of subsurface ocean temperatures are, in many regions, inconsistently distributed in time and space. These spatio-temporal inconsistencies in the observational network lead to difficulties in utilizing those observations effectively for ocean model evaluation or understanding larger-scale ocean characteristics. Model accuracy of subsurface ocean characteristics is especially important within regions that contain complex ocean structures. One such region is the European Arctic which not only contains several types of water masses with unique characteristics, but also wintertime sea ice coverage and complex bathymetry. This study presents an unsupervised neural networking technique that can be used in combination with traditional ocean model evaluation techniques to provide additional information on the accuracy of modeled vertical ocean temperature profiles. Self-organizing maps is an unsupervised machine learning technique that we apply to approximately twenty thousand Argo and CTD temperature profiles from 2012 to 2020 in the European Arctic to categorize the observed vertical ocean temperature structures in the top 150 m. The observed ocean profile categories, or neurons, defined by the self-organizing map show strong spatial and temporal dependencies. We then use the neuron weights, or the learned temperature profile structure of each neuron, to validate the spatial and temporal variability of modeled vertical temperature structures. This analysis gives us new insights about the model’s capabilities to reproduce specific vertical structures of the top-most ocean layer within different regions and seasons. Mapping modeled ocean temperature profiles onto the neuron-space of the observationally-defined self organized map highlights the potential of this method to advance our understanding of model deficiencies in that region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Programmable chiral states in flocks of active magnetic rollers

Inspired by nature, active matter exemplified by self-organization of motile units into macroscopic structures holds great promise for advanced tunable materials capable of flocking, shape-shifting, and self-healing. Active particles driven by external fields have repeatedly demonstrated potential for complex self-organization and collective behavior, yet how to guide the direction of their collective motion largely remains unexplored. Here, we report a system of microscopic ferromagnetic rollers driven by an alternating magnetic field that demonstrates programmable control of the direction of a self-organized coherent vortical motion (i.e., chirality). Facilitated by a droplet confinement, the rollers get synchronized and display either right- or left-handed spontaneous vortical motion, such that their moving direction determines the vortex chirality. Here, we reveal that one can remotely command a flock of magnetic rollers to switch or maintain its chiral state by modulating a phase shift of the sinusoidal magnetic field powering the active rollers. Building on our findings, we realize a self-assembled remotely controlled micro-pump architecture capable of switching the fluid transport direction on demand. Our studies may stimulate new design strategies for directed transport and flocking robotics at the microscale based on active colloids.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Plastics recycling via plasma-based depolymerization utilizing aqueous and gaseous discharge exposure

This proposed work aims to understand and quantify the effect of self-organization on polymer depolymerization in liquids. Here, the project will explore the role of the formation of large-scale plasma induced flows that occur under self- organization that leads to the convection of plastic particles (micron sized) to the plasma attachment regions. This convention should lead to enhanced transport and higher depolymerization rates. Also, associated with self-organization is the emission of charged droplets in the region of plasma attachment. A key question is do these droplets contain heavily broken up polymer chains or monomers? Is this emission another approach to collection of depolymerized media? These droplets are also charged and contains particles suggesting a means to control emitted media. Currently, plastics upcycling, the conversion of plastics to higher value products, is energy intensive due to high heat requirements (for example thermolysis). Plasma-based treatment offers a greener approach to the depolymerization of plastics and offers the possibility of upcycling to make higher value products such as higher-grade plastics and fuels.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

How earthquakes organize stress

Stress is not uniform in the Earth. Therefore, we must use natural experiments to measure the distribution of stresses and related quantities, rather than single values. For instance, dynamic triggering shows that faults are uniformly distributed over their loading cycles in Southern California. The probability that a fault ruptures across a barrier measures the in situ energy distribution. Fault roughness reflects the distribution of strength. These natural experiments produce observable distributions that are surprisingly consistent and suggest some degree of self-organization in the Earth’s crust. Once established, the functional form of the distributions can be used to track changes in response to earthquakes as well as to distinguish fundamentally different fault systems. Transient fault locking before stress release in laboratory experiments can be interpreted as a consequence of self-organization of fault stress. The robust self-organization of multiple variables in earthquake systems suggests that the most consequential mechanical outcome of earthquakes may be the redistribution of stress and the strain energy associated with it. The low friction on a fault during seismic slip as inferred by temperature measurements of the Tohoku earthquake is consistent with dissipation playing a secondary role to this redistribution process. Through stress redistribution and interaction, subduction zone faults tend to synchronize, perhaps due to their geometric simplicity, while the continental system of Southern California cannot synchronize, perhaps due to the complexity of the fault network. Earthquakes organize stress in the crust and produce a suite of well-defined, consistent distributions.

earthquakes↗
DOE OSTI2025

RCEMIP‐ACI: Aerosol‐Cloud Interactions in a Multimodel Ensemble of Radiative‐Convective Equilibrium Simulations

Aerosol‐cloud interactions are a persistent source of uncertainty in climate research. This study presents findings from a model intercomparison project examining the impact of aerosols on clouds and climate in convection‐permitting radiative‐convective equilibrium (RCE) simulations. Specifically, 11 different modeling teams conducted RCE simulations under varying aerosol concentrations, domain configurations, and sea surface temperatures (SSTs). We analyze the response of domain‐mean cloud and radiative properties to imposed aerosol concentrations across different SSTs. Additionally, we explore the potential impact of aerosols on convective aggregation and large‐scale circulation in large‐domain simulations. The results reveal that the cloud and radiative responses to aerosols vary substantially across models. However, a common trend across models, SSTs, and domain configurations is that increased aerosol loading tends to suppress warm rain formation, enhance cloud water content in the mid‐troposphere, and consequently increase mid‐tropospheric humidity and upper‐tropospheric temperature, thereby impacting static stability. The warming of the upper troposphere can be attributed to reduced lateral entrainment effects due to the higher environmental humidity in the mid‐troposphere. However, models do not agree on aerosol impacts on convective updraft velocity based on the preliminary examination of high‐percentiles of vertical velocity at a single mid‐troposheric layer (500 hPa). In large‐domain simulations, where convection tends to self‐organize, aerosol loading does not consistently influence self‐organization but tends to reduce the intensity of large‐scale circulation forming between convective clusters and dry regions. This reduction in circulation intensity can be explained by the increase in static stability due to the upper tropospheric warming.

54 ENVIRONMENTAL SCIENCES↗