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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Probing the impact of oxygen negative ions on the self-organized pattern in 1 atm DC glow with liquid anode

In an atmospheric DC glow discharge with a liquid anode, the plasma anode glow attached to the grounded liquid surface under certain conditions self-organizes into coherent patterns. Optical emission spectroscopy revealed that the emission consists primarily of the second positive system of nitrogen, N 2 (C-B), whose excitation energy is low and sensitive to changes in the electron energy distribution. In addition to electrons, negative ions can accumulate in the anode sheath and affect the local space charge. It has been speculated that these negative ions play a role in pattern formation at the anode surface. In this work, the role of oxygen negative ions was explored. It was found that the formation of anode patterns requires at least a 7% volume fraction of oxygen in the ambient gas. Results showed that O 2 - is the dominant negative ion species in atmospheric DC glow discharge, with a density of ~10 12 cm -3 . While the presence of oxygen appears to be crucial for pattern formation, this study indicated that patterns still formed without geometric changes even when 62% of negative ions in the plasma were detached by a laser. This suggests that negative ions do not support the patterns, while oxygen's heating effect may induce instability at the anode.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1992

Self-organized rock textures and multiring structure in the Duolun crater

The Duolun impact crater is a multiring basin located 200 km north of Beijing. From the center to the edge of the crater there are innermost rim, inner ring, outer rim, and outermost ring. Recently, we have found some self-organized textures or chaos phenomena in shock-metamorphic rocks from the Duolun impact crater, such as turbulence in matrices of impact glass, oscillatory zoning, or chemical chaos of spherulites in spherulitic splashed breccia, fractal wavy textures or self-similar wavy textures with varied scaling in impact glass, and crystallite beams shaped like Lorentz strange attractors. The rare phenomena indicate that the shock-metamorphic rocks from Duolun crater are formed far from equilibrium. If impact cratering generates momentarily under high-pressure and superhigh-temperature, occurrence of those chaos phenomena in shock-metamorphic rock is not surprising.

Wu, Siben↗
DOE OSTI2026

Dark Energy Survey Year 6 results: Clustering redshifts and importance sampling of self-organized-maps 𝑛⁡(𝑧) realizations for 3 × 2 ⁢pt samples

This work is part of a series establishing the redshift framework for the 3 × 2 ⁢pt analysis of the Dark Energy Survey Year 6 (DES Y6). For DES Y6, photometric redshift distributions are estimated using self-organizing maps (SOMs), calibrated with spectroscopic and many-band photometric data. To overcome limitations from color-redshift degeneracies and incomplete spectroscopic coverage, we enhance this approach by incorporating clustering-based redshift constraints (clustering-z, or WZ) from angular cross-correlations with BOSS and eBOSS galaxies and eBOSS quasar samples. We define a WZ likelihood and apply importance sampling to a large ensemble of SOM-derived 𝑛⁡(𝑧) realizations, selecting those consistent with the clustering measurements to produce a posterior sample for each lens and source bin. The analysis uses angular scales corresponding to 1.5–5 Mpc to optimize signal-to-noise ratio while mitigating modeling uncertainties and marginalizes over redshift-dependent galaxy bias and other systematics informed by the N-body simulation CARDINAL . While a sparser spectroscopic reference sample limits WZ constraining power at 𝑧 >1.1, particularly for source bins, we demonstrate that combining SOM with WZ improves redshift accuracy and enhances the overall cosmological constraining power of DES Y6. As a result, we estimate an improvement in 𝑆 8 of approximately 10% for cosmic shear and 3 ×2⁢pt analysis, primarily due to the WZ calibration of the source samples.

Cosmological parameters↗
NASA NTRS1991

Self-organization and clustering algorithms

Kohonen's feature maps approach to clustering is often likened to the k or c-means clustering algorithms. Here, the author identifies some similarities and differences between the hard and fuzzy c-Means (HCM/FCM) or ISODATA algorithms and Kohonen's self-organizing approach. The author concludes that some differences are significant, but at the same time there may be some important unknown relationships between the two methodologies. Several avenues of research are proposed.

Bezdek, James C.↗
NASA NTRS1992

Self-organization of cosmic radiation pressure instability. II - One-dimensional simulations

The clustering of statistically uniform discrete absorbing particles moving solely under the influence of radiation pressure from uniformly distributed emitters is studied in a simple one-dimensional model. Radiation pressure tends to amplify statistical clustering in the absorbers; the absorbing material is swept into empty bubbles, the biggest bubbles grow bigger almost as they would in a uniform medium, and the smaller ones get crushed and disappear. Numerical simulations of a one-dimensional system are used to support the conjecture that the system is self-organizing. Simple statistics indicate that a wide range of initial conditions produce structure approaching the same self-similar statistical distribution, whose scaling properties follow those of the attractor solution for an isolated bubble. The importance of the process for large-scale structuring of the interstellar medium is briefly discussed.

Hogan, Craig J.↗
NASA NTRS2005

Self-Organizing-Map Program for Analyzing Multivariate Data

SOM_VIS is a computer program for analysis and display of multidimensional sets of Earth-image data typified by the data acquired by the Multi-angle Imaging Spectro-Radiometer [MISR (a spaceborne instrument)]. In SOM_VIS, an enhanced self-organizing-map (SOM) algorithm is first used to project a multidimensional set of data into a nonuniform three-dimensional lattice structure. The lattice structure is mapped to a color space to obtain a color map for an image. The Voronoi cell-refinement algorithm is used to map the SOM lattice structure to various levels of color resolution. The final result is a false-color image in which similar colors represent similar characteristics across all its data dimensions. SOM_VIS provides a control panel for selection of a subset of suitably preprocessed MISR radiance data, and a control panel for choosing parameters to run SOM training. SOM_VIS also includes a component for displaying the false-color SOM image, a color map for the trained SOM lattice, a plot showing an original input vector in 36 dimensions of a selected pixel from the SOM image, the SOM vector that represents the input vector, and the Euclidean distance between the two vectors.

Li, P. Peggy↗
NASA NTRS2016

Atmospheric Drivers of Greenland Surface Melt Revealed by Self-Organizing Maps

Recent acceleration in surface melt on the Greenland ice sheet (GrIS) has occurred concurrently with a rapidly warming Arctic and has been connected to persistent, anomalous atmospheric circulation patterns over Greenland. To identify synoptic setups favoring enhanced GrIS surface melt and their decadal changes, we develop a summer Arctic synoptic climatology by employing self-organizing maps. These are applied to daily 500 hPa geopotential height fields obtained from the Modern Era Retrospective Analysis for Research and Applications reanalysis, 1979-2014. Particular circulation regimes are related to meteorological conditions and GrIS surface melt estimated with outputs from the Modèle Atmosphérique Régional. Our results demonstrate that the largest positive melt anomalies occur in concert with positive height anomalies near Greenland associated with wind, temperature, and humidity patterns indicative of strong meridional transport of heat and moisture. We find an increased frequency in a 500 hPa ridge over Greenland coinciding with a 63% increase in GrIS melt between the 1979-1988 and 2005-2014 periods, with 75.0% of surface melt changes attributed to thermodynamics, 17% to dynamics, and 8.0% to a combination. We also confirm that the 2007-2012 time period has the largest dynamic forcing relative of any period but also demonstrate that increased surface energy fluxes, temperature, and moisture separate from dynamic changes contributed more to melt even during this period. This implies that GrIS surface melt is likely to continue to increase in response to an ever warmer future Arctic, regardless of future atmospheric circulation patterns.

anomalies↗
NASA NTRS2006

Automated Classification of Thermal Infrared Spectra Using Self-organizing Maps

Existing and planned space missions to a variety of planetary and satellite surfaces produce an ever increasing volume of spectral data. Understanding the scientific informational content in this large data volume is a daunting task. Fortunately various statistical approaches are available to assess such data sets. Here we discuss an automated classification scheme based on Kohonen Self-organizing maps (SOM) we have developed. The SUM process produces an output layer were spectra having similar properties lie in close proximity to each other. One major effort is partitioning this output layer into appropriate regions. This is prefonned by defining dosed regions based upon the strength of the boundaries between adjacent cells in the SOM output layer. We use the Davies-Bouldin index as a measure of the inter-class similarities and intra-class dissimilarities that determines the optimum partition of the output layer, and hence number of SOM clusters. This allows us to identify the natural number of clusters formed from the spectral data. Mineral spectral libraries prepared at Arizona State University (ASU) and John Hopkins University (JHU) are used to test and evaluate the classification scheme. We label the library sample spectra in a hierarchical scheme with class, subclass, and mineral group names. We use a portion of the spectra to train the SOM, i.e. produce the output layer, while the remaining spectra are used to test the SOM. The test spectra are presented to the SOM output layer and assigned membership to the appropriate cluster. We then evaluate these assignments to assess the scientific meaning and accuracy of the derived SOM classes as they relate to the labels. We demonstrate that unsupervised classification by SOMs can be a useful component in autonomous systems designed to identify mineral species from reflectance and emissivity spectra in the therrnal IR.

Roush, Ted L.↗
DOE OSTI2020

Kinetic effects of self-organization in low-temperature magnetized plasmas (Final Report for DE-SC0019045: 9/1/2018-8/31/2019)

The research goal of this Early Career Research Program project is to advance the science of low temperature magnetized plasmas using first-principles kinetic, e.g., solving the Boltzmann equation, models. A better understanding of the plasma transport properties, such as electrical resistivity and thermal conductivity must be obtained to better control and design magnetized plasma sources. A key physical phenomenon in low-temperature magnetized plasmas is the self-organization of plasma flows, e.g., coherent plasma structures and transition between different discharge modes. Understanding the physics of coherent plasma structures has been identified as one of the four important frontier topics in a recent workshop by the Office of Fusion Energy Science. These self-emerging plasma patterns results from the nonlinear coupling between plasma constituents (ions, electrons, and neutral atoms), electromagnetic fields, and materials. The physics of low-temperature magnetized plasmas is particularly complex because (i) the collisionless instabilities affect collisional phenomena (ionization, transport, etc.) and vice versa and (ii) the presence of materials (sheath, electron injection, etc.) influences the bulk plasma properties in low-temperature plasma (LTP) devices. Computational models of these plasma flows remain challenging because the plasma density can vary several orders of magnitudes, a wide range of temporal and spatial scales must be resolved, the phenomena are inherently three-dimensional, and various physical processes, such as wall interaction, instabilities, inelastic collisions, etc., must be simultaneously and self-consistently taken into account. Understanding the fundamental transport mechanisms in cross-field configurations enables additional controllability of plasma properties and electron energy distribution functions (EEDFs).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Self-Organized Stress Distributions in Polycrystalline Materials [Dissertation]

Understanding stress distributions in solid materials is complicated by the fact that most materials are polycrystalline in nature, with each crystal having an elastically anisotropic reaction to force. This study is to gain a better understanding on how external forces placed on a polycrystal are related to internal reactions within and between the grains. The hypothesis is that the stress distribution in porous to fully dense materials are self-organized based on strong contacts between and within the individual grains created by force chains. Force chains, commonly known in loaded granular materials, and could be the phenomenon that connect micro to macro deformation. Scale bridging measurements conducted through Raman spectroscopy, Atomic Force Microscopy, and Digital Image Correlation will be used to create stress maps, modulus maps, and elastic strain maps across a variety of geological and pharmaceutical polycrystals. When possible, the resulting maps will be compared to current homogenization schemes and a full field models. Finite element modeling will be used to assess whether the patterning seen in the experimental map is a reasonable approximation based on the orientation data of the samples used. A minimum of three publications is projected to be accomplished focusing each on a different method to experimentally test and analyze stress distributions.

36 MATERIALS SCIENCE↗
NASA NTRS1993

Self-organization via active exploration in robotic applications. Phase 2: Hybrid hardware prototype

In many environments human-like intelligent behavior is required from robots to assist and/or replace human operators. The purpose of these robots is to reduce human time and effort in various tasks. Thus the robot should be robust and as autonomous as possible in order to eliminate or to keep to a strict minimum its maintenance and external control. Such requirements lead to the following properties: fault tolerance, self organization, and intelligence. A good insight into implementing these properties in a robot can be gained by considering human behavior. In the first phase of this project, a neural network architecture was developed that captures some fundamental aspects of human categorization, habit, novelty, and reinforcement behavior. The model, called FRONTAL, is a 'cognitive unit' regulating the exploratory behavior of the robot. In the second phase of the project, FRONTAL was interfaced with an off-the-shelf robotic arm and a real-time vision system. The components of this robotic system, a review of FRONTAL, and simulation studies are presented in this report.

Oegmen, Haluk↗
NASA NTRS1991

Self-organization of cosmic radiation pressure instability

Under some circumstances the absorption of radiation momentum by an absorbing medium opens the possibility of a dynamical instability, sometimes called 'mock gravity'. Here, a simplified abstract model is studied in which the radiation source is assumed to remain spatially uniform, there is no reabsorption or reradiated light, and no forces other than radiative pressure act on the absorbing medium. It is shown that this model displays the unique feature of being not only unstable, but also self-organizing. The structure approaches a statistical dynamical steady state which is almost independent of initial conditions. In this saturated state the absorbers are concentrated in thin walls around empty bubbles; as the instability develops the big bubbles get bigger and the small ones get crushed and disappear. A linear analysis shows that to first order the thin walls are indeed stable structures. It is speculated that this instability may play a role in forming cosmic large-scale structure.

Hogan, Craig J.↗
NASA NTRS1993

Self-Organized Criticality and Phase Problem in Magnetoaocustic Emission Burst in Ferromagnets

Two types of ferromagnets, pure iron and steel with varying geometry and microstructural properties, were prepared for the present study of magnetoacoustic emission (MAE). The purpose was to separate the effects of structural property variations from changes caused by differences in the sample geometry. The position and shape of the leading MAE sub-burst and its variation among the samples are explained by magnetic anisotropy and the results of numerical simulations which utilized the concept of self-organized criticality (SOC). The amplitude and duration of the second sub-burst, which previously was thought to occur as a result of a complicated interaction between non-180 deg domain walls and lattice defects, can easily be explained by the results of our simulation.

Namkung, M.↗
DOE OSTI2021

Visualization and analysis of coupling between plasmas self-organization and plasma-induced fluid circulation in 1 atm DC glows with liquid anode

The physical processes prevailing when plasma contacts liquid water are poorly understood; however, it is this very interaction that is the basis of new, exciting technologies for water purification, the treatment of cancer and disease and the production of new, high value chemical products. A chief impediment to widespread application of plasmas for the aforementioned applications is scale up and cost. Here we investigate plasma induced flows which facilitate transport of reactivity from the plasma to liquid water at rates much faster than diffusion. We find that this plasma induced motion varies depending on whether or not the plasma is self organized. The insight from this work paves the way to a better understanding of transport and ultimately informing plasma treatment technologies on how to best optimize the rate at which processes such as plasma based water purification occurs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Connecting Large‐Scale Meteorological Patterns to Extratropical Cyclones in CMIP6 Climate Models Using Self‐Organizing Maps

Abstract Extratropical cyclones (ETCs) are responsible for the majority of cool‐season extreme events in the northeastern United States (NEUS), often leading to high‐impact weather conditions that can have wide‐ranging socioeconomic impacts. Evaluating the ability of climate models to adequately simulate ETC dynamics is essential for improving model performance and increasing confidence in future projections used by stakeholders and policymakers. ETCs are traditionally studied using techniques such as case studies and synoptic typing, however, these approaches can be time‐consuming, require subjective analysis, and do not necessarily identify the coincident large‐scale meteorological patterns (LSMPs). Here, we apply self‐organizing maps (SOMs) as an automated machine‐learning approach to characterize the LSMPs and associated frequency and intensity of discrete ETC events over NEUS. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during the last four decades. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity (ACA) associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMPs and ETC events identified in the SOM. Our results identify a robust bias toward more zonal patterns, with models struggling to reproduce the more amplified patterns typically associated with the highest cyclone activity. While model resolution has some impact on simulation credibility, model configuration appears to be more important in LSMP representation. The vast majority of CMIP6 models produce too few ETCs, although model errors are distributed around historical reanalyses when ACA is normalized by storm frequency.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

A new approach for designing self-organizing systems and application to adaptive control

There is tremendous interest in the design of intelligent machines capable of autonomous learning and skillful performance under complex environments. A major task in designing such systems is to make the system plastic and adaptive when presented with new and useful information and stable in response to irrelevant events. A great body of knowledge, based on neuro-physiological concepts, has evolved as a possible solution to this problem. Adaptive resonance theory (ART) is a classical example under this category. The system dynamics of an ART network is described by a set of differential equations with nonlinear functions. An approach for designing self-organizing networks characterized by nonlinear differential equations is proposed.

Ramamoorthy, P. A.↗
DOE OSTI2021

Iterative self-organizing SCEne-LEvel sampling (ISOSCELES) for large-scale building extraction

Convolutional neural networks (CNN) provide state-of-the-art performance in many computer vision tasks, including those related to remote-sensing image analysis. Successfully training a CNN to generalize well to unseen data, however, requires training on samples that represent the full distribution of variation of both the target classes and their surrounding contexts. With remote sensing data, acquiring a sufficiently representative training set is a challenge due to both the inherent multi-modal variability of satellite or aerial imagery and the general high cost of labeling data. To address this challenge, we have developed ISOSCELES, an Iterative Self-Organizing SCEne LEvel Sampling method for hierarchical sampling of large image sets. Using affinity propagation, ISOSCELES automates the selection of highly representative training images. Compared to random sampling or using available reference data, the distribution of the training is principally data driven, reducing the chance of oversampling uninformative areas or undersampling informative ones. In comparison to manual sample selection by an analyst, ISOSCELES exploits descriptive features, spectral and/or textural, and eliminates human bias in sample selection. Using a hierarchical sampling approach, ISOSCELES can obtain a training set that reflects both between-scene variability, such as in viewing angle and time of day, and within-scene variability at the level of individual training samples. We verify the method by demonstrating its superiority to stratified random sampling in the challenging task of adapting a pre-trained model to a new image and spatial domain for country-scale building extraction. Using a pair of hand-labeled training sets comprising 1,987 sample image chips, a total of 496,000,000 individually labeled pixels, we show, across three distinct model architectures, an increase in accuracy, as measured by F1-score, of 2.2–4.2%.

42 ENGINEERING↗
DOE OSTI2020

Role of sheared E × B flow in self-organized, improved confinement states in magnetized plasmas

A great scientific success story of magnetic fusion research in the past several decades is the theoretical development and experimental testing of the process of turbulence decorrelation and stabilization by sheared E×B flow which shows that E×B shear effects are ubiquitous in magnetized plasmas. This concept of turbulence decorrelation and stabilization has the universality needed to explain the H-mode edge transport barriers seen in limiter and divertor tokamaks, stellarators, and mirror machines; the broader edge transport barrier seen in VHmode plasmas; and the core transport barriers formed in tokamaks. Similar effects are seen in linear devices. These examples of confinement improvement are of considerable physical interest; it is not often that a system self-organizes to reduce transport when an additional source of free energy is applied to it. The transport decrease associated with E×B velocity shear is also of great practical benefit to fusion research, since it contributed to substantially increased fusion yield in all DT magnetic fusion experiments conducted to date. The fundamental physics involved in transport reduction is the effect of E×B shear on the growth, radial extent, and phase correlation of turbulent eddies in the plasma. The same basic transport reduction process can be operational in various portions of the plasma because there are a number of ways to change the radial electric field Er. An important secondary theme in this area is the synergistic effect of E×B velocity shear and magnetic shear. Although the E×B velocity shear appears to have an effect on broader classes of microturbulence, magnetic shear can mitigate some potentially harmful effects of E×B velocity shear and facilitate turbulence stabilization. Our present understanding in this area is the result of a multi-decade, intertwined effort in theory, modeling and diagnostic development combined with continuing experimental investigations. These experiments have clearly demonstrated that increased E×B shear causes reductions in turbulence and transport. Finally, the experimental findings are generally consistent with the basic theoretical models although considerable work remains to be done before we have a fully predictive theory of transport in magnetized plasmas including E×B shear effects.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗