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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Ultraviolet and infrared laser-induced fragmentation of free (CF{sub 3}I){sub n} clusters in a molecular beam and (CF{sub 3}I){sub n} clusters inside or on the surface of large (Xe){sub m} clusters

The fragmentation of free homogeneous (CF{sub 3}I){sub n} clusters in a molecular beam (n ≤ 45 is the average number of molecules in the cluster) and (CF{sub 3}I){sub n} clusters inside or on the surface of large (Xe){sub m} clusters (m ≥ 100 is the average number of atoms in the cluster) by ultraviolet and infrared laser radiations has been studied. These three types of (CF{sub 3}I){sub n} clusters are shown to have different stabilities with respect to fragmentation by both ultraviolet and infrared radiations and completely different dependences of the fragmentation probability on the energy of ultraviolet and infrared radiations. When exposed to ultraviolet radiation, the free (CF{sub 3}I){sub n} clusters fragment at comparatively low fluences (Φ{sub UV} ≤ 0.15 J cm{sup −2}) and the weakest energy dependence of the fragmentation probability is observed for them. A stronger energy dependence of the fragmentation probability is observed for the (CF{sub 3}I){sub n} clusters localized inside (Xe){sub m} clusters, and the strongest dependence is observed for the (CF{sub 3}I){sub n} clusters located on the surface of (Xe){sub m} clusters. When the clusters are exposed to infrared radiation, the homogeneous (CF{sub 3}I){sub n} clusters efficiently fragment at low fluences (Φ{sub IR} ≤ 25 mJ cm{sup −2}), higher fluences (Φ{sub IR} ≈ 75 mJ cm{sup −2}) are needed for the fragmentation of the (CF{sub 3}I){sub n} localized inside (Xe){sub m} clusters, and even higher fluences (Φ{sub IR} ≈ 150 mJ cm{sup −2}) are needed for the fragmentation of the (CF{sub 3}I){sub n} clusters located on the surface of (Xe){sub m} clusters. It has been established that small (CF{sub 3}I){sub n} clusters located on the surface of (Xe){sub m} clusters do not fragment up to fluences Φ{sub IR} ≈ 250 mJ cm{sup −2}. The fragmentation efficiency of (CF{sub 3}I){sub n} clusters is shown to be the same (at the same fluence) when they are excited by both pulsed (τ{sub p} ≈ 150 ns) and continuous-wave infrared laser radiations. Possible causes of such a pattern of ultraviolet and infrared laser-induced fragmentation of these clusters are discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Microbial Community Structure and the Persistence of Cyanobacterial Populations in Salt Crusts of the Hyperarid Atacama Desert from Genome-Resolved Metagenomics

Although once thought to be devoid of biology, recent studies have identified salt deposits as oases for life in the hyperarid Atacama Desert. To examine spatial patterns of microbial species and key nutrient sources, we genomically characterized 26 salt crusts from three sites along a fog gradient. The communities are dominated by a large variety of Halobacteriales and Bacteroidetes, plus a few algal and Cyanobacterial species. CRISPR locus analysis suggests the distribution of a single Cyanobacterial population among all sites. This is in stark contrast to the extremely high sample specificity of most other community members. Only present at the highest moisture site is a genomically characterized Thermoplasmatales archaeon (Marine Group II) and six Nanohaloarchaea, one of which is represented by a complete genome. Parcubacteria (OD1) and Saccharibacteria (TM7), not previously reported from hypersaline environments, were found at low abundances. We found no indication of a N 2 fixation pathway in the communities, suggesting acquisition of bioavailable nitrogen from atmospherically derived nitrate. Samples cluster by site based on bacterial and archaeal abundance patterns and photosynthetic capacity decreases with increasing distance from the ocean. We conclude that moisture level, controlled by coastal fog intensity, is the strongest driver of community membership.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Machine learning reveals cyclic changes in seismic source spectra in Geysers geothermal field

The earthquake rupture process comprises complex interactions of stress, fracture, and frictional properties. New machine learning methods demonstrate great potential to reveal patterns in time-dependent spectral properties of seismic signals and enable identification of changes in faulting processes. Clustering of 46,000 earthquakes of 0.3 < ML < 1.5 from the Geysers geothermal field (CA) yields groupings that have no reservoir-scale spatial patterns but clear temporal patterns. Events with similar spectral properties repeat on annual cycles within each cluster and track changes in the water injection rates into the Geysers reservoir, indicating that changes in acoustic properties and faulting processes accompany changes in thermomechanical state. The methods open new means to identify and characterize subtle changes in seismic source properties, with applications to tectonic and geothermal seismicity.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Comparison of Clustering Techniques for Residential Energy Behavior using Smart Meter Data

Current practice in whole time series clustering of residential meter data focuses on aggregated or subsampled load data at the customer level, which ignores day-to-day differences within customers. This information is critical to determine each customer’s suitability to various demand side management strategies that support intelligent power grids and smart energy management. Clustering daily load shapes provides fine-grained information on customer attributes and sources of variation for subsequent models and customer segmentation. In this paper, we apply 11 clustering methods to daily residential meter data. We evaluate their parameter settings and suitability based on 6 generic performance metrics and post-checking of resulting clusters. Finally, we recommend suitable techniques and parameters based on the goal of discovering diverse daily load patterns among residential customers. To the authors’ knowledge, this paper is the first robust comparative review of clustering techniques applied to daily residential load shape time series in the power systems’ literature.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2012

An Independent Genome Duplication Inferred from Hox Paralogs in the American Paddlefish—A Representative Basal Ray-Finned Fish and Important Comparative Reference

Vertebrates have experienced two rounds of whole-genome duplication (WGD) in the stem lineages of deep nodes within the group and a subsequent duplication event in the stem lineage of the teleosts—a highly diverse group of ray-finned fishes. Here, we present the first full Hox gene sequences for any member of the Acipenseriformes, the American paddlefish, and confirm that an independent WGD occurred in the paddlefish lineage, approximately 42 Ma based on sequences spanning the entire HoxA cluster and eight genes on the HoxD gene cluster. These clusters comprise different HOX loci and maintain conserved synteny relative to bichir, zebrafish, stickleback, and pufferfish, as well as human, mouse, and chick. We also provide a gene genealogy for the duplicated fzd8 gene in paddlefish and present evidence for the first Hox14 gene in any ray-finned fish. Taken together, these data demonstrate that the American paddlefish has an independently duplicated genome. Substitution patterns of the “alpha” paralogs on both the HoxA and HoxD gene clusters suggest transcriptional inactivation consistent with functional diploidization. Further, there are similarities in the pattern of sequence divergence among duplicated Hox genes in paddlefish and teleost lineages, even though they occurred independently approximately 200 Myr apart. We highlight implications on comparative analyses in the study of the “fin-limb transition” as well as gene and genome duplication in bony fishes, which includes all ray-finned fishes as well as the lobe-finned fishes and tetrapod vertebrates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Characterization of Surface Heterogeneity-Induced Convection Using Cluster Analysis

Land-atmosphere interactions and boundary layer processes often control the formation of shallow clouds and subsequently deep convective precipitation over the Southern Great Plains. In this study, we examine the impacts of large-scale advection on the cloud populations and land-atmospheric coupling observed during the Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) field campaign in 2016. In this work, we performed two Large Eddy Simulations (LES) using the Weather Research and Forecasting Model for a day in which the transition from clear-sky to shallow precipitating clouds and isolated deep convection was observed. The control simulation reproduced the overall distribution of cloud populations by using realistic soil conditions with an interactive land model. In the sensitivity simulation where large-scale advection is removed, a strong relationship between the land and boundary layer is found. To study the timing, location, and intensity of convective initiation and the relationship of clouds with land surface properties, a cluster analysis of equivalent potential temperature is performed for the simulation without large-scale advection. That analysis shows that convective clouds first form over regions with higher surface sensible heat flux. Precipitation from those convective clouds likely triggers new updrafts nearby about two hours later through the lifting associated with cold pools. Furthermore, the cluster analysis also shows that in addition to the spatial pattern of soil moisture, land use and soil texture in western Oklahoma also influence the location of convective initiation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Evidence of Specific MJO Phase Occurrence with Summertime California Central Valley Extreme Hot Weather

This study examines associations between California Central Valley (CCV) heat waves and the Madden Julian Oscillation (MJO). These heat waves have major economic impact. Our prior work showed that CCV heat waves are frequently preceded by convection over the tropical Indian and eastern Pacific oceans, in patterns identifiable with MJO phases. The main analysis method is lagged composites (formed after each MJO phase pair) of CCV synoptic station temperature, outgoing longwave radiation (OLR), and velocity potential (VP). Over the CCV, positive temperature anomalies occur only after the Indian Ocean (phases 2–3) or eastern Pacific Ocean (phases 8–1) convection (implied by OLR and VP fields). The largest fractions of CCV hot days occur in the two weeks after onset of those two phase pairs. OLR and VP composites have significant subsidence and convergence above divergence over the CCV during heat waves, and these structures are each part of larger patterns having significant areas over the Indian and Pacific Oceans. Prior studies showed that CCV heat waves can be roughly grouped into two clusters: Cluster 2 is preceded by a heat wave over northwestern North America, while Cluster 1 is not. OLR and VP composite analyses are applied separately to these two clusters. However, for Cluster 2, the subsidence and VP over the CCV are not significant, and the large-scale VP pattern has low correlation with the MJO lagged composite field. Therefore, the association between the MJO convection and subsequent CCV heat wave is more evident in Cluster 1 than Cluster 2.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Typical hot water draw patterns based on field data

There is significant variation in hot water use and draw patterns among households. This report describes typical hot water use patterns in single-family residences in North America. We found that daily hot water use is highly variable both among residences and within the same residence. We compared the results of our analysis of the field data to the conditions and draw patterns established in the current U.S. Department of Energy (DOE) test procedure for residential water heaters. The results show a higher number of smaller draws at lower flow rates than used in the test procedure. The data from which the draw patterns were developed were obtained from 12 separate field studies. This report describes the ways in which we managed, cleaned, and analyzed the data and the results of our data analysis. After preparing the data, we used the complete data set to analyze inlet and outlet water temperatures. Then we divided the data into three clusters reflecting house configurations that demonstrated small, medium, or large median daily hot water use. We developed the three clusters partly to reflect efforts of the ASHRAE standard project committee (SPC) 118.2 to revise the test procedure for residential water heaters to incorporate a range of draw patterns. ASHRAE SPC 118.2 has identified the need to separately evaluate at least three, and perhaps as many as five, different water heater capacities. We analyzed the daily hot water use data within each cluster in terms of volume and number of hot water draws. The daily draw patterns in each cluster were characterized using distributions for volume of draws, duration of draws, time since previous draw, and flow rates.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Construction of Reconfigurable and Polymorphic DNA Origami Assemblies with Coiled‐Coil Patches and Patterns

Abstract DNA origami nanodevices achieve programmable structure and tunable mechanical and dynamic properties by leveraging the sequence‐specific interactions of nucleic acids. Previous advances have also established DNA origami as a useful building block to make well‐defined micron‐scale structures through hierarchical self‐assembly, but these efforts have largely leveraged the structural features of DNA origami. The tunable dynamic and mechanical properties also provide an opportunity to make assemblies with adaptive structures and properties. Here the integration of DNA origami hinge nanodevices and coiled‐coil peptides are reported into hybrid reconfigurable assemblies. With the same dynamic device and peptide interaction, it is made multiple higher‐order assemblies (i.e., polymorphic assembly) by organizing clusters of peptides into patches or arranging single peptides into patterns on the surfaces of DNA origami to control the relative orientation of devices. The coiled‐coil interactions are used to construct circular and linear assemblies whose structure and mechanical properties can be modulated with DNA‐based reconfiguration. Reconfiguration of linear assemblies leads to micron scale motions and ≈2.5‐10‐fold increase in bending stiffness. The results provide a foundation for stimulus‐responsive hybrid assemblies that can adapt their structure and properties in response to nucleic acid, peptide, protein, or other triggers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Subseasonal Representation and Predictability of North American Weather Regimes Using Cluster Analysis

Abstract This study focuses on assessing the representation and predictability of North American weather regimes, which are persistent large-scale atmospheric patterns, in a set of initialized subseasonal reforecasts created using the Community Earth System Model, version 2 (CESM2). The k -means clustering was used to extract four key North American (10°–70°N, 150°–40°W) weather regimes within ERA5 reanalysis, which were used to interpret CESM2 subseasonal forecast performance. Results show that CESM2 can recreate the climatology of the four main North American weather regimes with skill but exhibits biases during later lead times with overoccurrence of the West Coast high regime and underoccurrence of the Greenland high and Alaskan ridge regimes. Overall, the West Coast high and Pacific trough regimes exhibited higher predictability within CESM2, partly related to El Niño. Despite biases, several reforecasts were skillful and exhibited high predictability during later lead times, which could be partly attributed to skillful representation of the atmosphere from the tropics to extratropics upstream of North America. The high predictability at the subseasonal time scale of these case-study examples was manifested as an “ensemble realignment,” in which most ensemble members agreed on a prediction despite ensemble trajectory dispersion during earlier lead times. Weather regimes were also shown to project distinct temperature and precipitation anomalies across North America that largely agree with observational products. This study further demonstrates that unsupervised learning methods can be used to uncover sources and limits of subseasonal predictability, along with systematic biases present in numerical prediction systems. Significance Statement North American weather regimes are large-scale atmospheric patterns that can persist for several days. Their skillful subseasonal (2 weeks or greater) prediction can provide valuable lead time to prepare for temperature and precipitation anomalies that can stress energy and water resources. The purpose of this study was to assess the climatological representation and subseasonal predictability of four key North American weather regimes using a research subseasonal prediction system and clustering analysis. We found that the Pacific trough and West Coast high regimes exhibited higher predictability than other regimes and that skillful representation of conditions across the tropics and extratropics can increase predictability during later lead times. Future work will quantify causal pathways associated with high predictability.

58 GEOSCIENCES↗
DOE OSTI2025

An atomic cluster expansion potential for twisted multilayer graphene

Twisted multilayer graphene, characterized by its moiré patterns arising from inter-layer rotational misalignment, serves as a rich platform for exploring quantum phenomena. Machine learning interatomic potentials (MLIPs) are a promising approach to model such systems. Our work develops a method to generate training and test datasets for fitting MLIPs that capture all possible misalignments but remain small-scale to facilitate efficient data generation and parameter estimation. To achieve this, we generate configurations with periodic boundary conditions suitable for density functional theory calculations, and then introduce an internal twist and shift within those supercell structures. Using this technique, supplemented with an active learning workflow, we fit an Atomic Cluster Expansion potential for simulating twisted multilayer graphene and test it for accuracy and robustness on a range of simulation tasks.

2D materials↗
NASA NTRS1988

On infall into the Virgo cluster

The history of the study of the Galaxy's motion with respect to the Local Supercluster is reviewed. Direct measurements of the infall pattern suggest an infall component at the Local Group of about 250 km/s. This value is consistent with that expected if the average mass-to-light ratio of galaxies is similar to that dynamically measured in galaxy clusters, a value that gives an Omega of only about 0.3. The infall pattern is also consistent with galaxy density maps produced by IRAS surveys, although it may be only marginally consistent with the 'Great Attractor'. On smaller scales, there is strong evidence that most of the spiral galaxies near the cluster core are still falling in. Using the best distance estimates to the Virgo cluster, the local value of the Hubble constant is about 85 km/s/Mpc.

Huchra, John P.↗
DOE OSTI2012

THE ROLE OF THERMOHALINE MIXING IN INTERMEDIATE- AND LOW-METALLICITY GLOBULAR CLUSTERS

It is now widely accepted that globular cluster red giant branch (RGB) stars owe their strange abundance patterns to a combination of pollution from progenitor stars and in situ extra mixing. In this hybrid theory a first generation of stars imprints abundance patterns into the gas from which a second generation forms. The hybrid theory suggests that extra mixing is operating in both populations and we use the variation of [C/Fe] with luminosity to examine how efficient this mixing is. We investigate the observed RGBs of M3, M13, M92, M15, and NGC 5466 as a means to test a theory of thermohaline mixing. The second parameter pair M3 and M13 are of intermediate metallicity and our models are able to account for the evolution of carbon along the RGB in both clusters, although in order to fit the most carbon-depleted main-sequence stars in M13 we require a model whose initial [C/Fe] abundance leads to a carbon abundance lower than is observed. Furthermore, our results suggest that stars in M13 formed with some primary nitrogen (higher C+N+O than stars in M3). In the metal-poor regime only NGC 5466 can be tentatively explained by thermohaline mixing operating in multiple populations. We find thermohaline mixing unable to model the depletion of [C/Fe] with magnitude in M92 and M15. It appears as if extra mixing is occurring before the luminosity function bump in these clusters. To reconcile the data with the models would require first dredge-up to be deeper than found in extant models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

The association between exposure to environmental factors and the occurrence of attention-deficit/hyperactivity disorder (ADHD). A population-based retrospective cohort study

Highlights: • We found a certain north-south pattern for the risk of occurrence of ADHD. • These clusters could be related to some of the environmental variables. • In particular, an increased risk of occurrence of ADHD was found. • Living less than 100 m from an agricultural area or a residential Street. • Living less than 300 m from a motorway/dual carriageway or an industrial estate. A number of factors contribute to attention deficit hyperactivity disorder (ADHD) and although they are not fully known, the occurrence of ADHD seems to be a consequence of an interaction between multiple genetic and environmental factors. However, apart from pesticides, the evidence is inadequate and inconsistent as it differs not only in the population and time period analysed, but also in the type of study, the control of the confounding variables and the statistical methods used. In the latter case, the studies also differ in the adjustment of spatial and temporal variability. Our objective here, is to provide evidence on an association between environmental factors and ADHD.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Cooling rate and size effects on the medium-range structure of multicomponent oxide glasses simulated by molecular dynamics

A set of molecular dynamics simulations were performed to investigate the effect of cooling rate and system size on the medium-range structure of melt-derived multicomponent silicate glasses, represented by the quaternary 45S5 Bioglass composition. Given the significant impact of the glass degradation on applications of these materials in biomedicine and nuclear waste disposal, bulk structural features which directly affect the glass dissolution process are of particular interest. Connectivity of the silicate matrix, ion clustering and nanosegregation, distribution of ring and chain structural patterns represent critical features in this context, which can be directly extracted from the models. A key issue is represented by the effect of the computational approach on the corresponding glass models, especially in light of recent indications questioning the suitability of conventional MD approaches (that is, involving melt-and-quench of systems containing ∼10{sup 3} atoms at cooling rates of 5-10 K/ps) when applied to model these glasses. The analysis presented here compares MD models obtained with conventional and nonconventional cooling rates and system sizes, highlighting the trend and range of convergence of specific structural features in the medium range. The present results show that time-consuming computational approaches involving much lower cooling rates and/or significantly larger system sizes are in most cases not necessary in order to obtain a reliable description of the medium-range structure of multicomponent glasses. We identify the convergence range for specific properties and use them to discuss models of several glass compositions for which a possible influence of cooling-rate or size effects had been previously hypothesized. The trends highlighted here represent an important reference to obtain reliable models of multicomponent glasses and extract converged medium-range structural features which affect the glass degradation and thus their application in different fields. In addition, as a first application of the present findings, the fully converged structure of the 45S5 glass was further analyzed to shed new light on several dissolution-related features whose interpretation has been rather controversial in the past.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Machine learning analysis of perovskite oxides grown by molecular beam epitaxy

Reflection high-energy electron diffraction (RHEED) is a ubiquitous in situ molecular beam epitaxial (MBE) characterization tool. Although RHEED can be a powerful means for crystal surface structure determination, it is often used as a static qualitative surface characterization method at discrete intervals during a growth. A full analysis of RHEED data collected during the entirety of MBE growths is made possible using principle component analysis (PCA) and $\textit{k}$-means clustering to examine significant boundaries that occur in the temporal clusters grouped from RHEED data and identify statistically significant patterns. This process is applied to data from homoepitaxial SrTiO 3 growths, heteroepitaxial SrTiO 3 grown on scandate substrates, BaSnO 3 films grown on SrTiO 3 substrates, and LaNiO 3 films grown on SrTiO 3 substrates. We report this analysis may provide additional insights into the surface evolution and transitions in growth modes at precise times and depths during growth, and that video archival of an entire RHEED image sequence may be able to provide more insight and control overgrowth processes and film quality.

36 MATERIALS SCIENCE↗