Search NASA⌕ Search

SEARCH · Search NASA

Results for “cluster methods”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,657 records · Page 92

Deep Learning Based Frequency Stability Assessment in Power Grid with High Renewables

Frequency stability assessment is one critical aspect of power system security assessment. Traditional N-1 screening method is based on the simulations of a few typical daily and seasonal operation scenarios. However, the increasing integration of inverter-based renewables and the retirement of conventional synchronous generators result in decreasing system inertia and growing complexity of system operating conditions. Selecting a few typical operation scenarios cannot cover all operating conditions, and the time-domain simulation of all operation conditions requires tremendous time. This paper proposes a more efficient frequency stability assessment method based on deep learning. The affinity propagation clustering algorithm is used to divide the dataset into different clusters, so the selected dataset for training can cover the diversified operating conditions as much as possible. Also, feature normalization is applied to both the training dataset and testing dataset in order to remove any unnecessary bias. Especially, trained model based on full dataset normalization has bounded error in the prediction. The case study on the reduced 240-bus WECC system demonstrates that the proposed method can predict accurate frequency nadir with limited training dataset. The deep learning model using the revised feature normalization can predict more accurate frequency nadir than that using the traditional feature normalization and has very small maximum prediction error.

affinity propagation↗

Initiating Molecular Growth in the Interstellar Medium via Dimeric Complexes of Observed Ions and Molecules

A feasible initiation step for particle growth in the interstellar medium (ISM) is simulated by means of ab quantum chemistry methods. The systems studied are dimer ions formed by pairing nitrogen containing small molecules known to exist in the ISM with ions of unsaturated hydrocarbons or vice versa. Complexation energies, structures of ensuing complexes and electronic excitation spectra of the encounter complexes are estimated using various quantum chemistry methods. Moller-Plesset perturbation theory (MP2, Z-averaged perturbation theory (ZAP2), coupled cluster singles and doubles with perturbative triples corrections (CCSD(T)), and density functional theory (DFT) methods (B3LYP) were employed along with the correlation consistent cc-pVTZ and aug-cc-pVTZ basis sets. Two types of complexes are predicted. One type of complex has electrostatic binding with moderate (7-20 kcal per mol) binding energies, that are nonetheless significantly stronger than typical van der Waals interactions between molecules of this size. The other type of complex develops strong covalent bonds between the fragments. Cyclic isomers of the nitrogen containing complexes are produced very easily by ion-molecule reactions. Some of these complexes show intense ultraviolet visible spectra for electronic transitions with large oscillator strengths at the B3LYP, omegaB97, and equations of motion coupled cluster (EOM-CCSD) levels. The open shell nitrogen containing carbonaceous complexes especially exhibit a large oscillator strength electronic transition in the visible region of the electromagnetic spectrum.

Bera, Partha P.↗

Black holes in young stellar clusters

We present theoretical models for stellar black hole (BH) properties in young, massive star clusters. Using a Monte Carlo code for stellar dynamics, we model realistic star clusters with N ≅ 5 × 10{sup 5} stars and significant binary fractions (up to 50%) with self-consistent treatments of stellar dynamics and stellar evolution. We compute the formation rates and characteristic properties of single and binary BHs for various representative ages, cluster parameters, and metallicities. Because of dynamical interactions and supernova (SN) kicks, more single BHs end up retained in clusters compared to BHs in binaries. We also find that the ejection of BHs from a cluster is a strong function of initial density. In low-density clusters (where dynamical effects are negligible), it is mainly SN kicks that eject BHs from the cluster, whereas in high-density clusters (initial central density ρ {sub c}(0) ∼ 10{sup 5} M {sub ☉} pc{sup –3} in our models) the BH ejection rate is enhanced significantly by dynamics. Dynamical interactions of binary systems in dense clusters also modify the orbital period and eccentricity distributions while increasing the probability of a BH having a more massive companion.

79 ASTRONOMY AND ASTROPHYSICS↗

Diuranium(IV) Carbide Cluster U 2 C 2 Stabilized Inside Fullerene Cages

>Novel actinide cluster fullerenes, U 2 C 2 @ I h (7)-C 80 and U 2 C 2 @ D 3 h (5)-C 78 , were synthesized and fully characterized by mass spectrometry, single-crystal X-ray crystallography, UV–vis–NIR, nuclear magnetic resonance spectroscopy (NMR), X-ray absorption spectroscopy (XAS), Raman spectroscopy, IR spectroscopy, as well as density functional and multireference wave function calculations. The encapsulated U 2 C 2 is a novel example of a uranium carbide cluster featuring two U centers bridged by a C≡C unit. The U–C bond distances in these U 2 C 2 clusters are in the range between 2.130 and 2.421 Å. While the U 2 C 2 cluster in U 2 C 2 @C 80 adopts a butterfly-shaped geometry with a U–C 2 –U dihedral angle of 112.7° and a U–U distance of 3.855 Å, the U–U distance in U 2 C 2 @C 78 is 4.164 Å and the resulting U–C 2 –U dihedral angle is increased to 149.1°. The combined experimental and quantum-chemical results suggest that the formal U oxidation state is +4 in the U 2 C 2 cluster, and each U center transfers three electrons to the C 2 n cage and one electron to C 2 . Different from the strong U = C covalent bonding reported for U 2 C 2 @ C 80 , the U–C bonds in U 2 C 2 are less covalent and predominantly ionic. The C–C triple bond is somewhat weaker than in HCCH, and the C–C π bonds undergo donation bonding with the U centers. This work shows that the combination of the unique encapsulation effect of fullerene cages and the variable oxidation states of actinide elements can lead to the stabilization of novel actinide clusters, which are not accessible by conventional synthetic methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Objective Quantification of Convective Clustering Observed During the AMIE/DYNAMO Two-Day Rain Episodes

One critical bottleneck in developing and evaluating ways to represent the mesoscale organization of convection in cumulus parameterization schemes is that there is no single accepted method of objectively quantifying the degree of convective organization or clustering from observations. This study addresses this need using high-quality S-PolKa radar data from the Atmospheric Radiation Measurement Madden-Julian Oscillation Investigation Experiment/Dynamics of the Madden-Julian Oscillation (AMIE/DYNAMO) field campaign. We first identify convective elements (contiguous convective echoes [CCEs]) from radar reflectivity observations using the rain type classification algorithm of Powell et al. (2016, https://doi.org/10.1175/JTECH-D-15-0135.1). Then we apply scalar clustering metrics, including the organization index (I org ) of Tompkins and Semie, to the radar CCEs to test their ability of quantifying convective clustering during the observed two-day rain episodes. Our results show two distinct phases of convective clustering during the two-day rain episodes, with each phase covering about 10 hr before (Phase 1) and after (Phase 2) the time of peak rain rate. In Phase 1 clustering, the number of CCEs increases and convective cells cluster as new cells form preferentially near existing convective entities. The number of CCEs decreases as the environment stabilizes in Phase 2 clustering, during which already clustered cells with associated stratiform clouds are preferred over the isolated ones. I org is capable of capturing convective clustering in both phases. The possible mechanisms for convective clustering are discussed, including cold pool-updraft feedback, moisture-convection interaction, and mesoscale circulations. Our results suggest that parameterizations of convective organization should represent the feedback processes that are responsible for the convective clustering during both phases.

54 ENVIRONMENTAL SCIENCES↗

SparseMaps—A systematic infrastructure for reduced-scaling electronic structure methods. III. Linear-scaling multireference domain-based pair natural orbital N-electron valence perturbation theory

Multi-reference (MR) electronic structure methods, such as MR configuration interaction or MR perturbation theory, can provide reliable energies and properties for many molecular phenomena like bond breaking, excited states, transition states or magnetic properties of transition metal complexes and clusters. However, owing to their inherent complexity, most MR methods are still too computationally expensive for large systems. Therefore the development of more computationally attractive MR approaches is necessary to enable routine application for large-scale chemical systems. Among the state-of-the-art MR methods, second-order N-electron valence state perturbation theory (NEVPT2) is an efficient, size-consistent, and intruder-state-free method. However, there are still two important bottlenecks in practical applications of NEVPT2 to large systems: (a) the high computational cost of NEVPT2 for large molecules, even with moderate active spaces and (b) the prohibitive cost for treating large active spaces. In this work, we address problem (a) by developing a linear scaling “partially contracted” NEVPT2 method. This development uses the idea of domain-based local pair natural orbitals (DLPNOs) to form a highly efficient algorithm. As shown previously in the framework of single-reference methods, the DLPNO concept leads to an enormous reduction in computational effort while at the same time providing high accuracy (approaching 99.9% of the correlation energy), robustness, and black-box character. In the DLPNO approach, the virtual space is spanned by pair natural orbitals that are expanded in terms of projected atomic orbitals in large orbital domains, while the inactive space is spanned by localized orbitals. The active orbitals are left untouched. Our implementation features a highly efficient “electron pair prescreening” that skips the negligible inactive pairs. The surviving pairs are treated using the partially contracted NEVPT2 formalism. A detailed comparison between the partial and strong contraction schemes is made, with conclusions that discourage the strong contraction scheme as a basis for local correlation methods due to its non-invariance with respect to rotations in the inactive and external subspaces. A minimal set of conservatively chosen truncation thresholds controls the accuracy of the method. With the default thresholds, about 99.9% of the canonical partially contracted NEVPT2 correlation energy is recovered while the crossover of the computational cost with the already very efficient canonical method occurs reasonably early; in linear chain type compounds at a chain length of around 80 atoms. Calculations are reported for systems with more than 300 atoms and 5400 basis functions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The Resolved Stellar Population in 50 Regions of M83 from HST/WFC3 Early Release Science Observations

We present a multi-wavelength photometric study of approximately 15,000 resolved stars in the nearby spiral galaxy M83 (NGC 5236, D = 4.61 Mpc) based on Hubble Space Telescope Wide Field Camera 3 observations using four filters: F336W, F438W, F555W, and F814W. We select 50 regions (an average size of 260 pc by 280 pc) in the spiral arm and inter-arm areas of M83 and determine the age distribution of the luminous stellar populations in each region. This is accomplished by correcting for extinction toward each individual star by comparing its colors with predictions from stellar isochrones.We compare the resulting luminosity-weighted mean ages of the luminous stars in the 50 regions with those determined from several independent methods, including the number ratio of red-to-blue supergiants, morphological appearance of the regions, surface brightness fluctuations, and the ages of clusters in the regions. We find reasonably good agreement between these methods. We also find that young stars are much more likely to be found in concentrated aggregates along spiral arms, while older stars are more dispersed. These results are consistent with the scenario that star formation is associated with the spiral arms, and stars form primarily in star clusters and then disperse on short timescales to form the field population. The locations ofWolf-Rayet stars are found to correlate with the positions of many of the youngest regions, providing additional support for our ability to accurately estimate ages. We address the effects of spatial resolution on the measured colors, magnitudes, and age estimates. While individual stars can occasionally show measurable differences in the colors and magnitudes, the age estimates for entire regions are only slightly affected.

Hubble Space Telescope↗

The Physics and Chemistry of Cluster-based Catalyst Systems

In this project we used first principles calculations to investigate the physical and chemical properties of atomic clusters, small clumps of matter containing up to a few hundred atoms. The calculations are based on density functional theory, the most efficient and accurate method available for computing and thereby predicting the properties of molecules, clusters, and solids. An initial focus of our work involved studying the reactivity of small Pd clusters. We demonstrated that H2 molecules readily dissociate on Pd clusters and that multiple dissociations can occur on clusters larger than a few Pd atoms. For example, up to eight H2 molecules will dissociate on Pd13 at room temperature. A second focus of the project was to study the evolution of the electrical behavior of clusters, in particular, the evolution of metallic behavior in clusters of metal atoms. We developed a technique for using the electric dipole polarizability of the clusters – the response of the cluster to an applied electric field -- as a probe of this behavior. By decomposing the polarizability into so-called charge transfer (or metallic) and local (or insulating) contributions, we obtained the remarkable result that the per atom charge transfer contributions in sodium clusters attain the same value as occurs in bulk sodium metal for clusters containing as few as 10 atoms. Our calculated total polarizabilities matched experimentally measured trends essentially perfectly, validating the reliability of the calculations and supporting our conclusion that tiny clusters already display bulk-like metallic character. Similar results were obtained for other metal atom clusters, including potassium, aluminum, and zinc. We discovered that the evolution toward bulk metallic behavior for each of these cluster species is essentially identical, when judged using these polarizability-based descriptors. This suggests a universality in the approach of metal clusters to bulk-like metallicity. This discovery is a major outcome of our project. We also used our polarizability-based metrics to investigate the evolution of the properties of Si and NaCl clusters. These systems are non-metallic in the bulk limit. For Si, we find the behavior of small clusters to be metal-like and we attribute this to bonding arrangements in the clusters that are very different from the open structure of crystalline silicon. As the clusters get larger, the bonding in the cluster interior becomes more and more like that of the bulk material. We expect a transition from metal-like to insulator-like behavior to accompany this change is structure. Our results point to a transition at sizes beyond 146 atoms, the largest size treated in our study. We hope to study this transition further in the future. A third thrust of our project involved work on a new approach to eliminating unphysical electron self-interaction from density functional theory calculations. Two electrons exert repulsive forces on each other, but a single electron should not interact with itself. Because of approximations made in density functional theory, individual electrons effectively interact with themselves. The effect of this self-interaction is often small, especially when atoms are near their equilibrium positions in molecules. But the effects can be pronounced when bonds are stretched and also in some situations involving transition metals even when the atoms are near equilibrium. Our research involved the further development of a method for removing self-interaction called the Fermi-Löwdin orbital self-interaction correction or FLO-SIC. We conducted the first systematic FLO-SIC calculations for the individual atoms ranging from Li – Kr. We also carried out a critical comparison of the results of FLO-SIC with an older method known as the Perdew-Zunger self-interaction correction (PZ-SIC). FLO-SIC can be thought of as a modified approach to PZ-SIC that is expected to be computationally more efficient. We demonstrated that the two methods are distinct, but give essentially the same results for quantities such as molecular binding energies or electron removal energies. We are continuing to develop the FLO-SIC methodology with a goal of creating a computationally efficient method that is free of self-interaction effects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Understanding the presence of vacancy clusters in ZnO from a kinetic perspective

Vacancy clusters have been observed in ZnO, but their physical origin remains elusive. This is because under thermal equilibrium clusters typically have higher formation energies. Combining first-principles calculation and kinetic Monte Carlo (kMC) simulation, we determine the roles of non-equilibrium kinetics on the vacancy clustering. We find that clustering starts with the formation of Zn and O vacancy pairs (VZn-Vo), which further grow by attracting additional mono-vacancies. At this stage, vacancy diffusivity becomes crucial: due to the larger diffusivity of VZn compared to VO, more VZn-abundant clusters are formed than VO-abundant clusters. The large dissociation energies, e.g., 3 eV for (VZn-Vo), suggest that, once formed, it is difficult for the clusters to dissociate. As such, thermal annealing will increase the size of the clusters by promoting mono-vacancy diffusion, in agreement with experiment.

Ab initio calculations↗

Statistical Descriptors of Composite Fiber Aggregation

This study introduces a method of characterizing fiber aggregation and resin rich regions in composite microstructures. Microscale models of representative elements (RVE) need to be indicative of the extend of clustering (i.e. close fiber-to-fiber interaction) and resin rich “pools” which may impact the overall strength and performance of a composite structure. This algorithm was used to evaluate different unidirectional 2-D microstructure scans, which will be compared to their manufacturing method or any special treatment processes. These cluster and pool scan statistics can be used as criteria to judge statistical equivalency of artificially constructed microstructures.

Statistics↗

One-step synthesis of tetrahedral rhenium chalcocyanide cluster complexes [{Re_4(µ_3-Q)_4}(CN){sub 12}]{sup 4−} (Q = S, Se, Te) using ReI{sub 3}

A new method is proposed for the one-step synthesis of tetrahedral rhenium chalcocyanide cluster complexes [{Re_4(µ_3-Q)_4}(CN){sub 12}]{sup 4−} (Q = S, Se, Te) using rhenium(III) iodide as the starting compound. A cluster complex of the composition K4[{Re_4S_4}(CN){sub 12}]•H{sub 2}O was synthesized for the first time and characterized by X-ray diffraction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Insights into the Nanostructure, Solvation, and Dynamics of Liquid Electrolytes through Small-Angle X-Ray Scattering

The fundamental understanding of nanostructures of liquid electrolytes is expected to enable transformative gains in electrochemical energy storage capacities. However, the solvation structures and molecular dynamics in electrolytes are hard to probe, which limits further performance improvements in macroscopic properties such as ionic conductivity, viscosity, and stability. Small-angle X-ray scattering (SAXS), a non-destructive method for nanostructures characterization in liquids, offers a unique perspective on molecular clusters, anion-cation pairs, solvation sheath, percolating network, aggregates, and domain sizes of electrolytes. Notably, the SAXS method has excellent time-resolution, enabling the study of molecular dynamics by operando characterization. This review focuses on the microscopic understanding of liquid electrolytes by SAXS and operando SAXS. The unique capabilities of X-ray scattering for intra- and inter-molecular relationship and nanostructure characterization of electrolytes are highlighted.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Methods and systems for analysis

Provided herein are systems and methods for the detection, quantification, and/or monitoring of analytes in samples. The systems and methods can be used, for example, to track the deposition and electrochemical behavior of individual nanoparticles and nanoparticles clusters clusters in situ with high spatial and temporal resolution. The systems and methods can be used to track the deposition and oxidation of several hundreds to thousands of nanoparticles simultaneously and reconstruct their voltammetric curves at the single nanoparticle level.

Pan, Shanlin↗

Scaling Deep Learning on GPU and Knights Landing clusters

The speed of deep neural networks training has become a big bottleneck of deep learning research and development. For example, training GoogleNet by ImageNet dataset on one Nvidia K20 GPU needs 21 days. To speed up the training process, the current deep learning systems heavily rely on the hardware accelerators. However, these accelerators have limited on-chip memory compared with CPUs. To handle large datasets, they need to fetch data from either CPU memory or remote processors. We use both self-hosted Intel Knights Landing (KNL) clusters and multi-GPU clusters as our target platforms. From an algorithm aspect, current distributed machine learning systems are mainly designed for cloud systems. These methods are asynchronous because of the slow network and high fault-tolerance requirement on cloud systems. We focus on Elastic Averaging SGD (EASGD) to design algorithms for HPC clusters. Original EASGD used round-robin method for communication and updating. The communication is ordered by the machine rank ID, which is inefficient on HPC clusters. First, we redesign four efficient algorithms for HPC systems to improve EASGD's poor scaling on clusters. Async EASGD, Async MEASGD, and Hogwild EASGD are faster \textcolor{black}{than} their existing counterparts (Async SGD, Async MSGD, and Hogwild SGD, resp.) in all the comparisons. Finally, we design Sync EASGD, which ties for the best performance among all the methods while being deterministic. In addition to the algorithmic improvements, we use some system-algorithm codesign techniques to scale up the algorithms. By reducing the percentage of communication from 87% to 14%, our Sync EASGD achieves 5.3x speedup over original EASGD on the same platform. We get 91.5% weak scaling efficiency on 4253 KNL cores, which is higher than the state-of-the-art implementation.

60 APPLIED LIFE SCIENCES↗

Relaxation channels of multi-photon excited xenon clusters

The relaxation processes of the xenon clusters subjected to multi-photon excitation by laser radiation with quantum energies significantly lower than the thresholds of excitation of atoms and ionization of clusters were studied. Results obtained by means of the photoelectron spectroscopy method showed that desorption processes of excited atoms play a significant role in the decay of two-photon excited xenon clusters. A number of excited states of xenon atoms formed during this process were discovered and identified.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Oxidative decomposition mechanisms of lithium peroxide clusters: an Ab Initio study

Oxidative decomposition of solid lithium peroxide is an important part of the charging process in a Li-O 2 battery. Here, we investigate oxidative decomposition mechanisms of lithium peroxide clusters as molecular models for solid lithium peroxide using density functional methods to understand charging processes in advanced energy storage systems. Most calculations are done using a (Li 2 O 2 ) 4 cluster with similar results obtained from a larger (Li 2 O 2 ) 16 cluster. Reaction pathways of the clusters involving different sequences of oxidation, oxygen evolution, lithium cation removal, and spin excitation are investigated. The computations suggest that certain oxidative decomposition routes may not have dependence on the oxygen evolution or Li-ion removal kinetics due to the exothermicity of oxygen removal and Li + removal (by solvent) upon oxidation. The computed charge potentials evaluated using a tetramer model indicates that it is possible to have low overcharge potential provided there exists a good electronic conductivity to facilitate the oxidative decomposition. Finally, oxidation potentials of a series of Li x O y clusters are investigated to assess their dependence on stoichiometry and how the local site from which the electrons are being removed affects the charge potentials.

74 ATOMIC AND MOLECULAR PHYSICS↗

Deep transfer learning for star cluster classification: I. application to the PHANGS– HST survey

ABSTRACT We present the results of a proof-of-concept experiment that demonstrates that deep learning can successfully be used for production-scale classification of compact star clusters detected in Hubble Space Telescope(HST) ultraviolet-optical imaging of nearby spiral galaxies ($D\lesssim 20\, \textrm{Mpc}$) in the Physics at High Angular Resolution in Nearby GalaxieS (PHANGS)–HST survey. Given the relatively small nature of existing, human-labelled star cluster samples, we transfer the knowledge of state-of-the-art neural network models for real-object recognition to classify star clusters candidates into four morphological classes. We perform a series of experiments to determine the dependence of classification performance on neural network architecture (ResNet18 and VGG19-BN), training data sets curated by either a single expert or three astronomers, and the size of the images used for training. We find that the overall classification accuracies are not significantly affected by these choices. The networks are used to classify star cluster candidates in the PHANGS–HST galaxy NGC 1559, which was not included in the training samples. The resulting prediction accuracies are 70 per cent, 40 per cent, 40–50 per cent, and 50–70 per cent for class 1, 2, 3 star clusters, and class 4 non-clusters, respectively. This performance is competitive with consistency achieved in previously published human and automated quantitative classification of star cluster candidate samples (70–80 per cent, 40–50 per cent, 40–50 per cent, and 60–70 per cent). The methods introduced herein lay the foundations to automate classification for star clusters at scale, and exhibit the need to prepare a standardized data set of human-labelled star cluster classifications, agreed upon by a full range of experts in the field, to further improve the performance of the networks introduced in this study.

Wei, Wei↗

Muon track reconstruction in a segmented bolometric array using multi-objective optimization

Recent advances in segmented solid-state detector arrays for rare-event searches have allowed the technology to approach the ton-scale in detector mass and the scale of meters in size. Often focused around searches for neutrinoless double-beta decay or direct dark matter detection, such experiments also have the capability to search for exotic particles that leave track-like signatures across their volume. However, the segmented nature of such detector arrays often sets the spatial resolution and makes the problem of reconstructing track-like paths non-trivial. Here, in this paper, we present an algorithm that improves reconstruction of track-like events in segmented detectors using multi-objective optimization — a computational technique that optimizes more than one cost function at a time without specifying a quantitative weighting between them. Such a technique allows the reconstruction of tracks through a detector and the determination of path-lengths through individual elements. When combined with the reconstructed energy depositions in each element this allows for a calculation of the stopping power of track-like particles and opens the door to searches for particles with abnormal stopping power like monopoles or lightly-ionizing particles (LIPs). Results are presented which evaluate the precision of the reconstruction tools as they currently stand against Monte Carlo generated data. The algorithm is presented in the context of the CUORE experiment, but has applications to other segmented calorimeter detectors.

47 OTHER INSTRUMENTATION↗