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The trends in reactivity of CF(n) radicals with SiO2 and the site selectivity of the attack are studied using two different cluster models. The reaction barriers for the most energetically favorable reaction are computed. It is shown that CF(n) radicals are fairly unreactive towards SiO2.
Parallel computing promises several orders of magnitude increase in our ability to solve realistic computationally-intensive problems, but relies on their efficient mapping and execution on large-scale multiprocessor architectures. Unfortunately, many important applications are irregular and dynamic in nature, making their effective parallel implementation a daunting task. Moreover, with the proliferation of parallel architectures and programming paradigms, the typical scientist is faced with a plethora of questions that must be answered in order to obtain an acceptable parallel implementation of the solution algorithm. In this paper, we consider three representative irregular applications: unstructured remeshing, sparse matrix computations, and N-body problems, and parallelize them using various popular programming paradigms on a wide spectrum of computer platforms ranging from state-of-the-art supercomputers to PC clusters. We present the underlying problems, the solution algorithms, and the parallel implementation strategies. Smart load-balancing, partitioning, and ordering techniques are used to enhance parallel performance. Overall results demonstrate the complexity of efficiently parallelizing irregular algorithms.
Soot formed in laminar diffusion flames of heavily sooting fuels evolves through four distinct growth stages which give rise to four distinct aggregate fractal morphologies. These results were inferred from large and small angle static light scattering from the flames, microphotography of the flames, and analysis of soot sampled from the flames. The growth stages occur approximately over four successive orders of magnitude in aggregate size. Comparison to computer simulations suggests that these four growth stages involve either diffusion limited cluster aggregation or percolation in either three or two dimensions.
Chemical kinetic models for the nucleation and growth of clusters and single-walled carbon nanotube (SWNT) growth are developed for numerical simulations of the production of SWNTs. Two models that involve evaporation and condensation of carbon and metal catalysts, a full model involving all carbon clusters up to C80, and a reduced model are discussed. The full model is based on a fullerene model, but nickel and carbon/nickel cluster reactions are added to form SWNTs from soot and fullerenes. The full model has a large number of species--so large that to incorporate them into a flow field computation for simulating laser ablation and arc processes requires that they be simplified. The model is reduced by defining large clusters that represent many various sized clusters. Comparisons are given between these models for cases that may be applicable to arc and laser ablation production. Solutions to the system of chemical rate equations of these models for a ramped temperature profile show that production of various species, including SWNTs, agree to within about 50% for a fast ramp, and within 10% for a slower temperature decay time.
Aerogels are of interest to the aerospace community primarily for their thermal properties, notably their low thermal conductivities. While the gels are typically fragile, recent advances in the application of conformal polymer layers to these gels has made them potentially useful as lightweight structural materials as well. In this work, we investigate the strength and fracture behavior of silica aerogels using a molecular statics-based computer simulation technique. The gels' structure is simulated via a Diffusion Limited Cluster Aggregation (DLCA) algorithm, which produces fractal structures representing experimentally observed aggregates of so-called secondary particles, themselves composed of amorphous silica primary particles an order of magnitude smaller. We have performed multi-length-scale simulations of fracture in silica aerogels, in which the interaction b e e n two secondary particles is assumed to be described by a Morse pair potential parameterized such that the potential range is much smaller than the secondary particle size. These Morse parameters are obtained by atomistic simulation of models of the experimentally-observed amorphous silica "bridges," with the fracture behavior of these bridges modeled via molecular statics using a Morse/Coulomb potential for silica. We consider the energetics of the fracture, and compare qualitative features of low-and high-density gel fracture.
Wearable technology has the potential to revolutionize the way humans interact with one another, with information, and with the electronic systems that surround them. This change can already be seen in the dramatic increase in the availability and use of wearable health and activity monitors. These devices continuously monitor the wearer using on-‐body sensors and wireless communication. They provide feedback that can be used to improve physical health and performance. Smart watches and head mounted displays are also receiving a great deal of commercial attention, providing immediate access to information via graphical displays, as well as additional sensing features. For the purposes of the Wearable Technology CLUSTER, wearable technology is broadly defined as any electronic sensing, human interfaces, computing, or communication that is mounted on the body. Current commercially available wearable devices primarily house electronics in rigid packaging to provide protection from flexing, moisture, and other contaminants. NASA mentors are interested in this approach, but are also interested in direct integration of electronics into clothing to enable more comfortable systems. For human spaceflight, wearable technology holds a great deal of promise for significantly improving safety, efficiency, autonomy, and research capacity for the crew in space and support personnel on the ground. Specific capabilities of interest include: Continuous biomedical monitoring for research and detection of health problems. Environmental monitoring for individual exposure assessments and alarms. Activity monitoring for responsive robotics and environments. Multi-modal caution and warning using tactile, auditory, and visual alarms. Wireless, hands-free, on-demand voice communication. Mobile, on-demand access to space vehicle and robotic displays and controls. Many technical challenges must be overcome to realize these wearable technology applications. For example, to make a wearable device that is both functional and comfortable for long duration wear, developers must strive to reduce electronic mass and volume while also addressing constraints imposed by the body attachment method. Depending on the application, the device must be placed in a location that the user can see and reach, and that provides the appropriate access to air and the wearer's skin. Limited power is available from body-‐worn batteries and heat must be managed to prevent discomfort. If the clothing is to be washed, there are additional durability and washability hurdles that traditional electronics are not designed to address. Finally, each specific capability has unique technical challenges that will likely require unique solutions. In addition to the technical challenges, development of wearable devices is made more difficult by the diversity of skills required and the historic lack of collaboration across domains. Wearable technology development requires expertise in textiles engineering, apparel design, software and computer engineering, electronic design and manufacturing, human factors engineering, and application-‐specific fields such as acoustics, medical devices, and sensing. Knowledge from each of these domains must be integrated to create functional and comfortable devices. For this reason, the diversity of knowledge and experience represented in the Wearable Technology is critical to overcoming the fundamental challenges in the field.
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A query graph, which includes vertices and edges, represents a query on graph-structured data. The query graph is decomposed into query subgraphs. A network analysis tool performs continuous subgraph matching queries to facilitate analysis of computer network traffic, social media events, or other streams of data represented as a dynamic data graph (graph-structured data). This can help identify emerging trends in the data. Some features of the network analysis tool enhance performance by effectively utilizing distributed computing resources (including processing cores and memory at different nodes of a cluster) to speed up the process of updating the dynamic data graph and detecting matches of query subgraphs. Features of a query graph building tool enhance usability by providing intuitive ways to specify query graphs and their subgraphs. Features of a results visualization tool enhance usability by providing an intuitive way to present the results of continuous subgraph matching queries.
Estimation of the accident progress and status inside the pressure vessels (RPV) and primary containment vessels (PCV) is required for appropriate conductance of decommissioning in the Fukushima-Daiichi NPP. For that, it is necessary to obtain additional experimental data and revised models for the estimation using computer codes with increased accuracies. The Japan Atomic Energy Agency (JAEA) has selected phenomena to be reviewed and developed, considering previously obtained information, conditions specific to the Fukushima-Daiichi NPP accident, and recent progress of experimental and analytical technologies. As a result, research and development items have been picked up in terms of thermal-hydraulic behavior in the RPV and PCV, progression of fuel bundle degradation, failure of the lower head of RPV, and analysis of the accident. This paper introduces the selected phenomena to be reviewed and developed, research plans and recent results from the JAEA's corresponding research programs. (authors)
Usually, the spacer grid decreases the flow area and then enhances the convective heat transfer due to the flow acceleration and the turbulence increases. Their phenomena is shown in both egg-crate style spacer grids and mixing vane grids. Also, the mixing vane swirls the flow and enhances the convective heat transfer. In this study, the effect of spacer grid model of TRACE was performed on RBHT (Rod Bundle Heat Transfer) reflood tests. The RBHT tests were designed to conduct several separate-effects tests such as single phase steam cooling, low flow boiling and dispersed flow film boiling heat transfer, which is very important during a large break loss of coolant accident (LOCA). This facility has a heated length of 3.66 m (12.0 ft), which is typical of a pressurized water reactor (PWR) fuel assembly. The test section consists of a 7 x 7 rod bundle with 45 heated electrical rods, and seven mixing vane grids. The electrical rods have a diameter of 9.5 mm and are arranged with a 12.6 mm pitch with 45 heater rods and four unheated rods. Tests 1096, 1108, 1170, 1196, 1285 and 1383, which are in the TRACE assessment manual , were selected to evaluate the effect of the spacer grid model of TRACE. (authors)
We describe the production of the official set of mock catalogs for the Dark Energy Spectroscopic Instrument Peculiar Velocity Survey (DESI-PV) Data Release 1 (DR1). Our mock catalogs reproduce the Bright Galaxy Survey number density and clustering at low redshift $(z<0.1)$ and the DESI PV samples of Fundamental plane and Tully-Fisher distances, from which we derive peculiar velocities. We carefully match mock and data properties and we mimic measurements of distance indicators and peculiar velocities, which follow the same statistical properties as real data. Mock samples of type-Ia supernovae also complement the other two distance indicators. Our 675 available mock realizations were used consistently by our three different methodologies described in our companion papers that measure the growth rate of structure $fσ_8$ with DESI PV DR1. Those mocks allow us to perform precise tests of clustering models and uncertainty estimation to an unprecedented level of accuracy, and compute correlations between methodologies. The consensus value for the DESI DR1 PV growth rate measurement is $fσ_8 = 0.450 \pm 0.055$. This sample of mock catalogs represents the largest and most realistic set for cosmological measurements with peculiar velocities to date.
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.
We formulate the $O(3)$ non-linear sigma model in $1+1$ dimensions as a limit of a three-component scalar field theory restricted to the unit sphere in the large squeezing limit. This allows us to describe the model in terms of the continuous variable (CV) approach to quantum computing. Here we construct the ground state and excited states using the coupled cluster ansatz and find excellent agreement with the exact diagonalization results for a small number of lattice sites. We then present the simulation protocol for the time evolution of the model using CV gates, estimate the discretization error, and present numerical results obtained from a photonic quantum simulator. We expect that the methods developed in this work will be useful for exploring interesting dynamics for a wide class of sigma models and gauge theories, as well as for simulating scattering events on quantum hardware in the coming decade.
K-means clustering analysis is applied to frequency-domain thermoreflectance (FDTR) hyperspectral image data to rapidly screen the spatial distribution of thermophysical properties at material interfaces. Performing FDTR while raster scanning a sample consisting of 8.6 μm of doped-silicon (Si) bonded to a doped-Si substrate identifies spatial variation in the subsurface bond quality. Routine thermal analysis at select pixels quantifies this variation in bond quality and allows assignment of bonded, partially bonded, and unbonded regions. Performing this same routine thermal analysis across the entire map, however, becomes too computationally demanding for rapid screening of bond quality. To address this, K-means clustering was used to reduce the dimensionality of the dataset from more than 20 000 pixel spectra to just K = 3 component spectra. The three component spectra were then used to express every pixel in the image through a least-squares minimized linear combination providing continuous interpolation between the components across spatially varying features, e.g., bonded to unbonded transition regions. Fitting the component spectra to the thermal model, thermal properties for each K cluster are extracted and then distributed according to the weighting established by the regressed linear combination. Thermophysical property maps are then constructed and capture significant variation in bond quality over 25 μm length scales. The use of K-means clustering to achieve these thermal property maps results in a 74-fold speed improvement over explicit fitting of every pixel.
In this work, we present a consistent ab initio computation of the longitudinal response function RL in 40 Ca using the coupled-cluster and Lorentz integral transform methods starting from chiral nucleon-nucleon and three-nucleon interactions. We validate our approach by comparing our results for R L in 4 He and the Coulomb sum rule in 40 Ca against experimental data and other calculations. For R L in 40 Ca we obtain a very good agreement with experiment in the quasielastic peak up to intermediate momentum transfers, and we find that final state interactions are essential for an accurate description of the data. This work presents a milestone towards ab initio computations of neutrino-nucleus cross sections relevant for experimental long-baseline neutrino programs.
Reefing lines are an essential feature to manage inflation loads. During each Engineering Development Unit (EDU) test of the Capsule Parachute Assembly System (CPAS), a chase aircraft is staged to be level with the cluster of Main ringsail parachutes during the initial inflation and reefed stages. This allows for capturing high-quality still photographs of the reefed skirt, suspension line, and canopy geometry. The over-inflation angles are synchronized with measured loads data in order to compute the tension force in the reefing line. The traditional reefing tension equation assumes radial symmetry, but cluster effects cause the reefed skirt of each parachute to elongate to a more elliptical shape. This effect was considered in evaluating multiple parachutes to estimate the semi-major and semi-minor axes. Three flight tests are assessed, including one with a skipped first stage, which had peak reefing line tension over three times higher than the nominal parachute disreef sequence.
Network traffic monitoring is a core element in network operations and management for various purposes such as anomaly detection, change detection, and fault/failure detection. In this study, we introduce a new approach to online monitoring using a pattern-based representation of the network traffic. Unlike the past online techniques limited to a single variable to summarize (e.g., sketch), the focus of this study is on capturing the network state from the multivariate attributes under consideration. To this end, we employ clustering with its benefit of the aggregation of multidimensional variables. The clustered result represents the state of the network with regard to the monitored variables, which can also be compared with the previously observed patterns visually and quantitatively. Finally, we demonstrate the proposed method with two popular use cases, one for estimating state changes and the other for identifying anomalous states, to confirm its feasibility.
High-performance computing (HPC) is often perceived as a matter of making large-scale systems (e.g., clusters) run as fast as possible, regardless the required programming effort. However, the idea of "bringing HPC to the masses" has recently emerged. Inspired by this vision, we have designed SHAD, the Scalable High-performance Algorithms and Data-structures library. SHAD is open source software, written in C++, for C++ developers. Unlike other HPC libraries for distributed systems, which rely on SPMD models, SHAD adopts a shared-memory programming abstraction, to make C++ programmers feel at home. Underneath, SHAD manages tasking and data-movements, moving the computation where data resides and taking advantage of asynchrony to tolerate network latency. At the bottom of his stack, SHAD can interface with multiple runtime systems: this not only improves developer’s productivity, by hiding the complexity of such software and of the underlying hardware, but also greatly enhance code portability. Thanks to its abstraction layers, SHAD can indeed target different systems, ranging from laptops to HPC clusters, without any need for modifying the user-level code. We have prototyped and open-sourced the implementation of (a subset of) the C++ standard library (STL) targeting multi-node HPC clusters. Our work allows plain STL-based C++ code to scale on HPC systems, with no need for rewriting the code to exploit the complex hardware. SHAD is available under Apache v2 License at https://github.com/pnnl/SHAD. In this paper we overview the design of the SHAD library, depicting its main components: runtime systems abstractions for tasking; parallel and distributed data-structures; STL-compliant interfaces and algorithms.