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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

The benzene radical anion: A computationally demanding prototype for aromatic anions

The benzene radical anion is studied with ab initio coupled-cluster theory in large basis sets. Unlike the usual assumption, we find that, at the level of theory investigated, the minimum energy geometry is non-planar with tetrahedral distortion at two opposite carbon atoms. The anion is well known for its instability to auto-ionization which poses computational challenges to determine its properties. Despite the importance of the benzene radical anion, the considerable attention it has received in the literature so far has failed to address the details of its structure and shape-resonance character at a high level of theory. Here, we examine the dynamic Jahn-Teller effect and its impact on the anion potential energy surface. We find that a minimum energy geometry of C{sub 2} symmetry is located below one D{sub 2h} stationary point on a C{sub 2h} pseudo-rotation surface. The applicability of standard wave function methods to an unbound anion is assessed with the stabilization method. The isotropic hyperfine splitting constants (A{sub iso}) are computed and compared to data obtained from experimental electron spin resonance experiments. Satisfactory agreement with experiment is obtained with coupled-cluster theory and large basis sets such as cc-pCVQZ.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Numerical investigation of vorticity and bubble clustering in an air entraining hydraulic jump

In this paper, a high resolution computational fluid dynamics model is used to simulate a steady air entraining laboratory scale hydraulic jump. A detailed examination of shear layer instabilities reveals the dynamic relationship between spanwise vortices, free surface fluctuations, and air–water spatial patterns. Spanwise vortices generated at the toe roll-up under a variable depth roller, creating large free surface fluctuations through high velocity water ejections in the roller. The mean shear layer elevation and free surface elevations periodically alternate between positive and negative correlation throughout the roller, driven by dynamic vortex transport. Vortices descending towards the lower wall create an upwelling of non-bubbly fluid into the shear layer that contributes to regions of decreased bubble concentration between vortices. The position of a strong shear layer at the location of maximum air entrainment, directly above the jump toe, leads to highly aerated vortices that influence bubble behavior. Bubbles breakup quickly after entrainment at the toe and bubble clusters are observed most frequently below and at the end of the roller where bubble breakup and energy dissipation are diminished. Finally, the dominant separation angle of clustered bubbles is independent of downstream distance and aligns closely with the direction of initial shear, suggesting bubble clustering is a remnant of bubble breakup.

13 HYDRO ENERGY↗
DOE OSTI2026

Hierarchical Truncations for Many-Body Expansion Potentials

In this work, a new strategy to truncate high-order terms in the many-body expansion (MBE) is proposed. This new approach, which we call a hierarchical many-body expansion (HMBE), is based on a hierarchical partition of the system into multitier fragments and can in principle be applied to any large molecular system. Numerical tests on a series of (H 2 O) 64 structures are presented, demonstrating satisfactory relative energies between the clusters and binding energies of individual clusters compared with full-cluster calculations, with significantly fewer high-order terms computed than conventional MBE. The hierarchical truncation can be augmented by certain many-body terms for fragments at the interface between the partitions (called “Schengen terms”) to further improve accuracy. This work establishes the HMBE scheme as a promising framework to model very large systems (e.g., proteins), which are naturally built on a hierarchical structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

A comparison of thermal zone aggregation methods

The impact of increasing energy prices on building operation budgets has fueled demand for more energy-efficient structures. Existing building energy simulation tools generate an immense amount of data yet comparatively little knowledge. This paper introduces a framework that allows aggregation-based model reduction to operate on geometric building information models. The resulting aggregation sequence provides designers with faster simulations and affords insight into complex multi-scale thermal interactions. A comparison of the trade-off between simulation speed and accuracy for three hierarchical cluster partitioning methods concludes the discussion.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Taking a quantum leap in time to solution for simulations of high-Tc superconductors

We present a new quantum cluster algorithm to simulate models of high-Tc superconductors. This algorithm extends current methods with continuous lattice self-energies, thereby removing artificial long-range correlations. This cures the fermionic sign problem in the underlying quantum Monte Carlo solver for large clusters and realistic values of the Coulomb interaction in the entire temperature range of interest. We find that the new algorithm improves time-to-solution by nine orders of magnitude compared to current, state of the art quantum cluster simulations. An efficient implementation is given, which ports to multi-core as well as hybrid CPU-GPU systems. Running on 18,600 nodes on ORNL's Titan supercomputer enables us to compute a converged value of Tc/t = 0.053 ± 0.0014 for a 28 site cluster in the 2D Hubbard model with U/t = 7 at 10% hole doping. Typical simulations on Titan sustain between 9.2 and 15.4 petaflops (double precision measured over full run), depending on configuration and parameters used.

42 ENGINEERING↗
DOE OSTI2022

Assessing Mg–Sc–(rare earth) ternary phase stability via constituent binary cluster expansions

The disordered Mg–Sc body-centered cubic (bcc) phase is both lightweight and strong; however, the system is impractical for general industrial use due to the high cost of scandium. Here we propose a computationally efficient metric that assesses ternary rare earth element additions that may stabilize the bcc phase at lower Sc concentrations. We find that the bcc phase is stabilized by the ternary addition of Y or Er, but not by La, Ce, or Nd, and we validate these predictions by experimental production and characterization of Mg–Sc–(Y,Er,Nd) alloys. The results suggest a computationally efficient method to anticipate integration of ternary elements into binary systems using cluster expansions of constituent binaries.

36 MATERIALS SCIENCE↗
DOE OSTI2013

In search of genome annotation consistency: solid gene clusters and how to use them

Maintaining consistency in genome annotations is important for supporting many computational tasks, particularly metabolic modeling. The SEED project has implemented a process that improves annotation consistencies across microbial genomes for proteins with conserved sequences and genomic context. In this research report, we describe this process and show how this effort has resulted in improvements to microbial genome annotations in the SEED. We also compare SEED annotation consistencies with other commonly used resources such as IMG (the Joint Genome Institute’s Integrated Microbial Genomes system), RefSeq (the National Center for Biotechnology Information’s Reference Sequence Database), Swiss-Prot (the annotated protein sequence database of the Swiss Institute of Bioinformatics, European Molecular Biology Laboratory and the European Bioinformatics Institute) and TrEMBL (Translated European Molecular Biology Laboratory nucleotide sequence data Library). Our analysis indicates that manual and computational efforts are paying off for the databases where consistency is a major goal.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Parallel Multivariate Spatio-Temporal Clustering of Large Ecological Datasets on Hybrid Supercomputers

A proliferation of data from vast networks of remote sensing platforms (satellites, unmanned aircraft systems (UAS), airborne etc.), observational facilities (meteorological, eddy covariance etc.), state-of-the-art sensors, and simulation models offer unprecedented opportunities for scientific discovery. Unsupervised classification is a widely applied data mining approach to derive insights from such data. However, classification of very large data sets is a complex computational problem that requires efficient numerical algorithms and implementations on high performance computing (HPC) platforms. Additionally, increasing power, space, cooling and efficiency requirements has led to the deployment of hybrid supercomputing platforms with complex architectures and memory hierarchies like the Titan system at Oak Ridge National Laboratory. The advent of such accelerated computing architectures offers new challenges and opportunities for big data analytics in general and specifically, large scale cluster analysis in our case. Although there is an existing body of work on parallel cluster analysis, those approaches do not fully meet the needs imposed by the nature and size of our large data sets. Moreover, they had scaling limitations and were mostly limited to traditional distributed memory computing platforms. We present a parallel Multivariate Spatio-Temporal Clustering (MSTC) technique based on k-means cluster analysis that can target hybrid supercomputers like Titan. We developed a hybrid MPI, CUDA and OpenACC implementation that can utilize both CPU and GPU resources on computational nodes. We describe performance results on Titan that demonstrate the scalability and efficacy of our approach in processing large ecological data sets.

Sreepathi, Sarat↗
DOE OSTI2017

Optimum aggregation of geographically distributed flexible resources in strategic smart-grid/microgrid locations

This paper determines optimum aggregation areas for a given distribution network considering spatial distribution of loads and costs of aggregation. An elitist genetic algorithm combined with a hierarchical clustering and a Thevenin network reduction is implemented to compute strategic locations and aggregate demand within each area. The aggregation reduces large distribution networks having thousands of nodes to an equivalent network with few aggregated loads, thereby significantly reducing the computational burden. Furthermore, it not only helps distribution system operators in making faster operational decisions by understanding during which time of the day will be in need of flexibility, from which specific area, and in which amount, but also enables the flexibilities stemming from small distributed resources to be traded in various power/energy markets. A combination of central and local aggregation scheme where a central aggregator enables market participation, while local aggregators materialize the accepted bids, is implemented to realize this concept. The effectiveness of the proposed method is evaluated by comparing network performances with and without aggregation. Finally, for a given network configuration, steady-state performance of aggregated network is significantly accurate (≈ ±1.5% error) compared to very high errors associated with forecast of individual consumer demand.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Systems and methods for determining optimal parameters for dynamic quantum clustering analyses

In the present work, quantum clustering is extended to provide a dynamical approach for data clustering using a time-dependent Schrodinger equation. To expedite computations, we can approximate the time-dependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wave-functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition and/or feature filtering. Additionally, the parameters of the analysis can be modified in order to improve the efficiency of the dynamic quantum clustering processes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Insights into Supported Subnanometer Catalysts Exposed to CO via Machine-Learning-Enabled Multiscale Modeling

Subnanometer catalysts offer high noble metal utilization and superior performance for several reactions. However, understanding their structures and properties on an atomic scale under working conditions is challenging due to the large configurational space. Here, we introduce an efficient multiscale framework to predict their stability exposed to an adsorbate. The framework integrates a comprehensive toolset including density functional theory (DFT) calculations, cluster expansion, machine learning, and structure optimization. The end-to-end machine-learning workflow guides DFT data generation and enables significant computational acceleration. We demonstrate the approach for CO-adsorbed Pdn (n = 1–55) clusters on CeO 2 (111). Simulation results reveal that CO can facilitate restructuring by stabilizing smaller planar structures and bilayer structures of specific intermediate sizes, consistent with experimental reports. Metal–support interactions, preferential CO adsorption, and metal nuclearity and structure control catalyst stability. As a result, the framework allows automatic discovery of stable catalyst structures and a systematic strategy to exploit properties in the subnanometer scale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Simple, direct and efficient multi-way spectral clustering

Abstract We present a new algorithm for spectral clustering based on a column-pivoted QR factorization that may be directly used for cluster assignment or to provide an initial guess for k-means. Our algorithm is simple to implement, direct and requires no initial guess. Furthermore, it scales linearly in the number of nodes of the graph and a randomized variant provides significant computational gains. Provided the subspace spanned by the eigenvectors used for clustering contains a basis that resembles the set of indicator vectors on the clusters, we prove that both our deterministic and randomized algorithms recover a basis close to the indicators in Frobenius norm. We also experimentally demonstrate that the performance of our algorithm tracks recent information theoretic bounds for exact recovery in the stochastic block model. Finally, we explore the performance of our algorithm when applied to a real-world graph.

Damle, Anil↗
DOE OSTI2018

A theoretical and experimental benchmark study of core-excited states in nitrogen

The high resolution near edge X-ray absorption fine structure spectrum of nitrogen displays the vibrational structure of the core-excited states. This makes nitrogen well suited for assessing the accuracy of different electronic structure methods for core excitations. We report high resolution experimental measurements performed at the SOLEIL synchrotron facility. These are compared with theoretical spectra calculated using coupled cluster theory and algebraic diagrammatic construction theory. The coupled cluster singles and doubles with perturbative triples model known as CC3 is shown to accurately reproduce the experimental excitation energies as well as the spacing of the vibrational transitions. In conclusion, the computational results are also shown to be systematically improved within the coupled cluster hierarchy, with the coupled cluster singles, doubles, triples, and quadruples method faithfully reproducing the experimental vibrational structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Verification of sub-grid filtered drag models for gas-particle fluidized beds with immersed cylinder arrays

The accuracy of coarse-grid multiphase CFD simulations of fluidized beds may be improved via the inclusion of filtered constitutive models. In our previous study (Sarkar et al., Chem. Eng. Sci., 104, 399-412), we developed such a set of filtered drag relationships for beds with immersed arrays of cooling tubes. Verification of these filtered drag models is addressed in this work. Predictions from coarse-grid simulations with the sub-grid filtered corrections are compared against accurate, highly-resolved simulations of full-scale turbulent and bubbling fluidized beds. The filtered drag models offer a computationally efficient yet accurate alternative for obtaining macroscopic predictions, but the spatial resolution of meso-scale clustering heterogeneities is sacrificed.

42 ENGINEERING↗
DOE OSTI2020

First-principles modeling of chemistry in mixed solvents: Where to go from here?

Mixed solvents (i.e., binary or higher order mixtures of ionic or nonionic liquids) play crucial roles in chemical syntheses, separations, and electrochemical devices because they can be tuned for specific reactions and applications. Apart from fully explicit solvation treatments that can be difficult to parameterize or computationally expensive, there is currently no well-established first-principles regimen for reliably modeling atomic-scale chemistry in mixed solvent environments. We offer our perspective on how this process could be achieved in the near future as mixed solvent systems become more explored using theoretical and computational chemistry. We first outline what makes mixed solvent systems far more complex compared to single-component solvents. An overview of current and promising techniques for modeling mixed solvent environments is provided. We focus on so-called hybrid solvation treatments such as the conductor-like screening model for real solvents and the reference interaction site model, which are far less computationally demanding than explicit simulations. We also propose that cluster-continuum approaches rooted in physically rigorous quasi-chemical theory provide a robust, yet practical, route for studying chemical processes in mixed solvents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

The final WaZP galaxy cluster catalog of the Dark Energy Survey and comparison with SZE data

In this work, we present and characterize the galaxy cluster catalog detected by the WaZP cluster finder, which is not based on red-sequence identification, on the full six years of observations of the Dark Energy Survey (DES-Y6). The full catalog contains over 400k detected clusters with richnesses, Ngals, above 5 and that reach redshifts up to 1.3. We also provide a version of the catalog where the observation depth and richness computation are homogenized to be used for cosmology, containing 33k rich (Ngals >25) clusters. We compare our results with the previous WaZP catalog obtained from the DES first-year data release (DES-Y1). We find that essentially all clusters within the common footprint and depth limit are recovered. The deeper observations on DES-Y6 and the more complete available spectroscopic redshift sample lead to improvements in the redshifts of the clusters, resulting in an average scatter of 1.4% and offset of 0.2%. The optical clusters are also cross-matched with Sunyaev Zel'dovich Effect (SZE) cluster samples detected by the South Pole Telescope (SPT) and the Atacama Cosmology Telescope (ACT). We find that essentially all SZE clusters with reasonable overlapping footprint have a corresponding WaZP cluster. Conversely, 90% of the optical detections with richness greater than 150 have a counterpart in the deeper regions of the SZE surveys. Based on cross-match with the SZE catalogs, we also find that 15-20% of the SZE matched systems have more than one possible WaZP counterpart at the same redshift and within the SZE R500c, indicating possible interacting or unrelaxed systems. Finally, given the optical and SZE beams, WaZP and SZE centerings are found to be consistent. A more detailed study of the SZE-WaZP mass-richness relation will be presented in a separate paper.

Benoist, C. [OCA, Nice, Lab. Lagrange; LIneA, Rio ↗
NASA NTRS2005

Testing the Archivas Cluster (Arc) for Ozone Monitoring Instrument (OMI) Scientific Data Storage

The Ozone Monitoring Instrument (OMI) launched on NASA's Aura Spacecraft, the third of the major platforms of the EOS program on July 15,2004. In addition to the long term archive and distribution of the data from OM1 through the Goddard Earth Science Distributed Active Archive Center (GESDAAC), we are evaluating other archive mechanisms that can archive the data in a more immediately available method where it can be used for futher data production and analysis. In 2004, Archivas, Inc. was selected by NASA s Small Business Innovative Research (SBIR) program for the development of their Archivas Cluster (ArC) product. Arc is an online disk based system utilizing self-management and automation on a Linux cluster. Its goal is to produce a low cost solution coupled with the ease of management. The OM1 project is an application partner of the SBIR program, and has deployed a small cluster (5TB) based on the beta Archwas software. We performed extensive testing of the unit using production OM1 data since launch. In 2005, Archivas, Inc. was funded in SBIR Phase II for further development, which will include testing scalability with the deployment of a larger (35TB) cluster at Goddard. We plan to include Arc in the OM1 Team Leader Computing Facility (TLCF) hosting OM1 data for direct access and analysis by the OMI Science Team. This presentation will include a brief technical description of the Archivas Cluster, a summary of the SBIR Phase I beta testing results, and an overview of the OMI ground data processing architecture including its interaction with the Phase II Archivas Cluster and hosting of OMI data for the scientists.

Tilmes, Curt↗