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Defect reaction network in Si-doped InP : numerical predictions

Schultz, Peter A.

This Report characterizes the defects in the defect reaction network in silicon-doped, n-type InP deduced from first principles density functional theory. The reaction network is deduced by following exothermic defect reactions starting with the initially mobile interstitial defects reacting with common displacement damage defects in Si-doped InP until culminating in immobile reaction products. The defect reactions and reaction energies are tabulated, along with the properties of all the silicon-related defects in the reaction network. This Report serves to extend the results for intrinsic defects in SAND 2012-3313: %E2%80%9CSimple intrinsic defects in InP: Numerical predictions%E2%80%9D to include Si-containing simple defects likely to be present in a radiation-induced defect reaction sequence.

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Simulation information regarding Sandia National Laboratories trinity capability improvement metric

Agelastos, Anthony M.; Lin, Paul L.

Sandia National Laboratories, Los Alamos National Laboratory, and Lawrence Livermore National Laboratory each selected a representative simulation code to be used as a performance benchmark for the Trinity Capability Improvement Metric. Sandia selected SIERRA Low Mach Module: Nalu, which is a uid dynamics code that solves many variable-density, acoustically incompressible problems of interest spanning from laminar to turbulent ow regimes, since it is fairly representative of implicit codes that have been developed under ASC. The simulations for this metric were performed on the Cielo Cray XE6 platform during dedicated application time and the chosen case utilized 131,072 Cielo cores to perform a canonical turbulent open jet simulation within an approximately 9-billion-elementunstructured- hexahedral computational mesh. This report will document some of the results from these simulations as well as provide instructions to perform these simulations for comparison.

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HPCG Benchmark Technical Specification

Heroux, Michael A.

The High Performance Conjugate Gradient (HPCG) benchmark [cite SNL, UTK reports] is a tool for ranking computer systems based on a simple additive Schwarz, symmetric Gauss-Seidel preconditioned conjugate gradient solver. HPCG is similar to the High Performance Linpack (HPL), or Top 500, benchmark [1] in its purpose, but HPCG is intended to better represent how today’s applications perform. In this paper we describe the technical details of HPCG: how it is designed and implemented, what code transformations are permitted and how to interpret and report results.

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Development of ab initio techniques critical for future science-based explosives R&D

Wixom, Ryan R.; Wills, Ann E.

Density Functional Theory (DFT) has emerged as an indispensable tool in materials research, since it can accurately predict properties of a wide variety of materials at both equilibrium and extreme conditions. However, for organic molecular crystal explosives, successful application of DFT has largely failed due to the inability of current exchange-correlation functionals to correctly describe intermolecular van der Waals (vdWs) forces. Despite this, we have discovered that even with no treatment of vdWs bonding, the AM05 functional and DFT based molecular dynamics (MD) could be used to study the properties of molecular crystals under compression. We have used DFT-MD to predict the unreacted Hugoniots for PETN and HNS and validated the results by comparison with crystalline and porous experimental data. Since we are also interested in applying DFT methods to study the equilibrium volume properties of explosives, we studied the nature of the vdWs bonding in pursuit of creating a new DFT functional capable of accurately describing equilibrium bonding of molecular crystals. In this report we discuss our results for computing shock Hugoniots of molecular crystals and also what was learned about the nature of bonding in these materials.

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A comparison of methods for representing sparsely sampled random quantities

Romero, Vicente J.; Swiler, Laura P.; Urbina, Angel U.

This report discusses the treatment of uncertainties stemming from relatively few samples of random quantities. The importance of this topic extends beyond experimental data uncertainty to situations involving uncertainty in model calibration, validation, and prediction. With very sparse data samples it is not practical to have a goal of accurately estimating the underlying probability density function (PDF). Rather, a pragmatic goal is that the uncertainty representation should be conservative so as to bound a specified percentile range of the actual PDF, say the range between 0.025 and .975 percentiles, with reasonable reliability. A second, opposing objective is that the representation not be overly conservative; that it minimally over-estimate the desired percentile range of the actual PDF. The presence of the two opposing objectives makes the sparse-data uncertainty representation problem interesting and difficult. In this report, five uncertainty representation techniques are characterized for their performance on twenty-one test problems (over thousands of trials for each problem) according to these two opposing objectives and other performance measures. Two of the methods, statistical Tolerance Intervals and a kernel density approach specifically developed for handling sparse data, exhibit significantly better overall performance than the others.

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Nonlinear kinematics for piezoelectricity in ALEGRA-EMMA

Mitchell, John A.; Fuller, Timothy J.

This report develops and documents nonlinear kinematic relations needed to implement piezoelectric constitutive models in ALEGRA-EMMA [5], where calculations involving large displacements and rotations are routine. Kinematic relationships are established using Gausss law and Faradays law; this presentation on kinematics goes beyond piezoelectric materials and is applicable to all dielectric materials. The report then turns to practical details of implementing piezoelectric models in an application code where material principal axes are rarely aligned with user defined problem coordinate axes. This portion of the report is somewhat pedagogical but is necessary in order to establish documentation for the piezoelectric implementation in ALEGRA-EMMA. This involves transforming elastic, piezoelectric, and permittivity moduli from material principal axes to problem coordinate axes. The report concludes with an overview of the piezoelectric implementation in ALEGRA-EMMA and small verification examples.

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Kalman-filtered compressive sensing for high resolution estimation of anthropogenic greenhouse gas emissions from sparse measurements

Ray, Jaideep R.; Lee, Jina L.; Lefantzi, Sophia L.; van Bloemen Waanders, Bart G.

The estimation of fossil-fuel CO2 emissions (ffCO2) from limited ground-based and satellite measurements of CO2 concentrations will form a key component of the monitoring of treaties aimed at the abatement of greenhouse gas emissions. The limited nature of the measured data leads to a severely-underdetermined estimation problem. If the estimation is performed at fine spatial resolutions, it can also be computationally expensive. In order to enable such estimations, advances are needed in the spatial representation of ffCO2 emissions, scalable inversion algorithms and the identification of observables to measure. To that end, we investigate parsimonious spatial parameterizations of ffCO2 emissions which can be used in atmospheric inversions. We devise and test three random field models, based on wavelets, Gaussian kernels and covariance structures derived from easily-observed proxies of human activity. In doing so, we constructed a novel inversion algorithm, based on compressive sensing and sparse reconstruction, to perform the estimation. We also address scalable ensemble Kalman filters as an inversion mechanism and quantify the impact of Gaussian assumptions inherent in them. We find that the assumption does not impact the estimates of mean ffCO2 source strengths appreciably, but a comparison with Markov chain Monte Carlo estimates show significant differences in the variance of the source strengths. Finally, we study if the very different spatial natures of biogenic and ffCO2 emissions can be used to estimate them, in a disaggregated fashion, solely from CO2 concentration measurements, without extra information from products of incomplete combustion e.g., CO. We find that this is possible during the winter months, though the errors can be as large as 50%.

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Neurons to algorithms LDRD final report

Aimone, James B.; Warrender, Christina E.; Trumbo, Derek T.

Over the last three years the Neurons to Algorithms (N2A) LDRD project teams has built infrastructure to discover computational structures in the brain. This consists of a modeling language, a tool that enables model development and simulation in that language, and initial connections with the Neuroinformatics community, a group working toward similar goals. The approach of N2A is to express large complex systems like the brain as populations of a discrete part types that have specific structural relationships with each other, along with internal and structural dynamics. Such an evolving mathematical system may be able to capture the essence of neural processing, and ultimately of thought itself. This final report is a cover for the actual products of the project: the N2A Language Specification, the N2A Application, and a journal paper summarizing our methods.

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Electrical conductivity in oxygen-deficient phases of transition metal oxides from first-principles calculations

Desjarlais, Michael P.; Thompson, Aidan P.; Brennecka, Geoffrey L.; Marinella, Matthew J.

Density-functional theory calculations, ab-initio molecular dynamics, and the Kubo-Greenwood formula are applied to predict electrical conductivity in Ta2Ox (0 x 5) as a function of composition, phase, and temperature, where additional focus is given to various oxidation states of the O monovacancy (VOn; n=0,1+,2+). Our calculations of DC conductivity at 300K agree well with experimental measurements taken on Ta2Ox thin films and bulk Ta2O5 powder-sintered pellets, although simulation accuracy can be improved for the most insulating, stoichiometric compositions. Our conductivity calculations and further interrogation of the O-deficient Ta2O5 electronic structure provide further theoretical basis to substantiate VO0 as a donor dopant in Ta2O5 and other metal oxides. Furthermore, this dopant-like behavior appears specific to neutral VO cases in both Ta2O5 and TiO2 and was not observed in other oxidation states. This suggests that reduction and oxidation reactions may effectively act as donor activation and deactivation mechanisms, respectively, for VO0 in transition metal oxides.

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ALEGRA Update: Modernization and Resilience Progress

Robinson, Allen C.; Petney, Sharon P.; Drake, Richard R.; Weirs, Vincent G.; Adams, Brian M.; Vigil, Dena V.; Carpenter, John H.; Garasi, Christopher J.; Wong, Michael K.; Robbins, Joshua R.; Siefert, Christopher S.; Strack, Otto E.; Wills, Ann E.; Trucano, Timothy G.; Bochev, Pavel B.; Summers, Randall M.; Stewart, James R.; Ober, Curtis C.; Rider, William J.; Haill, Thomas A.; Lemke, Raymond W.; Cochrane, Kyle C.; Desjarlais, Michael P.; Love, Edward L.; Voth, Thomas E.; Mosso, Stewart J.; Niederhaus, John H.

Abstract not provided.

Development of a fourth generation predictive capability maturity model

Hills, Richard G.; Witkowski, Walter R.; Rider, William J.; Trucano, Timothy G.; Urbina, Angel U.

The Predictive Capability Maturity Model (PCMM) is an expert elicitation tool designed to characterize and communicate completeness of the approaches used for computational model definition, verification, validation, and uncertainty quantification associated for an intended application. The primary application of this tool at Sandia National Laboratories (SNL) has been for physics-based computational simulations in support of nuclear weapons applications. The two main goals of a PCMM evaluation are 1) the communication of computational simulation capability, accurately and transparently, and 2) the development of input for effective planning. As a result of the increasing importance of computational simulation to SNLs mission, the PCMM has evolved through multiple generations with the goal to provide more clarity, rigor, and completeness in its application. This report describes the approach used to develop the fourth generation of the PCMM.

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LDRD project 151362 : low energy electron-photon transport

Kensek, Ronald P.; Hjalmarson, Harold P.; Magyar, Rudolph J.; Bondi, Robert J.

At sufficiently high energies, the wavelengths of electrons and photons are short enough to only interact with one atom at time, leading to the popular %E2%80%9Cindependent-atom approximation%E2%80%9D. We attempted to incorporate atomic structure in the generation of cross sections (which embody the modeled physics) to improve transport at lower energies. We document our successes and failures. This was a three-year LDRD project. The core team consisted of a radiation-transport expert, a solid-state physicist, and two DFT experts.

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DAKOTA reliability methods applied to RAVEN/RELAP-7

Swiler, Laura P.

This report summarizes the result of a NEAMS project focused on the use of reliability methods within the RAVEN and RELAP-7 software framework for assessing failure probabilities as part of probabilistic risk assessment for nuclear power plants. RAVEN is a software tool under development at the Idaho National Laboratory that acts as the control logic driver and post-processing tool for the newly developed Thermal-Hydraulic code RELAP-7. Dakota is a software tool developed at Sandia National Laboratories containing optimization, sensitivity analysis, and uncertainty quantification algorithms. Reliability methods are algorithms which transform the uncertainty problem to an optimization problem to solve for the failure probability, given uncertainty on problem inputs and a failure threshold on an output response. The goal of this work is to demonstrate the use of reliability methods in Dakota with RAVEN/RELAP-7. These capabilities are demonstrated on a demonstration of a Station Blackout analysis of a simplified Pressurized Water Reactor (PWR).

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Xyce parallel electronic simulator users guide, version 6.0

Russo, Thomas V.; Mei, Ting M.; Keiter, Eric R.; Schiek, Richard S.; Thornquist, Heidi K.; Verley, Jason V.; Coffey, Todd S.; Pawlowski, Roger P.; Warrender, Christina E.

This manual describes the use of the Xyce Parallel Electronic Simulator. Xyce has been designed as a SPICE-compatible, high-performance analog circuit simulator, and has been written to support the simulation needs of the Sandia National Laboratories electrical designers. This development has focused on improving capability over the current state-of-the-art in the following areas: Capability to solve extremely large circuit problems by supporting large-scale parallel computing platforms (up to thousands of processors). This includes support for most popular parallel and serial computers. A differential-algebraic-equation (DAE) formulation, which better isolates the device model package from solver algorithms. This allows one to develop new types of analysis without requiring the implementation of analysis-specific device models. Device models that are specifically tailored to meet Sandias needs, including some radiationaware devices (for Sandia users only). Object-oriented code design and implementation using modern coding practices. Xyce is a parallel code in the most general sense of the phrase a message passing parallel implementation which allows it to run efficiently a wide range of computing platforms. These include serial, shared-memory and distributed-memory parallel platforms. Attention has been paid to the specific nature of circuit-simulation problems to ensure that optimal parallel efficiency is achieved as the number of processors grows.

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Xyce parallel electronic simulator reference guide, version 6.0

Keiter, Eric R.; Mei, Ting M.; Russo, Thomas V.; Pawlowski, Roger P.; Schiek, Richard S.; Coffey, Todd S.; Thornquist, Heidi K.; Verley, Jason V.; Warrender, Christina E.

This document is a reference guide to the Xyce Parallel Electronic Simulator, and is a companion document to the Xyce Users Guide [1] . The focus of this document is (to the extent possible) exhaustively list device parameters, solver options, parser options, and other usage details of Xyce. This document is not intended to be a tutorial. Users who are new to circuit simulation are better served by the Xyce Users Guide [1] .

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General purpose steam table library :

Carpenter, John H.; Belcourt, Kenneth N.

Completion of the CASL L3 milestone THM.CFD.P7.04 provides a general purpose tabular interpolation library for material properties to support, in particular, standardized models for steam properties. The software consists of three parts, implementations of analytic steam models, a code to generate tables from those models, and an interpolation package to interface the tables to CFD codes such as Hydra-TH. Verification of the standard model is maintained through the entire train of routines. The performance of interpolation package exceeds that of freely available analytic implementation of the steam properties by over an order of magnitude.

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Numerical integration of the extended variable generalized Langevin equation with a positive Prony representable memory kernel

Journal of Chemical Physics

Baczewski, Andrew D.; Bond, Stephen D.

Generalized Langevin dynamics (GLD) arise in the modeling of a number of systems, ranging from structured fluids that exhibit a viscoelastic mechanical response, to biological systems, and other media that exhibit anomalous diffusive phenomena. Molecular dynamics (MD) simulations that include GLD in conjunction with external and/or pairwise forces require the development of numerical integrators that are efficient, stable, and have known convergence properties. In this article, we derive a family of extended variable integrators for the Generalized Langevin equation with a positive Prony series memory kernel. Using stability and error analysis, we identify a superlative choice of parameters and implement the corresponding numerical algorithm in the LAMMPS MD software package. Salient features of the algorithm include exact conservation of the first and second moments of the equilibrium velocity distribution in some important cases, stable behavior in the limit of conventional Langevin dynamics, and the use of a convolution-free formalism that obviates the need for explicit storage of the time history of particle velocities. Capability is demonstrated with respect to accuracy in numerous canonical examples, stability in certain limits, and an exemplary application in which the effect of a harmonic confining potential is mapped onto a memory kernel. © 2013 AIP Publishing LLC.

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Reducing uncertainty in high-resolution sea ice models

Peterson, Kara J.; Bochev, Pavel B.

Arctic sea ice is an important component of the global climate system, reflecting a significant amount of solar radiation, insulating the ocean from the atmosphere and influencing ocean circulation by modifying the salinity of the upper ocean. The thickness and extent of Arctic sea ice have shown a significant decline in recent decades with implications for global climate as well as regional geopolitics. Increasing interest in exploration as well as climate feedback effects make predictive mathematical modeling of sea ice a task of tremendous practical import. Satellite data obtained over the last few decades have provided a wealth of information on sea ice motion and deformation. The data clearly show that ice deformation is focused along narrow linear features and this type of deformation is not well-represented in existing models. To improve sea ice dynamics we have incorporated an anisotropic rheology into the Los Alamos National Laboratory global sea ice model, CICE. Sensitivity analyses were performed using the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) to determine the impact of material parameters on sea ice response functions. Two material strength parameters that exhibited the most significant impact on responses were further analyzed to evaluate their influence on quantitative comparisons between model output and data. The sensitivity analysis along with ten year model runs indicate that while the anisotropic rheology provides some benefit in velocity predictions, additional improvements are required to make this material model a viable alternative for global sea ice simulations.

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Spatially varying embedded stochastic galerkin methods for steady-state PDEs

Cyr, Eric C.

Existing discretizations for stochastic PDEs, based on a tensor product between the deterministic basis and the stochastic basis, treat the required resolution of uncertainty as uniform across the physical domain. However, solutions to many PDEs of interest exhibit spatially localized features that may result in uncertainty being severely over or under-resolved by existing discretizations. In this report, we explore the mechanics and accuracy of using a spatially varying stochastic expansion. This is achieved through an adaptive refinement algorithm where simple error estimates are used to independently drive refinement of the stochastic basis at each point in the physical domain. Results are presented comparing the accuracy of the adaptive techinque to the accuracy achieved using uniform refinement.

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Design issues in the semantics and scheduling of asynchronous tasks

Olivier, Stephen L.

The asynchronous task model serves as a useful vehicle for shared memory parallel programming, particularly on multicore and manycore processors. As adoption of model among programmers has increased, support has emerged for the integration of task parallel language constructs into mainstream programming languages, e.g., C and C++. This paper examines some of the design decisions in Cilk and OpenMP concerning semantics and scheduling of asynchronous tasks with the aim of informing the efforts of committees considering language integration, as well as developers of new task parallel languages and libraries.

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Comparing Algorithms for Graph Isomorphism Using Discrete- and Continuous-Time Quantum Random Walks

Journal of Computational and Theoretical Nanoscience

Rudinger, Kenneth M.; Laros, James H.; Bach, Eric; Friesen, Mark; Joynt, Robert; Coppersmith, S.N.

Berry and Wang [Phys. Rev. A 83, 042317 (2011)] show numerically that a discrete-time quan- tum random walk of two noninteracting particles is able to distinguish some non-isomorphic strongly regular graphs from the same family. Here we analytically demonstrate how it is possible for these walks to distinguish such graphs, while continuous-time quantum walks of two noninteracting parti- cles cannot. We show analytically and numerically that even single-particle discrete-time quantum random walks can distinguish some strongly regular graphs, though not as many as two-particle noninteracting discrete-time walks. Additionally, we demonstrate how, given the same quantum random walk, subtle di erences in the graph certi cate construction algorithm can nontrivially im- pact the walk's distinguishing power. We also show that no continuous-time walk of a xed number of particles can distinguish all strongly regular graphs when used in conjunction with any of the graph certi cates we consider. We extend this constraint to discrete-time walks of xed numbers of noninteracting particles for one kind of graph certi cate; it remains an open question as to whether or not this constraint applies to the other graph certi cates we consider.

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Results 6401–6600 of 9,998
Results 6401–6600 of 9,998