Extreme wind is one environmental aspect used in safe building and structure design. The Department of Energy (DOE) Standard (STD) 1020-2012 includes various building standards adopted by DOE, and guidance necessary to comply with Facility Safety. Recent updates to the American Society of Civil Engineers (ASCE) Standards and International Building Codes (IBC) were used to initiate a review of local maximum wind speeds and verify estimates to be used in new building design. This report summarizes the site specific wind data and techniques used in estimating the maximum wind speeds anticipated at Sandia National Laboratories, New Mexico. The local data suggests site specific increases in wind speed maxima over information documented in past standards and building codes.
Considerable effort is currently being spent designing neuromorphic hardware for addressing challenging problems in a variety of pattern-matching applications. These neuromorphic systems offer low power architectures with intrinsically parallel and simple spiking neuron processing elements. Unfortunately, these new hardware architectures have been largely developed without a clear justification for using spiking neurons to compute quantities for problems of interest. Specifically, the use of spiking for encoding information in time has not been explored theoretically with complexity analysis to examine the operating conditions under which neuromorphic computing provides a computational advantage (time, space, power, etc.) In this paper, we present and formally analyze the use of temporal coding in a neural-inspired algorithm for optimization-based computation in neural spiking architectures.
We present a quantitative analysis of the correlation of resonant wavelength variation with process variables, and find that 50% of the resonant wavelength variation for microrings is due to systematic process conditions. We also discuss the improvement of device uniformity by mitigating these systematic variations.
Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium, IPDPS 2017
Bhalachandra, Sridutt; Porterfield, Allan; Olivier, Stephen L.; Prins, Jan F.
Energy efficiency in high performance computing (HPC) will be critical to limit operating costs and carbon footprints in future supercomputing centers. Energy efficiency of a computation can be improved by reducing time to completion without a substantial increase in power drawn or by reducing power with a little increase in time to completion. We present an Adaptive Core-specific Runtime (ACR) that dynamically adapts core frequencies to workload characteristics, and show examples of both reductions in power and improvement in the average performance. This improvement in energy efficiency is obtained without changes to the application. The adaptation policy embedded in the runtime uses existing core-specific power controls like software-controlled clock modulation and per-core Dynamic Voltage Frequency Scaling (DVFS) introduced in Intel Haswell. Experiments on six standard MPI benchmarks and a real world application show an overall 20% improvement in energy efficiency with less than 1% increase in execution time on 32 nodes (1024 cores) using per-core DVFS. An improvement in energy efficiency of up to 42% is obtained with the real world application ParaDis through a combination of speedup and power reduction. For one configuration, ParaDis achieves an average speedup of 11%, while the power is lowered by about 31%. The average improvement in the performance seen is a direct result of the reduction in run-to-run variation and running at turbo frequencies.
Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing - data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are capable of learning continuously, deep artificial networks have a limited ability for incorporating new information in an already trained network. As a result, methods for continuous learning are potentially highly impactful in enabling the application of deep networks to dynamic data sets. Here, inspired by the process of adult neurogenesis in the hippocampus, we explore the potential for adding new neurons to deep layers of artificial neural networks in order to facilitate their acquisition of novel information while preserving previously trained data representations. Our results on the MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes lower and upper case letters and digits, demonstrate that neurogenesis is well suited for addressing the stability-plasticity dilemma that has long challenged adaptive machine learning algorithms.
Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017
Yamazaki, Ichitaro; Hoemmen, Mark F.; Luszczek, Piotr; Dongarra, Jack
We compare the performance of pipelined and s-step GMRES, respectively referred to as l-GMRES and s-GMRES, on distributed multicore CPUs. Compared to standard GMRES, s-GMRES requires fewer all-reduces, while l-GMRES overlaps the all-reduces with computation. To combine the best features of two algorithms, we propose another variant, (l, t)-GMRES, that not only does fewer global all-reduces than standard GMRES, but also overlaps those all-reduces with other work. We implemented the thread-parallelism and communication-overlap in two different ways. The first uses nonblocking MPI collectives with thread-parallel computational kernels. The second relies on a shared-memory task scheduler. In our experiments, (l, t)-GMRES performed better than l-GMRES by factors of up to 1.67×. In addition, though we only used 50 nodes, when the latency cost became significant, our variant performed up to 1.22× better than s-GMRES by hiding all-reduces.
We consider the problem of writing performance portablesparse matrix-sparse matrix multiplication (SPGEMM) kernelfor many-core architectures. We approach the SPGEMMkernel from the perspectives of algorithm design and implementation, and its practical usage. First, we design ahierarchical, memory-efficient SPGEMM algorithm. We thendesign and implement thread scalable data structures thatenable us to develop a portable SPGEMM implementation. We show that the method achieves performance portabilityon massively threaded architectures, namely Intel's KnightsLanding processors (KNLs) and NVIDIA's Graphic ProcessingUnits (GPUs), by comparing its performance to specializedimplementations. Second, we study an important aspectof SPGEMM's usage in practice by reusing the structure ofinput matrices, and show speedups up to 3× compared to thebest specialized implementation on KNLs. We demonstratethat the portable method outperforms 4 native methods on2 different GPU architectures (up to 17× speedup), and it ishighly thread scalable on KNLs, in which it obtains 101× speedup on 256 threads.
The in-memory graph layout affects performance of distributed-memory graph computations. Graph layout could refer to partitioning or replication of vertex and edge arrays, selective replication of data structures that hold meta-data, and reordering vertex and edge identifiers. In this work, we consider one-dimensional graph layouts, where disjoint sets of vertices and their adjacencies are partitioned among processors. Using the PuLP graph partitioning method and a breadth-first search (BFS)-based vertex ordering strategy, we empirically evaluate the impact of this graph layout on a collection of five distributed-memory graph computations. Our evaluation considers several objective metrics in addition to execution time, and we observe a considerable performance improvement over randomization.
Drilling is a repetitive, dangerous and costly process and a strong candidate for automation. We describe a method for autonomously controlling a rotary drilling process as it transitions through multiple materials with very different dynamics. This approach classifies the drilling medium based on real-time measurements and comparison to prior drilling data, and can identify the material type, drilling region, and approximately optimal set-point based on data from as few as one operating condition. The controller uses these set-points as initial conditions, and then conducts an optimal search to maximize performance, e.g. by minimizing mechanical specific energy. The control architecture is described, and the material estimation process is detailed. The results of experiments that implement autonomous drilling through a layered concrete and granite sample are discussed.
Drilling is a repetitive, dangerous and costly process and a strong candidate for automation. We describe a method for autonomously controlling a rotary drilling process as it transitions through multiple materials with very different dynamics. This approach classifies the drilling medium based on real-time measurements and comparison to prior drilling data, and can identify the material type, drilling region, and approximately optimal set-point based on data from as few as one operating condition. The controller uses these set-points as initial conditions, and then conducts an optimal search to maximize performance, e.g. by minimizing mechanical specific energy. The control architecture is described, and the material estimation process is detailed. The results of experiments that implement autonomous drilling through a layered concrete and granite sample are discussed.
Wijesinghe, Sidath; Maskey, Sabina; Perahia, Dvora; Grest, Gary S.
Long-lived soft nanoparticles, formed by conjugated polymers, constitute a new class of far-from-equilibrium responsive structures for nano-medicine. Tethering ionizable groups to the polymers enables functionality. However concurrently, the ionic groups perturb the delicate balance of interactions that governs these particles. Using fully atomistic molecular dynamics simulations, this study probed the effects of charged groups tethered to poly para phenylene ethynylene substituted by alkyl groups on the polymer conformation and dynamics in confined geometry. We find that the ionizable groups affect the entire shape of the polydots and impact the conformation and dynamics of the polymer.
2017 IEEE Int. Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, DFT 2017
Siddiqua, Taniya; Sridharan, Vilas; Raasch, Steven E.; Debardeleben, Nathan; Ferreira, Kurt B.; Levy, Scott; Baseman, Elisabeth; Guan, Qiang
In order to provide high system resilience, it is important to understand the nature of the faults that occur in the field. This study analyzes fault rates from a production system that has been monitored for five years, capturing data for the entire operational lifetime of the system. The data show that devices in this system did not show any sign of aging during the monitoring period, suggesting that the lifetime of a system may be longer than five years. In DRAM, the relative incidence of fault modes changed insignificantly over the system's lifetime: The relative rate of each fault mode at the end of the system's lifetime was within 1.4 percentage point of the rate observed during the first year. SRAM caches in the system exhibited different fault modes including cache-way fault and single-bit faults. Overall, this study provides insights on how fault modes and types in a system evolve over the system's lifetime.
We have performed two-dimensional Fourier transform spectroscopy on intrinsic and modulation doped quantum wells in external magnetic fields up to 10 T. In the undoped sample, the strong Coulomb interactions and the increasing separations of the electron and hole charge distributions with increasing magnetic fields lead to a nontrivial in-plane dispersion of the magneto-excitons. Thus, the discrete and degenerate Landau levels are coupled to a continuum. The signature of this continuum is the emergence of elongated spectral line shapes at the Landau level energies, which are exposed by the multidimensional nature of our technique. Surprisingly, the elongation of the peaks is completely absent in the lowest Landau level spectra obtained from the modulation doped quantum well at high fields.
A detailed design of pressure separation by packed columns of particles, in a solar-thermochemical reactor prototype, is presented. Results show that the concept is sound and robust under a multitude operational conditions. Straightforward control approaches, such as pumping speed and pressure adjustments, can be implemented to cover a wide range of contingencies.
A detailed design of pressure separation by packed columns of particles, in a solar-thermochemical reactor prototype, is presented. Results show that the concept is sound and robust under a multitude operational conditions. Straightforward control approaches, such as pumping speed and pressure adjustments, can be implemented to cover a wide range of contingencies.
This paper describes improvements in task scheduling for the Chapel parallel programming language provided in its default on-node tasking runtime, the Qthreads library. We describe a new scheduler distrib which builds on the approaches of two previous Qthreads schedulers, Sherwood and Nemesis, and combines the best aspects of both-work stealing and load balancing from Sherwood and a lock free queue access from Nemesis- to make task queuing better suited for the use of Chapel in the manycore era. We demonstrate the efficacy of this new scheduler by showing improvements in various individual benchmarks of the Chapel test suite on the Intel Knights Landing architecture.
Iron opacity calculations presently disagree with measurements at an electron temperature of ∼180-195 eV and an electron density of (2-4)×1022cm-3, conditions similar to those at the base of the solar convection zone. The measurements use x rays to volumetrically heat a thin iron sample that is tamped with low-Z materials. The opacity is inferred from spectrally resolved x-ray transmission measurements. Plasma self-emission, tamper attenuation, and temporal and spatial gradients can all potentially cause systematic errors in the measured opacity spectra. In this article we quantitatively evaluate these potential errors with numerical investigations. The analysis exploits computer simulations that were previously found to reproduce the experimentally measured plasma conditions. The simulations, combined with a spectral synthesis model, enable evaluations of individual and combined potential errors in order to estimate their potential effects on the opacity measurement. The results show that the errors considered here do not account for the previously observed model-data discrepancies.
HPDC 2017 - Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing
Xie, Bing; Huang, Yezhou; Chase, Jefrey S.; Choi, Jong Y.; Klasky, Scott; Lofstead, Gerald (Jay) F.; Oral, Sarp
In this paper, we develop a predictive model useful for output performance prediction of supercomputer file systems under production load. Our target environment is Titan-the 3rd fastest supercomputer in the world-and its Lustre-based multi-stage write path. We observe from Titan that although output performance is highly variable at small time scales, the mean performance is stable and consistent over typical application run times. Moreover, we find that output performance is non-linearly related to its correlated parameters due to interference and saturation on individual stages on the path. These observations enable us to build a predictive model of expected write times of output patterns and I/O configurations, using feature transformations to capture non-linear relationships. We identify the candidate features based on the structure of the Lustre/Titan write path, and use feature transformation functions to produce a model space with 135,000 candidate models. By searching for the minimal mean square error in this space we identify a good model and show that it is effective.
A simple one-step pulsed laser deposition (PLD) method has been applied to grow self-assembled metal-oxide nanocomposite thin films. The as-deposited Co-BaZrO3 films show high epitaxial quality with ultra-fine vertically aligned Co nanopillars (diameter <5 nm) embedded in a BZO matrix. The diameter of the nanopillars can be further tuned by varying the deposition frequency. The metal and oxide phases grow separately without inter-diffusion or mixing. Taking advantage of this unique structure, a high saturation magnetization of ∼1375 emu cm-3 in the Co-BaZrO3 nanocomposites has been achieved and further confirmed by Lorentz microscopy imaging in TEM. Furthermore, the coercivity values of this nanocomposite thin films range from 600 Oe (20 Hz) to 1020 Oe (2 Hz), which makes the nanocomposite an ideal candidate for high-density perpendicular recording media.
In an effort to characterize the fast neutron radiation background, 16 EJ-309 liquid scintillator cells were installed in the Radiological Multi-sensor Analysis Platform (RadMAP) to collect data in the San Francisco Bay Area. Each fast neutron event was associated with specific weather metrics (pressure, temperature, absolute humidity) and GPS coordinates. The expected exponential dependence of the fast neutron count rate on atmospheric pressure was demonstrated and event rates were subsequently adjusted given the measured pressure at the time of detection. Pressure adjusted data was also used to investigate the influence of other environmental conditions on the neutron background rate. Using National Oceanic and Atmospheric Administration (NOAA) coastal area lidar data, an algorithm was implemented to approximate sky-view factors (the total fraction of visible sky) for points along RadMAPs route. Three areas analyzed in San Francisco, Downtown Oakland, and Berkeley all demonstrated a suppression in the background rate of over 50% for the range of sky-view factors measured. This effect, which is due to the shielding of cosmic-ray produced neutrons by surrounding buildings, was comparable to the pressure influence which yielded a 32% suppression in the count rate over the range of pressures measured.
Radiation heat transfer is an important phenomenon in many physical systems of practical interest. When participating media is important, the radiative transfer equation (RTE) must be solved for the radiative intensity as a function of location, time, direction, and wavelength. In many heat-transfer applications, a quasi-steady assumption is valid, thereby removing time dependence. The dependence on wavelength is often treated through a weighted sum of gray gases (WSGG) approach. The discrete ordinates method (DOM) is one of the most common methods for approximating the angular (i.e., directional) dependence. The DOM exactly solves for the radiative intensity for a finite number of discrete ordinate directions and computes approximations to integrals over the angular space using a quadrature rule; the chosen ordinate directions correspond to the nodes of this quadrature rule. This paper applies a projection-based model-reduction approach to make high-order quadrature computationally feasible for the DOM for purely absorbing applications. First, the proposed approach constructs a reduced basis from (high-fidelity) solutions of the radiative intensity computed at a relatively small number of ordinate directions. Then, the method computes inexpensive approximations of the radiative intensity at the (remaining) quadrature points of a high-order quadrature using a reduced-order model constructed from the reduced basis. Finally, this results in a much more accurate solution than might have been achieved using only the ordinate directions used to compute the reduced basis. One- and three-dimensional test problems highlight the efficiency of the proposed method.
Polynomial chaos (PC)-based intrusive methods for uncertainty quantification reformulate the original deterministic model equations to obtain a system of equations for the PC coefficients of the model outputs. This system of equations is larger than the original model equations, but solving it once yields the uncertainty information for all quantities in the model. This chapter gives an overview of the literature on intrusive methods, outlines the approach on a general level, and then applies it to a system of three ordinary differential equations that model a surface reaction system. Common challenges and opportunities for intrusive methods are also highlighted.
When faced with a restrictive evaluation budget that is typical of today's highfidelity simulation models, the effective exploitation of lower-fidelity alternatives within the uncertainty quantification (UQ) process becomes critically important. Herein, we explore the use of multifidelity modeling within UQ, for which we rigorously combine information from multiple simulation-based models within a hierarchy of fidelity, in seeking accurate high-fidelity statistics at lower computational cost. Motivated by correction functions that enable the provable convergence of a multifidelity optimization approach to an optimal high-fidelity point solution, we extend these ideas to discrepancy modeling within a stochastic domain and seek convergence of a multifidelity uncertainty quantification process to globally integrated high-fidelity statistics. For constructing stochastic models of both the low-fidelity model and the model discrepancy, we employ stochastic expansion methods (non-intrusive polynomial chaos and stochastic collocation) computed by integration/interpolation on structured sparse grids or regularized regression on unstructured grids. We seek to employ a coarsely resolved grid for the discrepancy in combination with a more finely resolved Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the US Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000. Grid for the low-fidelity model. The resolutions of these grids may be defined statically or determined through uniform and adaptive refinement processes. Adaptive refinement is particularly attractive, as it has the ability to preferentially target stochastic regions where the model discrepancy becomes more complex, i.e., where the predictive capabilities of the low-fidelity model start to break down and greater reliance on the high-fidelity model (via the discrepancy) is necessary. These adaptive refinement processes can either be performed separately for the different grids or within a coordinated multifidelity algorithm. In particular, we present an adaptive greedy multifidelity approach in which we extend the generalized sparse grid concept to consider candidate index set refinements drawn from multiple sparse grids, as governed by induced changes in the statistical quantities of interest and normalized by relative computational cost. Through a series of numerical experiments using statically defined sparse grids, adaptive multifidelity sparse grids, and multifidelity compressed sensing, we demonstrate that the multifidelity UQ process converges more rapidly than a single-fidelity UQ in cases where the variance of the discrepancy is reduced relative to the variance of the high-fidelity model (resulting in reductions in initial stochastic error), where the spectrum of the expansion coefficients of the model discrepancy decays more rapidly than that of the high-fidelity model (resulting in accelerated convergence rates), and/or where the discrepancy is more sparse than the high-fidelity model (requiring the recovery of fewer significant terms).
In this paper, we consider the problem of falls risk prediction in elderly adults using smartphone-based inertial gait measurements. We begin by collecting a parallel data set from a pressure sensitive walkway and smartphones. The walk-way data is used to calculate the falls risk ground truth using well-established biomechanical norms. The smartphone data and falls risk labels are then used to train and evaluate both the one-class support vector machine (OC-SVM) and the support vector data description (SVDD) novelty detectors. In our evaluation, we find the SVDD has an average F1 score, used as a measure of classifier performance by equally weighting precision and recall, of 76% for females and 79% for males compared to 79%for a universal model. These results demonstrate the potential for predicting falls risk from smartphone data using novelty detection.
Verifying that hardware design implementations adhere to specifications is a time intensive and sometimes intractable problem due to the massive size of the system's state space. Formal methods techniques can be used to prove certain tractable specification properties; however, they are expensive, and often require subject matter experts to develop and solve. Nonetheless, hardware verification is a critical process to ensure security and safety properties are met, and encapsulates problems associated with trust and reliability. For complex designs where coverage of the entire state space is unattainable, prioritizing regions most vulnerable to security or reliability threats would allow efficient allocation of valuable verification resources. Stackelberg security games model interactions between a defender, whose goal is to assign resources to protect a set of targets, and an attacker, who aims to inflict maximum damage on the targets after first observing the defender's strategy. In equilibrium, the defender has an optimal security deployment strategy, given the attacker's best response. We apply this Stackelberg security framework to synthesized hardware implementations using the design's network structure and logic to inform defender valuations and verification costs. The defender's strategy in equilibrium is thus interpreted as a prioritization of the allocation of verification resources in the presence of an adversary. We demonstrate this technique on several open-source synthesized hardware designs.
We have measured photoionization-efficiency curves for pyrene, fluoranthene, chrysene, perylene, and coronene in the photon energy range of 7.5-10.2 eV and derived their photoionization cross-section curves in this energy range. All measurements were performed using tunable vacuum ultraviolet (VUV) radiation generated at the Advanced Light Source synchrotron at Lawrence Berkeley National Laboratory. The VUV radiation was used for photoionization, and detection was performed using a time-of-flight mass spectrometer. We measured the photoionization efficiency of 2,5-dimethylfuran simultaneously with those of pyrene, fluoranthene, chrysene, perylene, and coronene to obtain references of the photon flux during each measurement from the known photoionization cross-section curve of 2,5-dimethylfuran.
We present a critical evaluation of photoionization efficiency (PIE) measurements coupled with aerosol mass spectrometry for the identification of condensed soot-precursor species extracted from a premixed atmospheric-pressure ethylene/oxygen/nitrogen flame. Definitive identification of isomers by any means is complicated by the large number of potential isomers at masses likely to comprise particles at flame temperatures. This problem is compounded using PIE measurements by the similarity in ionization energies and PIE-curve shapes among many of these isomers. Nevertheless, PIE analysis can provide important chemical information. For example, our PIE curves show that neither pyrene nor fluoranthene alone can describe the signal from C16H10 isomers and that coronene alone cannot describe the PIE signal from C24H12 species. A linear combination of the reference PIE curves for pyrene and fluoranthene yields good agreement with flame-PIE curves measured at 202 u, which is consistent with pyrene and fluoranthene being the two major C16H10 isomers in the flame samples, but does not provide definite proof. The suggested ratio between fluoranthene and pyrene depends on the sampling conditions. We calculated the values of the adiabatic-ionization energy (AIE) of 24 C16H10 isomers. Despite the small number of isomers considered, the calculations show that the differences in AIEs between several of the isomers can be smaller than the average thermal energy at room temperature. The calculations also show that PIE analysis can sometimes be used to separate hydrocarbon species into those that contain mainly aromatic rings and those that contain significant aliphatic content for species sizes investigated in this study. Our calculations suggest an inverse relationship between AIE and the number of aromatic rings. We have demonstrated that further characterization of precursors can be facilitated by measurements that test species volatility. (Graph Presented).
We present an approach to optimize the cache locality for recursive programs by dynamically splicing-recursively interleaving-the execution of distinct function invocations. By utilizing data effect annotations, we identify concurrency and data reuse opportunities across function invocations and interleave them to reduce reuse distance. We present algorithms that efficiently track effects in recursive programs, detect interference and dependencies, and interleave execution of function invocations using user-level (non-kernel) lightweight threads. To enable multi-core execution, a program is parallelized using a nested fork/join programming model. Our cache optimization strategy is designed to work in the context of a random work-stealing scheduler. We present an implementation using the MIT Cilk framework that demonstrates significant improvements in sequential and parallel performance, competitive with a state-of-the-art compile-time optimizer for loop programs and a domainspecific optimizer for stencil programs.
Coherent anti-Stokes Raman spectroscopy (CARS) is a sensitive technique for probing highly luminous flames in combustion applications to determine temperatures and species concentrations. CARS thermometry has been demonstrated for the vibrational Q-branch and pure-rotational S-branch of several small molecules. Practical advantages of pure-rotational CARS, such as multi-species detection, reduction of coherent line mixing and collisional narrowing even at high pressures, and the potential for more precise thermometry, have motivated experimental and theoretical advances in S-branch CARS of nitrogen (N2), for example, which is a dominant species in air-fed combustion processes. Although hydrogen (H2) is of interest given its prevalence as a reactant and product in many gas-phase reactions, laser bandwidth limitations have precluded the extension of CARS thermometry to the H2 S-branch. We demonstrate H2 thermometry using hybrid femtosecond/picosecond pure-rotational CARS, in which a broadband pump/Stokes pulse enables simultaneous excitation of the set of H2 S-branch transitions populated at flame temperatures over the spectral region of 0-2200 cm-1. We present a pure-rotational H2 CARS spectral model for data fitting and compare extracted temperatures to those from simultaneously collected N2 spectra in two systems of study: a heated flow and a diffusion flame on a Wolfhard-Parker slot burner. From 300 to 650 K in the heated flow, the H2 and N2 CARS extracted temperatures are, on average, within 2% of the set temperature. For flame measurements, the fitted H2 and N2 temperatures are, on average, within 5% of each other from 300 to 1600 K. Our results confirm the viability of pure-rotational H2 CARS thermometry for probing combustion reactions.
The distinctive viscoelastic behavior of polymers results from a coupled interplay of motion on multiple length and time scales. Capturing the broad time and length scales of polymer motion remains a challenge. Using polyethylene (PE) as a model macromolecule, we construct coarse-grained (CG) models of PE with three to six methyl groups per CG bead and probe two critical aspects of the technique: pressure corrections required after iterative Boltzmann inversion (IBI) to generate CG potentials that match the pressure of reference fully atomistic melt simulations and the transferability of CG potentials across temperatures. While IBI produces nonbonded pair potentials that give excellent agreement between the atomistic and CG pair correlation functions, the resulting pressure for the CG models is large compared with the pressure of the atomistic system. We find that correcting the potential to match the reference pressure leads to nonbonded interactions with much deeper minima and slightly smaller effective bead diameter. However, simulations with potentials generated by IBI and pressure-corrected IBI result in similar mean-square displacements (MSDs) and stress autocorrelation functions G(t) for PE melts. While the time rescaling factor required to match CG and atomistic models is the same for pressure-and non-pressure-corrected CG models, it strongly depends on temperature. Transferability was investigated by comparing the MSDs and stress autocorrelation functions for potentials developed at different temperatures.
Zador, Judit; Fellows, Madison D.; Miller, James A.
In gas-phase combustion systems the interest in acetylene stems largely from its role in molecular weight growth processes. The consensus is that above 1500 K acetylene pyrolysis starts mainly with the homolytic fission of the C-H bond creating an ethynyl radical and an H atom. However, below ∼1500 K this reaction is too slow to initiate the chain reaction. It has been hypothesized that instead of dissociation, self-reaction initiates this process. Nevertheless, rigorous theoretical or direct experimental evidence is lacking, to an extent that even the molecular mechanism is debated in the literature. In this work we use rigorous ab initio transition-state theory master equation methods to calculate pressure- and temperature-dependent rate coefficients for the association of two acetylene molecules and related reactions. We establish the role of vinylidene, the high-energy isomer of acetylene in this process, compare our results with available experimental data, and assess the competition between the first-order and second-order initiation steps. We also show the effect of the rapid isomerization among the participating wells and highlight the need for time-scale analysis when phenomenological rate coefficients are compared to observed time scales in certain experiments. (Graph Presented).
This project makes use of "biomimetic behavioral engineering" in which adaptive strategies used by animals in the real world are applied to the development of autonomous robots. The key elements of the biomimetic approach are to observe and understand a survival behavior exhibited in nature, to create a mathematical model and simulation capability for that behavior, to modify and optimize the behavior for a desired robotics application, and to implement it. The application described in this report is dynamic soaring, a behavior that certain sea birds use to extract flight energy from laminar wind velocity gradients in the shallow atmospheric boundary layer directly above the ocean surface. Theoretical calculations, computational proof-of-principle demonstrations, and the first instrumented experimental flight test data for dynamic soaring are presented to address the feasibility of developing dynamic soaring flight control algorithms to sustain the flight of unmanned airborne vehicles (UAVs). Both hardware and software were developed for this application. Eight-foot custom foam sailplanes were built and flown in a steep shear gradient. A logging device was designed and constructed with custom software to record flight data during dynamic soaring maneuvers. A computational toolkit was developed to simulate dynamic soaring in special cases and with a full 6-degree of freedom flight dynamics model in a generalized time-dependent wind field. Several 3-dimensional visualization tools were built to replay the flight simulations. A realistic aerodynamics model of an eight-foot sailplane was developed using measured aerodynamic derivatives. Genetic programming methods were developed and linked to the simulations and visualization tools. These tools can now be generalized for other biomimetic behavior applications. This work was carried out in 2000 and 2001, and until now its results have only been available in an internal Sandia report.
We apply density-functional theory calculations to predict dopant modulation of electrical conductivity (σo) for seven dopants (C, Si, Ge, H, F, N, and B) sampled at 18 quantum molecular dynamics configurations of five independent insertion sites into two (high/low) baseline references of σo in amorphous Ta2O5, where each reference contains a single, neutral O vacancy center (VO0). From this statistical population (n = 1260), we analyze defect levels, physical structure, and valence charge distributions to characterize nanoscale modification of the atomistic structure in local dopant neighborhoods. C is the most effective dopant at lowering Ta2Ox σo, while also exhibiting an amphoteric doping behavior by either donating or accepting charge depending on the host oxide matrix. Both B and F robustly increase Ta2Ox σo, although F does so through elimination of Ta high charge outliers, while B insertion conversely creates high charge O outliers through favorable BO3 group formation, especially in the low σo reference. While N applications to dope and passivate oxides are prevalent, we found that N exacerbates the stochasticity of σo we sought to mitigate; sensitivity to the N insertion site and some propensity to form N-O bond chemistries appear responsible. We use direct first-principles predictions of σo to explore feasible Ta2O5 dopants to engineer improved oxides with lower variance and greater repeatability to advance the manufacturability of resistive memory technologies.
Finelli, Andrew; Willett, Peter; Bar-Shalom, Yaakov; Melgaard, David K.; Byrne, Raymond
A method for tracking streaking targets (targets whose signatures are spread across multiple pixels in a focal plane array) is developed. The outputs of a bank of matched filters are thresholded and then used for measurement extraction. The use of the Deep Target Extractor (DTE, previously called the MLPMHT) allows for tracking in the very low observable (VLO) environment common when a streaking target is present. A definition of moving target signal to noise ratio (MT-SNR) is also presented as a metric for trackability. The extraction algorithm and the DTE are then tested across several variables, including trajectory, MT-SNR, and streak length. The DTE and measurement extraction process performs remarkably well in this difficult tracking environment on these data features.
Parasitic resistances cause devices in a resistive memory array to experience different read/write voltages depending on the device location, resulting in uneven writes and larger leakage currents. We present a new method to compensate for this by adding extra series resistance to the drivers to equalize the parasitic resistance seen by all the devices. This allows for uniform writes, enabling multi-level cells with greater numbers of distinguishable levels, and reduced write power, enabling larger arrays.
Safety assessments estimate the long-term performance of geological disposal systems for radioactive waste using quantitative models. This paper reviews regulatory standards, selection of scenarios for analysis, the development of computational models and their linkage into a system analysis, and the iterative relationship between site characterization and safety assessment. Uncertainty must be acknowledged and can be accounted for using both conservative deterministic and probabilistic approaches. In addition to generating performance estimates for comparison to regulatory standards, safety assessments can also guide research and model development, evaluate design alternatives, enhance the scientific understanding of the system, and contribute to public acceptance.
Solid state NMR spectroscopy is inherently sensitive to chemical structure and composition, and thus makes an ideal method to probe the heterogeneity of multicomponent polymers. Specifically, NMR spin diffusion experiments can be used to extract reliable information about spatial domain sizes on multiple length scales, provided that magnetization selection of one domain can be achieved. In this paper, we demonstrate the preferential filtering of protons in fluorinated domains during NMR spin diffusion exper-iments using 1H-19F heteronuclear dipolar dephasing based on rotational echo double resonance (REDOR) MAS NMR techniques. Three pulse sequence variations are demonstrated based on the different nuclei detected: direct 1H detection, plus both 1H→13C cross polarization (CP) and 1H→19F CP detection schemes. This 1H-19F REDOR-filtered spin diffusion method was used to measure fluorinated domain sizes for a complex polymer blend. The efficacy of the REDOR-based spin filter does not rely on spin relaxation behavior or chemical shift differences, and thus is applicable for performing NMR spin diffu-sion experiments in samples where traditional magnetization filters may prove unsuccessful. This REDOR-filtered NMR spin diffusion method can also be extended to other samples where a heteronuclear spin pair exists that is unique to the domain of interest.
Vacuum gap λ/2 microwave resonators are demonstrated as a route toward higher integration in superconducting qubit circuits. The resonators are fabricated from pieces on two silicon chips bonded together with an In-Sb bond. Measurements of the devices yield resonant frequencies in good agreement with simulations. Creating low loss circuits in this geometry is also discussed.
Journal of Verification, Validation and Uncertainty Quantification
Pathmanathan, Pras; Gray, Richard A.; Romero, Vicente J.; Morrison, Tina M.
Computational modeling has the potential to revolutionize medicine the way it transformed engineering. However, despite decades of work, there has only been limited progress to successfully translate modeling research to patient care. One major difficulty which often occurs with biomedical computational models is an inability to perform validation in a setting that closely resembles how the model will be used. For example, for a biomedical model that makes in vivo clinically relevant predictions, direct validation of predictions may be impossible for ethical, technological, or financial reasons. Unavoidable limitations inherent to the validation process lead to challenges in evaluating the credibility of biomedical model predictions. Therefore, when evaluating biomedical models, it is critical to rigorously assess applicability, that is, the relevance of the computational model, and its validation evidence to the proposed context of use (COU). However, there are no well-established methods for assessing applicability. Here, we present a novel framework for performing applicability analysis and demonstrate its use with a medical device computational model. The framework provides a systematic, step-by-step method for breaking down the broad question of applicability into a series of focused questions, which may be addressed using supporting evidence and subject matter expertise. The framework can be used for model justification, model assessment, and validation planning. While motivated by biomedical models, it is relevant to a broad range of disciplines and underlying physics. The proposed applicability framework could help overcome some of the barriers inherent to validation of, and aid clinical implementation of, biomedical models.
The structural properties of reported inorganic scandium (Sc) salts were reviewed, including the halide (Cl, Br, and I), nitrate, sulfate, and phosphate salts. Additional analytical techniques used for characterization of these complexes (metrical data, FTIR and 45Sc NMR spectroscopy) were tabulated. A structural comparison of Sc to select lanthanide (La, Gd, Lu) salt complexes was briefly evaluated.
Ultrafast control of the polarization state of light may enable a plethora of applications in optics, chemistry and biology. However, conventional polarizing elements, such as polarizers and waveplates, are either static or possess only gigahertz switching speeds. Here, with the aid of high-mobility indium-doped cadmium oxide (CdO) as the gateway plasmonic material, we realize a high-quality factor Berreman-type perfect absorber at a wavelength of 2.08 μm. On sub-bandgap optical pumping, the perfect absorption resonance strongly redshifts because of the transient increase of the ensemble-averaged effective electron mass of CdO, which leads to an absolute change in the p-polarized reflectance from 1.0 to 86.3%. By combining the exceedingly high modulation depth with the polarization selectivity of the perfect absorber, we experimentally demonstrate a reflective polarizer with a polarization extinction ratio of 91 that can be switched on and off within 800 fs.
Properties of NbN and TaxN thin films grown at ambient temperatures on SiO2/Si substrates by reactive-pulsed laser deposition and reactive magnetron sputtering (MS) as a function of N2 gas flow were investigated. Both techniques produced films with smooth surfaces, where the surface roughness did not depend on the N2 gas flow during growth. High crystalline quality, (111) oriented NbN films with Tc up to 11 K were produced by both techniques for N contents near 50%. The low temperature transport properties of the TaxN films depended upon both the N2 partial pressure used during growth and the film thickness. The root mean square surface roughness of TaxN films grown by MS increased as the film thickness decreased down to 10 nm.
Missert, Nancy; Kotula, Paul G.; Rye, Michael J.; Rehm, Laura; Sluka, Volker; Kent, Andrew D.; Yohannes, Daniel; Kirichenko, Alex F.; Vernik, Igor V.; Mukhanov, Oleg A.; Bolkhovsky, Vladimir; Wynn, Alex; Johnson, Leonard; Gouker, Mark
A focused ion beam was used to obtain cross-sectional specimens from both magnetic multilayer and Nb/Al-AlOx/Nb Josephson junction devices for characterization by scanning transmission electron microscopy (STEM) and energy dispersive X-ray spectroscopy (EDX). Automated multivariate statistical analysis of the EDX spectral images produced chemically unique component images of individual layers within the multilayer structures. STEM imaging elucidated distinct variations in film morphology, interface quality, and/or etch artifacts that could be correlated to magnetic and/or electrical properties measured on the same devices.
The rapid increase in penetration of distributed energy resources on the electric power distribution system has created a need for more comprehensive interconnection modelling and impact analysis. Unlike conventional scenario - based studies , quasi - static time - series (QSTS) simulation s can realistically model time - dependent voltage controllers and the diversity of potential impacts that can occur at different times of year . However, to accurately model a distribution system with all its controllable devices, a yearlong simulation at 1 - second resolution is often required , which could take conventional computers a computational time of 10 to 120 hours when an actual unbalanced distribution feeder is modeled . This computational burden is a clear l imitation to the adoption of QSTS simulation s in interconnection studies and for determining optimal control solutions for utility operations . Our ongoing research to improve the speed of QSTS simulation has revealed many unique aspects of distribution system modelling and sequential power flow analysis that make fast QSTS a very difficult problem to solve. In this report , the most relevant challenges in reducing the computational time of QSTS simulations are presented: number of power flows to solve, circuit complexity, time dependence between time steps, multiple valid power flow solutions, controllable element interactions, and extensive accurate simulation analysis.
A 9.6 kW test array of Prism bifacial modules and reference monofacial modules installed in February 2016 at the New Mexico Regional Test Center has produced one year of performance data. The data reveal that the Prism modules are out-performing the monofacial modules, with bifacial gains in energy over the twelve-month period ranging from 17% to 132%, depending on the orientation and ground albedo. These measured bifacial gains were found to be in good agreement with modeled bifacial gains using equations previously published by Prism Solar. The most dramatic increase in performance was seen among the vertically mounted, west-facing modules, where the bifacial modules produced more than double the energy of monofacial modules in the same orientation. Because peak energy generation (mid- morning and mid-afternoon) for these bifacial modules may best match load on the electric grid, the west-facing orientation may be more economically desirable than traditional south-facing module orientations (which peak at solar noon).
Sandia National Laboratories, New Mexico (SNL/NM) is a government-owned/contractoroperated laboratory. National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., manages and operates SNL/NM for the U.S. Department of Energy (DOE), National Nuclear Security Administration (NNSA). The DOE/NNSA Sandia Field Office administers the contract and oversees contractor operations at the site. Two types of groundwater surveillance monitoring are conducted at SNL/NM: (1) on a site-wide basis as part of the SNL/NM Long-Term Stewardship (LTS) Program’s Groundwater Monitoring Program (GMP) Groundwater Surveillance Task and (2) on a site-specific groundwater monitoring at LTS/Environmental Restoration (ER) Operations sites with ongoing groundwater investigations. This Annual Groundwater Monitoring Report summarizes data collected during groundwater monitoring events conducted at GMP locations and at the following SNL/NM sites through December 31, 2016: Burn Site Groundwater Area of Concern (AOC); Chemical Waste Landfill; Mixed Waste Landfill; Technical Area-V Groundwater AOC; and the Tijeras Arroyo Groundwater AOC. Environmental monitoring and surveillance programs are required by the New Mexico Environment Department (NMED) and DOE Order 436.1, Departmental Sustainability, and DOE Order 231.1B, Environment, Safety, and Health Reporting.
This report provides a summary of the radionuclide releases from the United States (U.S.) Department of Energy (DOE) National Nuclear Security Administration facilities at Sandia National Laboratories, New Mexico (SNL/NM) during Calendar Year (CY) 2016, including the data, calculations, and supporting documentation for demonstrating compliance with 40 Code of Federal Regulation (CFR) 61, Subpart H--NATIONAL EMISSION STANDARDS FOR EMISSIONS OF RADIONUCLIDES OTHER THAN RADON FROM DEPARTMENT OF ENERGY FACILITIES (Radiological NESHAP). A description is given of the sources and their contributions to the overall dose assessment. In addition, the maximally exposed individual (MEI) radiological dose calculation and the population dose to local and regional residents are discussed.
In recent years, insider threat programs have become an important aspect of nuclear security, and nuclear security training courses. However, many nuclear security insider threat programs fail to address the insider threat attack and monitoring potential that exists on information technology (IT) systems. This failure is critical because of the importance of information technology and networks in today’s world. IT systems offer an opportunity to perpetrate dangerous insider attacks, but they also present an opportunity to monitor for them and prevent them. This paper suggests a number of best practices for monitoring and preventing insider attacks on IT systems, and proposes the development of a new IT insider threat tabletop that can be used to help train nuclear security practitioners on how best to implement IT insider threat prevention best practices. The development of IT insider threat best practices and a practical tabletop exercise will allow nuclear security practitioners to improve nuclear security trainings as it integrates a critical part of insider threat prevention into the broader nuclear security system.
This report uses the CMIP5 series of climate model simulations to produce country- level uncertainty distributions for use in socioeconomic risk assessments of climate change impacts. It provides appropriate probability distributions, by month, for 169 countries and autonomous-areas on temperature, precipitation, maximum temperature, maximum wind speed, humidity, runoff, soil moisture and evaporation for the historical period (1976-2005), and for decadal time periods to 2100. It also provides historical and future distributions for the Arctic region on ice concentration, ice thickness, age of ice, and ice ridging in 15-degree longitude arc segments from the Arctic Circle to 80 degrees latitude, plus two polar semicircular regions from 80 to 90 degrees latitude. The uncertainty is meant to describe the lack of knowledge rather than imprecision in the physical simulation because the emphasis is on unfalsified risk and its use to determine potential socioeconomic impacts. The full report is contained in 27 volumes.
This User Manual contains a description of the wired and infrared (IR) variants of the Chain of Custody Item Monitor (CoCIM), the Coordinator for reading stored messages, and the inspector Message Viewer user interface (UI) software, as well as instructions for use. This manual does not include descriptions or use instructions for the radio frequency (RF) variant of the CoCIM. The intended audience is planners and participants in treaty verification exercises where chain of custody (CoC) elements are required.
Cybersecurity is essential to maintaining operations, and is now a de facto cost of business. Despite this, there is little consensus on how to systematically make decisions about cyber countermeasures investments. Identifying gaps and determining the expected return on investment (ROI) of adding a new cybersecurity countermeasure is frequently a hand-waving exercise at best. Worse, cybersecurity nomenclature is murky and frequently over-loaded, which further complicates issues by inhibiting clear communication. This paper presents a series of foundational models and nomenclature for discussing cybersecurity countermeasures, and then introduces the Cyber Defense (CyDef) model, which provides a systematic and intuitive way for decision-makers to effectively communicate with operations and device experts.
This report uses the CMIP5 series of climate model simulations to produce country- level uncertainty distributions for use in socioeconomic risk assessments of climate change impacts. It provides appropriate probability distributions, by month, for 169 countries and autonomous-areas on temperature, precipitation, maximum temperature, maximum wind speed, humidity, runoff, soil moisture and evaporation for the historical period (1976-2005), and for decadal time periods to 2100. It also provides historical and future distributions for the Arctic region on ice concentration, ice thickness, age of ice, and ice ridging in 15-degree longitude arc segments from the Arctic Circle to 80 degrees latitude, plus two polar semicircular regions from 80 to 90 degrees latitude. The uncertainty is meant to describe the lack of knowledge rather than imprecision in the physical simulation because the emphasis is on unfalsified risk and its use to determine potential socioeconomic impacts. The full report is contained in 27 volumes.
This report uses the CMIP5 series of climate model simulations to produce country- level uncertainty distributions for use in socioeconomic risk assessments of climate change impacts. It provides appropriate probability distributions, by month, for 169 countries and autonomous-areas on temperature, precipitation, maximum temperature, maximum wind speed, humidity, runoff, soil moisture and evaporation for the historical period (1976-2005), and for decadal time periods to 2100. It also provides historical and future distributions for the Arctic region on ice concentration, ice thickness, age of ice, and ice ridging in 15-degree longitude arc segments from the Arctic Circle to 80 degrees latitude, plus two polar semicircular regions from 80 to 90 degrees latitude. The uncertainty is meant to describe the lack of knowledge rather than imprecision in the physical simulation because the emphasis is on unfalsified risk and its use to determine potential socioeconomic impacts. The full report is contained in 27 volumes.
This report uses the CMIP5 series of climate model simulations to produce country- level uncertainty distributions for use in socioeconomic risk assessments of climate change impacts. It provides appropriate probability distributions, by month, for 169 countries and autonomous-areas on temperature, precipitation, maximum temperature, maximum wind speed, humidity, runoff, soil moisture and evaporation for the historical period (1976-2005), and for decadal time periods to 2100. It also provides historical and future distributions for the Arctic region on ice concentration, ice thickness, age of ice, and ice ridging in 15-degree longitude arc segments from the Arctic Circle to 80 degrees latitude, plus two polar semicircular regions from 80 to 90 degrees latitude. The uncertainty is meant to describe the lack of knowledge rather than imprecision in the physical simulation because the emphasis is on unfalsified risk and its use to determine potential socioeconomic impacts. The full report is contained in 27 volumes.
This report uses the CMIP5 series of climate model simulations to produce country- level uncertainty distributions for use in socioeconomic risk assessments of climate change impacts. It provides appropriate probability distributions, by month, for 169 countries and autonomous-areas on temperature, precipitation, maximum temperature, maximum wind speed, humidity, runoff, soil moisture and evaporation for the historical period (1976-2005), and for decadal time periods to 2100. It also provides historical and future distributions for the Arctic region on ice concentration, ice thickness, age of ice, and ice ridging in 15-degree longitude arc segments from the Arctic Circle to 80 degrees latitude, plus two polar semicircular regions from 80 to 90 degrees latitude. The uncertainty is meant to describe the lack of knowledge rather than imprecision in the physical simulation because the emphasis is on unfalsified risk and its use to determine potential socioeconomic impacts. The full report is contained in 27 volumes.
