Triangle counting is a foundational graph-analysis kernel in network science. It has also been one of the challenge problems for the 'Static Graph Challenge'. In this work, we propose a novel, hybrid, parallel triangle counting algorithm based on its linear algebra formulation. Our framework uses MPI and Cilk to exploit the benefits of distributed-memory and shared-memory parallelism, respectively. The problem is partitioned among MPI processes using a two-dimensional (2D) Cartesian block partitioning. One-dimensional (1D) rowwise partitioning is used within the Cartesian blocks for shared-memory parallelism using the Cilk programming model. Besides exhibiting very good strong scaling behavior in almost all tested graphs, our algorithm achieves the fastest time on the 1.4B edge real-world twitter graph, which is 3.217 seconds, on 1,092 cores. In comparison to past distributed-memory parallel winners of the graph challenge, we demonstrate a speed up of 2.7× on this twitter graph. This is also the fastest time reported for parallel triangle counting on the twitter graph when the graph is not replicated.
This report documents the initial testing of the Sandia Parallel Aerodynamics and Reentry Code (SPARC) to directly simulate hypersonic, turbulent boundary layer flow over a sharp 7- degree half-angle cone. This type of computation involves a tremendously large range of scales both in time and space, requiring a large number of grid cells and the efficient utilization of a large pool of resources. The goal of the simulation is to mimic and verify a wind tunnel experiment that seeks to measure the turbulent surface pressure fluctuations. These data are necessary for building a model to predict random vibration loading in the reentry flight environment. A low-dissipation flux scheme in SPARC is used on a 2.7 billion cell mesh to capture the turbulent fluctuations in the boundary layer flow. The grid is divided into 115200 partitions and simulated using the Knight's Landings (KNL) partition of the Trinity system. The parallel performance of SPARC is explored on the Trinity system, as well as some of the other new architectures. Extracting data from the simulation shows good agreement with the experiment as well as a colleague's simulation. The data provide a guide for which a new model can be built for better prediction of the reentry random vibration loads.
Atmospheric ice affects Earth's radiative properties and initiates most precipitation. Growing ice typically requires a particle, often airborne mineral dust, e.g., to catalyze freezing of supercooled cloud droplets. How chemistry, structure and morphology determine the ice-nucleating ability of minerals remains elusive. Not surprisingly, poor understanding of a erosol-cloud interactions is a major source of uncertainty in climate models. In this project, we combine d optical microscopy with atomic force microscopy to explore the mechanisms of initial ice formation on alkali feldspar, a mineral proposed to dominate ice nucleation in Earth's atmosphere. When cold air becomes supersaturated with respect to water, we discovered that supercooled liquid water condenses at steps without having to overcome a nucleation barrier, and subsequently freezes quickly. Our results imply that steps, common even on macroscopically flat feldspar surfaces, can accelerate water condensation followed by freezing, thus promoting glaciation and dehydration of mixed - phase clouds. Motivated by the fact that current climate simulations do not properly account for feldspar's extreme efficiency to nucleate ice, we modified DOE's climate model, the Energy Exascale Earth System Model (E3SM), to increase the activation of ice nucleation on feldspar dust. This included adding a new aerosol tracer into the model and updating the ice nucleation parameterization, based on Classical Nucleation Theory, for multiple mineral dust tracers. Although t he se modifications have little impact on global averages , predictions of regional averages can be strongly affected .
Interval Assignment (IA) means selecting the number of mesh edges for each CAD curve. IIA is a discrete algorithm over integers. A priority queue iteratively selects compatible sets of intervals to increase in lock-step by integers. In contrast, the current capability in Cubit is floating-point Linear Programming with Branch-and-Bound for integerization (BBIA).
The failure of subsurface seals (i.e., wellbores, shaft and drift seals in a deep geologic nuclear waste repository) has important implications for US Energy Security. The performance of these cementitious seals is controlled by a combination of chemical and mechanical forces, which are coupled processes that occur over multiple length scales. The goal of this work is to improve fundamental understanding of cement-geomaterial interfaces and develop tools and methodologies to characterize and predict performance of subsurface seals. This project utilized a combined experimental and modeling approach to better understand failure at cement-geomaterial interfaces. Cutting-edge experimental methods and characterization methods were used to understand evolution of the material properties during chemo-mechanical alteration of cement-geomaterial interfaces. Software tools were developed to model chemo-mechanical coupling and predict the complex interplay between reactive transport and solid mechanics. Novel, fit-for-purpose materials were developed and tested using fundamental understanding of failure processes at cement-geomaterial interfaces.
Local electromagnetic probing was developed to allow investigation of a variety of devices in noisy electrical environments. The quality and applicability of this technique was assessed during this one year LDRD. To obtain details about the experimental setup, the devices imaged, and the experimental details, please refer to the classified report from the project manager, Will Zortman, or the NSP IA lead, Kristina Czuchlewski.
In this report we describe an enhanced methodology for performing stochastic Bayesian inversions of atmospheric trace gas inversions that allows the time variation of model parameters to be inferred. We use measurements of methane atmospheric mixing ratio made in Livermore, California along with atmospheric transport modeling and published prior estimates of emissions to estimate the regional emissions of methane and the temporal variations in inferred bias parameters. We compute Bayesian model evidence and continuous rank probability score to optimize the model with respect to temporal resolution. Using two different emissions inventories, we perform inversions for a series of models with increasing temporal resolution in the model bias representation. We show that temporal variation in the model bias can improve the model fit and can also increase the likelihood that the parameterization is appropriate, as measured by the Bayesian model evidence.
The overall goal of this work was to advance the integrated workflow capabilities to provide the weapon system analysts the tools to construct, communicate and robustly execute end-to-end computational simulation models and analysis. Specifically, this includes developing and running automated, CompSim workflows that map model inputs to responses from large-scale studies executed on a distributed, heterogenous computing environment (e.g., CAD applications on Windows laptop to Sierra FEM codes on Trinity within a single workflow). The workflows developed enable ensemble calculations in support of ND program decisions, including sensitivity analysis, optimization and uncertainty quantification.
This report details initial investigations of neural network surrogate models for combustion applications. The models are assessed with respect to averaged predictive fidelity over a test data set as well as with respect to the accuracy of resolving landmark statistics of interest, as a function of increasing training data volume.
Remote sensing (RS) data collection capabilities are rapidly evolving hyper-spectrally (sensing more spectral bands), hyper-temporally (faster sampling rates) and hyper-spatially (increasing number of smaller pixels). Accordingly, sensor technologies have outpaced transmission capa- bilities introducing a need to process more data at the sensor. While many sophisticated data processing capabilities are emerging, power and other hardware requirements for these approaches on conventional electronic systems place them out of context for resource constrained operational environments. To address these limitations, in this research effort we have investigated and char- acterized neural-inspired architectures to determine suitability for implementing RS algorithms In doing so, we have been able to highlight a 100x performance per watt improvement using neu- romorphic computing as well as developed an algorithmic architecture co-design and exploration capability.
Many shock experiments, whether impact, laser, or magnetically driven, use reflected optical light from shocked samples to diagnose their material properties. Specifically, optical velocimetry diagnostics, which do not require absolute power measurements, are regularly used to obtain equation-of-state information of materials. However, new diagnostics will be necessary to expand the realm of measured material properties, and many useful diagnostic techniques do require absolute measurements. Thus, it is important to understand what happens at the reflective surface of shock experiments, and the effect scattering has on the light collection of optical probes. To this end, we present results from experiments done to observe the behavior of a reflected beam from a specular coating on an optical window during shock impact. We find that the specular condition of the coating is adversely affected by the shock front, but this can be mitigated by minimizing roughness on the surface preceding the coating.
This report describes the potential of a novel class of materials—α-ZrW2O8, Zr2WP2O12, and related compounds that contract upon amorphization as possible radionuclide waste-forms. The proposed ceramic waste-forms would consist of zoned grains, or sintered ceramics with center- loaded radionuclides and barren shells. Radiation-induced amorphization would result in core shrinkage but would not fracture the shells or overgrowths, maintaining isolation of the radionuclide. In this report, we have described synthesis techniques to produce phase-pure forms of the materials, and how to fully densify those materials. Structural models for the materials were developed and validated using DFPT approaches, and radionuclide substitution was evaluated; U(IV), Pu(IV), Tc(IV) and Tc(VII) all readily substitute into the material structures. MD modeling indicated that strain associated with radiation-induced amorphization would not affect the integrity of surrounding crystalline materials, and these results were validated via ion beam experimental studies. Finally, we have evaluated the leach rates of the barren materials, as determined by batch and flow-through reactor experiments. ZrW2O8 leaches rapidly, releasing tungstate while Zr is retained as a solid oxide or hydroxide. Tungsten release rates remain elevated over time and are highly sensitive to contact times, suggesting that this material will not be an effective waste-form. Conversely, tungsten releases rates from Zr2WP2O12 rapidly drop, show little dependence on short-term changes in fluid contact time, and in over time, become tied to P release rates. The results presented here suggest that this material may be a viable waste-form for some hard-to-handle radionuclides such as Pu and Tc.
This progress report describes work done in FY19 at Sandia National Laboratories (SNL) to assess the localized corrosion performance of container/cask materials used in the interim storage of spent nuclear fuel (SNF). Of particular concern is stress corrosion cracking (SCC), by which a through-wall crack could potentially form in a canister outer wall over time intervals that are shorter than possible dry storage times. Work in FY19 refined our understanding of the chemical and physical environment on canister surfaces and evaluated the relationship between chemical and physical environment and the form and extent of corrosion that occurs.
This report summarizes the methods and algorithms that were developed on the Sandia National Laboratory LDRD project entitled "Polynomial Chaos methods in Xyce for Embedded Uncertainty Quantification in Circuit Analysis", which was project 200265 and proposal 2019-0817. As much of our work has been published in other reports and publications, this report gives a brief summary. Those who are interested in the technical details are encouraged to read the full published results and also contact the report authors for the status of follow-on projects.
This research aims to develop brain-inspired solutions for reliable and adaptive autonomous navigation in systems that have limited internal and external sensors and may not have access to reliable GPS information. The algorithms investigated and developed by this project was performed in the context of Sandas A4H (autonomy for hypersonics) mission campaign. These algorithms were additionally explored with respect to their suitability for implementation on emerging neuromorphic computing hardware technology. This project is premised on the hypothesis that brain-inspired SLAM (simultaneous localization and mapping) algorithms may provide an energy-efficient, context-flexible approach to robust sensor-based, real-time navigation.
This report documents work done at the Sandia Ion Beam Laboratory to develop a capability to produce 14 Me neutrons at levels sufficient for testing radiation effects on electronic materials and components. The work was primarily enabled by a laboratory directed research and development (LDRD) project. The main elements of the work were to optimize target lifetime, test a new thin- film target design concept to reduce tritium usage, design and construct a new target chamber and beamline optimized for high-flux tests, and conduct tests of effects on electronic devices and components. These tasks were all successfully completed. The improvements in target performance and target chamber design have increased the flux and fluence of 14 MV neutrons available at the test location by several orders of magnitude. The outcome of the project is that a new capability for testing radiation-effects on electronic components from 14 MeV neutrons is now available at Sandia National Laboratories. This capability has already been extensively used for many qualification and component evaluation and development tests.
Under its Grid Modernization Initiative, the U.S. Department of Energy(DOE),in collaboration with energy industry stakeholders developed a multi-year research plan to support modernizing the electric grid. One of the foundational projects for accelerating modernization efforts is information and communications technology interoperability. A key element of this project has been the development of a methodology for engaging ecosystems related to grid integration to create roadmaps that advance the ease of integration of related smart technology. This document is the product of activities undertaken in 2017 through 2019.It provides a Cybersecurity Plan describing the technology to be adopted in the project with details as per the GMLC Call document.
Uncertainty quantification is recognized as a fundamental task to obtain predictive numerical simulations. However, many realistic engineering applications require complex and computationally expensive high-fidelity numerical simulations for the accurate characterization of the system responses. Moreover, complex physical models and extreme operative conditions can easily lead to hundreds of uncertain parameters that need to be propagated through high-fidelity codes. Under these circumstances, a single fidelity approach, i.e. a workflow that only uses high-fidelity simulations to perform the uncertainty quantification task, is unfeasible due to the prohibitive overall computational cost. In recent years, multifidelity strategies have been introduced to overcome this issue. The core idea of this family of methods is to combine simulations with varying levels of fidelity/accuracy in order to obtain the multifidelity estimators or surrogates with the same accuracy of their single fidelity counterparts at a much lower computational cost. This goal is usually accomplished by defining a prioria sequence of discretization levels or physical modeling assumptions that can be used to decrease the complexity of a numerical realization and thus its computational cost. However ,less attention has been dedicated to low-fidelity models that can be built directly from the small number of high-fidelity simulations available. In this work we focus our attention on Reduced-Order Models that can be considered a particular class of data-driven approaches. Our main goal is to explore the combination of multifidelity uncertainty quantification and reduced-order models to obtain an efficient framework for propagating uncertainties through expensive numerical codes.
There are numerous applications that combine data collected from sensors with machine-learning based classification models to predict the type of event or objects observed. Both the collection of the data itself and the classification models can be tuned for optimal performance, but we hypothesize that additional gains can be realized by jointly assessing both factors together. Through this research, we used a seismic event dataset and two neural network classification models that issued probabilistic predictions on each event to determine whether it was an earthquake or a quarry blast. Real world applications will have constraints on data collection, perhaps in terms of a budget for the number of sensors or on where, when, or how data can be collected. We mimicked such constraints by creating subnetworks of sensors with both size and locational constraints. We compare different methods of determining the set of sensors in each subnetwork in terms of their predictive accuracy and the number of events that they observe overall. Additionally, we take the classifiers into account, treating them both as black-box models and testing out various ways of combining predictions among models and among the set of sensors that observe any given event. We find that comparable overall performance can be seen with less than half the number of sensors in the full network. Additionally, a voting scheme that uses the average confidence across the sensors for a given event shows improved predictive accuracy across nearly all subnetworks. Lastly, locational constraints matter, but sometimes in unintuitive ways, as better-performing sensors may be chosen instead of the ones excluded based on location. This being a short-term research effort, we offer a lengthy discussion on interesting next-steps and ties to other ongoing research efforts that we did not have time to pursue. These include a detailed analysis of the subnetwork performance broken down by event type, specific location, and model confidence. This project also included a Campus Executive research partnership with Texas A&M University. Through this, we worked with a professor and student to study information gain for UAV routing. This was an alternative way of looking at the similar problem space that includes sensor operation for data collection and the resulting benefit to be gained from it. This work is described in an Appendix.
We demonstrate the ability to measure R-curves of brittle materials using a method adapted from Theo Fett et al. The method is validated with a NIST standard reference material and demonstrated using Si3N4 of two different microstructures; glass-ceramic, and PZT. As expected, each material's R-curve is seen to be slightly different with glass-ceramics showing the most pronounced R-curve effects. Plans for future applications and experimental efforts are discussed.
The ability to surface engineer structures or components using coatings made by the thermal spray processes is very common practice and offers great design flexibility with traditional structure metallic substrates (e.g., Al, Steel, Ti). However, the joining of high melting temperature materials to a polymeric substrate presents a problem due to the melt deposition coating formation mechanism locally subjecting the polymer substrate to temperatures exceeding the limits of the polymer. Thus, it was desired to modify the surface of a polymer so that a thin metallic film could be robustly bonded to the polymer and act as a heat sink for impinging molten droplets from a thermal spray process and allow a thick film coating to be built upon the polymer.
Sandia National Laboratories (SNL) was contracted by the Defense Threat Reduction Agency (DTRA), through KBRwyle to perform testing and evaluation of the SNL Smart Pre-concentrator (SPC) system and a COTS FTIR system procured by DTRA through KBRwyle. Two common chemical warfare agent simulants, dimethyl methylphosphonate and triethyl phosphate were selected as the compounds of interest. SNL tested both systems using a COTS vapor generation system, capable of delivering known concentrations of specific chemical compounds to both detection systems, with Sandia responsible for the SPC system. Both systems were measured against COTS sorbent collection tubes analyzed by SNL via a laboratory GCMS system. Concentrations measured from tubes upstream from the FTIR system differed from the expected concentrations, while downstream tubes were mostly within 20% of the target concentration.