Objectives of the RIA Course review: (1) Identify ways to improve the existing risk course and tool, and improve course outcomes for participant nations; (2) Ensure that course and tool content are consistent with best practices from the risk community; and (3) Improve the utility of "leave-behinds" for participant nations.
As the world becomes increasingly more globalized, seemingly obscure global regions have begun to rise as growing platforms for national power. Perhaps one of the most significant, and fast-growing region is the Arctic. The Arctic has become a hub for international activities, it's importance being identified by its expansive natural resources and proximity of nations. There are eight nations that have claimed geographic stake in the Arctic: Canada, The Kingdom of Denmark, Finland, Iceland, Norway, the Russian Federation, Sweden, and the United States of America.
The computational power of HPC is beyond our comprehension when we hear that 5 quadrillion computations can happen in a matter of seconds, or that machine learning is changing the way everything works. But none of that happens in a vacuum, and the teams behind the scenes—the developers of the hardware, the operating systems, the data transfer protocols, and the applications themselves—are the unsung heroes of a world where faster is better and you'd better hope there's no bug in the software or the hardware to slow you down. HPC is most successful when all these aspects work together seamlessly. The stories that follow are a tribute to the hardworking teams behind the scenes.
This document represents the results of deliverable D05.02 (Identify relevant efforts at SNL and other institutions) under the activity area Relevant Efforts Review. The goal of the Relevant Efforts Review activity is to identify relevant data integration efforts at SNL and possibly other institutions and compile lessons learned that are relevant to the development of a framework for data integration efforts in support of analysts and decision makers. The intent of this activity is to provide, by examples, context of how the requirements-gathering process has already been implemented in other instances and to guide the development of such a process for OCIA's needs. Information for this report was gathered through SNL staff interviews and the team members' knowledge and project experiences.
The idea of acausality for control of a wave energy converter (WEC) is a concept that has been popular since the birth of modern wave energy research in the 1970s. This concept has led to considerable research into wave prediction and feedforward WEC control algorithms. However, the findings in this report mostly negate the need for wave prediction to improve WEC energy absorption, and favor instead feedback driven control strategies. Feedback control is shown to provide performance that rivals a prediction-based controller, which has been unrealistically assumed to have perfect prediction. It is well known in classical control engineering that perfect knowledge of past and future events will always lead to higher performing systems. However, it is also well known that the underlying system must be well-designed; control cannot fix a bad design. Additionally, one must consider the practical application of a control design, which relies on measurements and actuation systems. There are major implications to cost and reliability when relying on remote sensors requiring real-time data-streaming (e.g., remote wave buoys). This report shows that for a well-designed WEC, in which closed loop dynamics is considered since early stages of design, a suboptimal controller using no prediction can achieve more than 90% of the theoretical maximum. A predictionless feedback resonating (FBR) controller performs within 0.1% percent of a controller with perfect future knowledge (something which is not practically attainable). Given the major challenges with accurate and robust wave prediction, this result provides a major argument and incentive for utilizing feedback for WEC control. Implementation of these feedback strategies is readily attainable, while the strategy requiring perfect wave prediction will demand an unknown number of additional years to research and develop, all in the service of a marginal 1% benefit.
Blue noise sampling has proved useful for many graphics applications, but remains underexplored in high-dimensional spaces due to the difficulty of generating distributions and proving properties about them. We present a blue noise sampling method with good quality and performance across different dimensions. The method, spoke-dart sampling, shoots rays from prior samples and selects samples from these rays. It combines the advantages of two major high-dimensional sampling methods: the locality of advancing front with the dimensionality-reduction of hyperplanes, specifically line sampling. We prove that the output sampling is saturated with high probability, with bounds on distances between pairs of samples and between any domain point and its nearest sample. We demonstrate spoke-dart applications for approximate Delaunay graph construction, global optimization, and robotic motion planning. Both the blue-noise quality of the output distribution and the adaptability of the intermediate processes of our method are useful in these applications.
This case study focuses on the Navajo Nation's efforts to provided residential power access through solar photovoltaic systems to some of its approximately 34,000 remote off-grid tribal members. The solution the Nation has adopted in collaboration with Sandia National Laboratories offers insights into how the Navajo Tribal Utility Authority's work could serve as a residential model to meet the needs of the 1.2 billion people globally who are without electrical residential power.
Because hyperbolic space has properties that make it amenable to graph representations, there is significant interest in scalable hyperbolic-space embedding methods. These embeddings enable constant-time approximation of shortest-path distances, and so are significantly more efficient than full shortest-path computations. In this article, we improve on existing landmark-based hyperbolic embedding algorithms for large-scale graphs. Whereas previous methods compute the embedding by using the derivative-free Nelder- Mead simplex optimization method, our approach uses the limited-memoryBFGS(LBFGS) method, which is quasi-Newton optimization, with analytic gradients. Our method is not only significantly faster but also produces higher-quality embeddings. Moreover, we are able to include the hyperbolic curvature as a variable in the optimization. We compare our hyperbolic embedding method implementation in Python (called Hypy) against the best publicly available software, Rigel. Our method is an order of magnitude faster and shows significant improvements in the accuracy of the shortest-path distance calculations. Tests are performed on a variety of real-world networks, and we show the scalability of our method by embedding a graph with 1.8 billion edges and 65 million nodes.
Damage mechanisms in elastomeric syntactic foams filled with glass microballoons (GMB) and resulting effects on the macroscale elastic constants have been investigated. Direct numerical simulations of the material microstructure, composite theory analyses, and uniaxial compression tests across a range of filler volume fractions were conducted. The room temperature and elastic behavior of composites with undamaged, fully debonded, and fully crushed GMBs were investigated for syntactic foams with a polydimethylsiloxane matrix. Good agreement was obtained between numerical studies, composite theory, and experiments. Debonding was studied via finite element models due to the difficulty of isolating this damage mechanism experimentally. The predictions indicate that the bulk modulus is insensitive to the state of debonding at low-GMB-volume fractions but is dramatically reduced if GMBs are crushed. The shear behavior is affected by both debonding and crush damage mechanisms. The acute sensitivity of the bulk modulus to crushed GMBs is further studied in simulations in which only a fraction of GMBs are crushed. We find that the composite bulk modulus drops severely even when just a small fraction of GMBs are crushed. Various material parameters such as GMB wall thickness, volume fraction, and minimum balloon spacing are also investigated, and they show that the results presented here are general and apply to a wide range of microstructure and GMB filler properties.
The development of robust enzymes, in particular cellulases, is a key step in the success of biological routes to ‘second-generation’ biofuels. The typical sources of the enzymes used to degrade biomass include mesophilic and thermophilic organisms. The endoglucanase J30 from glycoside hydrolase family 9 was originally identified through metagenomic analyses of compost-derived bacterial consortia. These studies, which were tailored to favor growth on targeted feedstocks, have already been shown to identify cellulases with considerable thermal tolerance. The amino-acid sequence of J30 shows comparably low identity to those of previously analyzed enzymes. As an enzyme that combines a well measurable activity with a relatively low optimal temperature (50°C) and a modest thermal tolerance, it offers the potential for structural optimization aimed at increased stability. Here, the crystal structure of wild-type J30 is presented along with that of a designed triple-mutant variant with improved characteristics for industrial applications. Through the introduction of a structural Zn2+ site, the thermal tolerance was increased by more than 10°C and was paralleled by an increase in the catalytic optimum temperature by more than 5°C.
Here, we present simulation results quantitatively showing that circularly polarized light persists in transmission through several real-world and model fog environments better than linearly polarized light over broad wavelength ranges from the visible through the infrared. We present results for polydisperse particle distributions from realistic and measured fog environments, comparing the polarization persistence of linear and circular polarization. Using a polarization-tracking Monte Carlo program, we simulate polarized light propagation through four MODTRAN fog models (moderate and heavy radiation fog and moderate and heavy advection fog) and four real-world measured fog particle distributions (Garland measured radiation and advection fogs, Kunkel measured advection fog, and Sandia National Laboratories’ Fog Facility’s fog). Simulations were performed for each fog environment with wavelengths ranging from 0.4 to 12 µm for increasing optical thicknesses of 5, 10, and 15 (increasing fog density or sensing range). Circular polarization persists superiorly for all optical wavelength bands from the visible to the long-wave infrared in nearly all fog types for all optical thicknesses. Throughout our analysis, we show that if even a small percentage of a fog’s particle size distribution is made up of large particles, those particles dominate the scattering process. In nearly all real-world fog situations, these large particles and their dominant scattering characteristics are present. Larger particles are predominantly forward-scattering and contribute to circular polarization’s persistence superiority over broad wavelength ranges and optical thicknesses/range. Circularly polarized light can transmit over 30% more signal in its intended state compared to linearly polarized light through real-world fog environments. This work broadens the understanding of how circular polarization persists through natural fog particle distributions with natural variations in mode particle radius and single or bimodal characteristics.
I have once been asked to read a lecture to a group of 6th graders on Game Theory. After agreeing to it, I realized that explaining the game theory basics to 6th graders my be difficult, given that terms such as Nash equilibrium, minimax, maximin, optimization may not resonate in a 6th grade classroom. Instead I've introduced game theory using the rock-paper-scissors (RPS) game. Turns out kids are excellent gametheoreticians. In RPS, they understood both the benefits of randomizing their own strategy and of predicting their opponent's moves. They offered a number of heuristics both for the prediction and opening move. These heuristics included optimizing against past opponent moves, such as not playing rock if the opponent just played scissors, and playing a specific opening hand, such as "paper". Visualizing the effects of such strategic choices on-the-fly would be interesting and educational. This brief essay attempts demonstrating and visualizing the value of a few different strategic options in RPS. Specifically, we would like to illustrate the following: 1) what is the value of being unpredictable?; and 2) what is the value of being able to predict your opponent? In regard to prediction of human players, the question 2) has been reflected in Jon McLoone's entry in Wolfram Blog from January 20, 2014[1]. McLoone created a predictive algorithm for playing against human opponents, that learns to beat human opponents reliably after approximately 30 - 40 games. I use McLoone's implementation to represent a predictive and random strategies. The rest of this documents 1) investigates performance of this predictive strategy against a random strategy (which is optimal in RPS) and in 2) attempts to turn this predictive power against the predictive strategy by allowing the opponent the full knowledge of the predictor's strategy (but not the choices made using the strategy). This exposes a weakness in predictions made without taking risks into account by illustrating that predictive strategy may make the predictor predictable as well.
Lagrangian shock hydrodynamics simulations will fail to proceed past a certain time if the mesh is approaching tangling. A common solution is an Arbitrary Lagrangian Eulerian (ALE) form, in which the mesh is improved (remeshing) and the solution is remapped onto the improved mesh. The simplest remeshing techniques involve moving only the nodes of the mesh. More advanced remeshing techniques involve altering the mesh connectivity in portions of the domain in order to prevent tangling. Work has been done using Voronoi-based polygonal mesh generators and 2D quad/triangle mesh adaptation. Here, this paper presents the use of tetrahedral mesh adaptation methods as the remeshing step in an otherwise Lagrangian finite element shock hydrodynamics code called Alexa.
The Spectral Neighbor Analysis Potential (SNAP) is a classical interatomic potential that expresses the energy of each atom as a linear function of selected bispectrum components of the neighbor atoms. An extension of the SNAP form is proposed that includes quadratic terms in the bispectrum components. The extension is shown to provide a large increase in accuracy relative to the linear form, while incurring only a modest increase in computational cost. The mathematical structure of the quadratic SNAP form is similar to the embedded atom method (EAM), with the SNAP bispectrum components serving as counterparts to the two-body density functions in EAM. The effectiveness of the new form is demonstrated using an extensive set of training data for tantalum structures. Similar to artificial neural network potentials, the quadratic SNAP form requires substantially more training data in order to prevent overfitting. The quality of this new potential form is measured through a robust cross-validation analysis.
The powder-bed laser additive manufacturing (AM) process is widely used in the fabrication of three-dimensional metallic parts with intricate structures, where kinetically controlled diffusion and microstructure ripening can be hindered by fast melting and rapid solidification. Therefore, the microstructure and physical properties of parts made by this process will be significantly different from their counterparts produced by conventional methods. This work investigates the microstructure evolution for an AM fabricated AlSi10Mg part from its nonequilibrium state toward equilibrium state. Special attention is placed on silicon dissolution, precipitate formation, collapsing of a divorced eutectic cellular structure, and microstructure ripening in the thermal annealing process. These events alter the size, morphology, length scale, and distribution of the beta silicon phase in the primary aluminum, and changes associated with elastic properties and microhardness are reported. The relationship between residual stress and silicon dissolution due to changes in lattice spacing is also investigated and discussed.