We present a novel class of dynamic neural networks that is capable of learning, in an unsupervised manner, attractors that correspond to generalities in a data set. Upon presentation of a test stimulus, the networks follow a sequence of attractors that correspond to subsets of increasing size or generality in the original data set. The networks, inspired by those of the insect antennal lobe, build upon a modified Hopfield network in which nodes are periodically suppressed, global inhibition is gradually strengthened, and the weight of input neurons is gradually decreased relative to recurrent connections. This allows the networks to converge on a Hopfield network's equilibrium within each suppression cycle, and to switch between attractors in between cycles. The fast mutually reinforcing excitatory connections that dominate dynamics within cycles ensures the robust error-tolerant behavior that characterizes Hopfield networks. The cyclic inhibition releases the network from what would otherwise be stable equilibriums or attractors. Increasing global inhibition and decreasing dependence on the input leads successive attractors to differ, and to display increasing generality. As the network is faced with stronger inhibition, only neurons connected with stronger mutually excitatory connections will remain on; successive attractors will consist of sets of neurons that are more strongly correlated, and will tend to select increasingly generic characteristics of the data. Using artificial data, we were able to identify configurations of the network that appeared to produce a sequence of increasingly general results. The next logical steps are to apply these networks to suitable real-world data that can be characterized by a hierarchy of increasing generality and observe the network's performance. This report describes the work, data, and results, the current understanding of the results, and how the work could be continued. The code, data, and preliminary results are included and are available as an archive.
We developed an Augmented Musculature Device (AMD) that assists the movements of its wearer. It has direct application to aiding military and law enforcement personnel, the neurologically impaired, or those requiring any type of cybernetic assistance. The AMD consists of a collection of artificial muscles, each individually actuated, strategically placed along the surface of the human body. The actuators employed by the AMD are known as 'air muscles' and operate pneumatically. They are commercially available from several vendors and are relatively inexpensive. They have a remarkably high force-to-weight ratio--as high as 400:1 (as compared with 16:1 typical of DC motors). They are flexible and elastic, even when powered, making them ideal for interaction with humans.
The challenge of modeling the organization and function of biological membranes on a solid support has received considerable attention in recent years, primarily driven by potential applications in biosensor design. Affinity-based biosensors show great promise for extremely sensitive detection of BW agents and toxins. Receptor molecules have been successfully incorporated into phospholipid bilayers supported on sensing platforms. However, a collective body of data detailing a mechanistic understanding of membrane processes involved in receptor-substrate interactions and the competition between localized perturbations and delocalized responses resulting in reorganization of transmembrane protein structure, has yet to be produced. This report describes a systematic procedure to develop detailed correlation between (recognition-induced) protein restructuring and function of a ligand gated ion channel by combining single molecule fluorescence spectroscopy and single channel current recordings. This document is divided into three sections: (1) reported are the thermodynamics and diffusion properties of gramicidin using single molecule fluorescence imaging and (2) preliminary work on the 5HT{sub 3} serotonin receptor. Thirdly, we describe the design and fabrication of a miniaturized platform using the concepts of these two technologies (spectroscopic and single channel electrochemical techniques) for single molecule analysis, with a longer term goal of using the physical and electronic changes caused by a specific molecular recognition event as a transduction pathway in affinity based biosensors for biotoxin detection.
This white paper represents a summary of work intended to lay the foundation for development of a climatological/agent model of climate-induced conflict. The paper combines several loosely-coupled efforts and is the final report for a four-month late-start Laboratory Directed Research and Development (LDRD) project funded by the Advanced Concepts Group (ACG). The project involved contributions by many participants having diverse areas of expertise, with the common goal of learning how to tie together the physical and human causes and consequences of climate change. We performed a review of relevant literature on conflict arising from environmental scarcity. Rather than simply reviewing the previous work, we actively collected data from the referenced sources, reproduced some of the work, and explored alternative models. We used the unfolding crisis in Darfur (western Sudan) as a case study of conflict related to or triggered by climate change, and as an exercise for developing a preliminary concept map. We also outlined a plan for implementing agents in a climate model and defined a logical progression toward the ultimate goal of running both types of models simultaneously in a two-way feedback mode, where the behavior of agents influences the climate and climate change affects the agents. Finally, we offer some ''lessons learned'' in attempting to keep a diverse and geographically dispersed group working together by using Web-based collaborative tools.
The Common Geometry Module (CGM) is a code library which provides geometry functionality used for mesh generation and other applications. This functionality includes that commonly found in solid modeling engines, like geometry creation, query and modification; CGM also includes capabilities not commonly found in solid modeling engines, like geometry decomposition tools and support for shared material interfaces. CGM is built upon the ACIS solid modeling engine, but also includes geometry capability developed beside and on top of ACIS. CGM can be used as-is to provide geometry functionality for codes needing this capability. However, CGM can also be extended using derived classes in C++, allowing the geometric model to serve as the basis for other applications, for example mesh generation. CGM is supported on Sun Solaris, SGI, HP, IBM, DEC, Linux and Windows NT platforms. CGM also includes support for loading ACIS models on parallel computers, using MPI-based communication. Future plans for CGM are to port it to different solid modeling engines, including Pro/Engineer or SolidWorks. CGM is being released into the public domain under an LGPL license; the ACIS-based engine is available to ACIS licensees on request.
Thermal actuators have proven to be a robust actuation method in surface-micromachined MEMS processes. Their higher output force and lower input voltage make them an attractive alternative to more traditional electrostatic actuation methods. A predictive model of thermal actuator behavior has been developed and validated that can be used as a design tool to customize the performance of an actuator to a specific application. This tool has also been used to better understand thermal actuator reliability by comparing the maximum actuator temperature to the measured lifetime. Modeling thermal actuator behavior requires the use of two sequentially coupled models, the first to predict the temperature increase of the actuator due to the applied current and the second to model the mechanical response of the structure due to the increase in temperature. These two models have been developed using Matlab for the thermal response and ANSYS for the structural response. Both models have been shown to agree well with experimental data. In a parallel effort, the reliability and failure mechanisms of thermal actuators have been studied. Their response to electrical overstress and electrostatic discharge has been measured and a study has been performed to determine actuator lifetime at various temperatures and operating conditions. The results from this study have been used to determine a maximum reliable operating temperature that, when used in conjunction with the predictive model, enables us to design in reliability and customize the performance of an actuator at the design stage.
The solution of the governing steady transport equations for momentum, heat and mass transfer in fluids undergoing non-equilibrium chemical reactions can be extremely challenging. The difficulties arise from both the complexity of the nonlinear solution behavior as well as the nonlinear, coupled, non-symmetric nature of the system of algebraic equations that results from spatial discretization of the PDEs. In this paper, we briefly review progress on developing a stabilized finite element ( FE) capability for numerical solution of these challenging problems. The discussion considers the stabilized FE formulation for the low Mach number Navier-Stokes equations with heat and mass transport with non-equilibrium chemical reactions, and the solution methods necessary for detailed analysis of these complex systems. The solution algorithms include robust nonlinear and linear solution schemes, parameter continuation methods, and linear stability analysis techniques. Our discussion considers computational efficiency, scalability, and some implementation issues of the solution methods. Computational results are presented for a CFD benchmark problem as well as for a number of large-scale, 2D and 3D, engineering transport/reaction applications.
This study investigates algebraic multilevel domain decomposition preconditioners of the Schwarz type for solving linear systems associated with Newton-Krylov methods. The key component of the preconditioner is a coarse approximation based on algebraic multigrid ideas to approximate the global behavior of the linear system. The algebraic multilevel preconditioner is based on an aggressive coarsening graph partitioning of the non-zero block structure of the Jacobian matrix. The scalability of the preconditioner is presented as well as comparisons with a two-level Schwarz preconditioner using a geometric coarse grid operator. These comparisons are obtained on large-scale distributed-memory parallel machines for systems arising from incompressible flow and transport using a stabilized finite element formulation. The results demonstrate the influence of the smoothers and coarse level solvers for a set of 3D example problems. For preconditioners with more than one level, careful attention needs to be given to the balance of robustness and convergence rate for the smoothers and the cost of applying these methods. For properly chosen parameters, the two- and three-level preconditioners are demonstrated to be scalable to 1024 processors.
Least-squares finite-element methods for Darcy flow offer several advantages relative to the mixed-Galerkin method: the avoidance of stability conditions between finite-element spaces, the efficiency of solving symmetric and positive definite systems, and the convenience of using standard, continuous nodal elements for all variables. However, conventional C{sup o} implementations conserve mass only approximately and for this reason they have found limited acceptance in applications where locally conservative velocity fields are of primary interest. In this paper, we show that a properly formulated compatible least-squares method offers the same level of local conservation as a mixed method. The price paid for gaining favourable conservation properties is that one has to give up what is arguably the least important advantage attributed to least-squares finite-element methods: one can no longer use continuous nodal elements for all variables. As an added benefit, compatible least-squares methods inherit the best computational properties of both Galerkin and mixed-Galerkin methods and, in some cases, yield identical results, while offering the advantages of not having to deal with stability conditions and yielding positive definite discrete problems. Numerical results that illustrate our findings are provided.
Acts of terrorism could have a range of broad impacts on an economy, including changes in consumer (or demand) confidence and the ability of productive sectors to respond to changes. As a first step toward a model of terrorism-based impacts, we develop here a model of production and employment that characterizes dynamics in ways useful toward understanding how terrorism-based shocks could propagate through the economy; subsequent models will introduce the role of savings and investment into the economy. We use Aspen, a powerful economic modeling tool developed at Sandia, to demonstrate for validation purposes that a single-firm economy converges to the known monopoly equilibrium price, output, and employment levels, while multiple-firm economies converge toward the competitive equilibria typified by lower prices and higher output and employment. However, we find that competition also leads to churn by consumers seeking lower prices, making it difficult for firms to optimize with respect to wages, prices, and employment levels. Thus, competitive firms generate market ''noise'' in the steady state as they search for prices and employment levels that will maximize profits. In the context of this model, not only could terrorism depress overall consumer confidence and economic activity but terrorist acts could also cause normal short-run dynamics to be misinterpreted by consumers as a faltering economy.
The impact of 3D structure on wire array z-pinch dynamics is a topic of current interest, and has been studied by the controlled seeding of wire perturbations. First, Al wires were etched at Sandia, creating 20% radial perturbations with variable axial wavelength. Observations of magnetic bubble formation in the etched regions during experiments on the MAGPIE accelerator are discussed and compared to 3D MHD modeling. Second, thin NaF coatings of 1 mm axial extent were deposited on Al wires and fielded on the Zebra accelerator. Little or no axial transport of the NaF spectroscopic dopant was observed in spatially resolved K-shell spectra, which places constraints on particle diffusivity in dense z-pinch plasmas. Finally, technology development for seeding perturbations is discussed.
This paper builds upon previous work [Sprigg and Ehlen, 2004] by introducing a bond market into a model of production and employment. The previous paper described an economy in which households choose whether to enter the labor and product markets based on wages and prices. Firms experiment with prices and employment levels to maximize their profits. We developed agent-based simulations using Aspen, a powerful economic modeling tool developed at Sandia, to demonstrate that multiple-firm economies converge toward the competitive equilibria typified by lower prices and higher output and employment, but also suffer from market noise stemming from consumer churn. In this paper we introduce a bond market as a mechanism for household savings. We simulate an economy of continuous overlapping generations in which each household grows older in the course of the simulation and continually revises its target level of savings according to a life-cycle hypothesis. Households can seek employment, earn income, purchase goods, and contribute to savings until they reach the mandatory retirement age; upon retirement households must draw from savings in order to purchase goods. This paper demonstrates the simultaneous convergence of product, labor, and savings markets to their calculated equilibria, and simulates how a disruption to a productive sector will create cascading effects in all markets. Subsequent work will use similar models to simulate how disruptions, such as terrorist attacks, would interplay with consumer confidence to affect financial markets and the broader economy.
In the search for ''good'' parallel programming environments for Sandia's current and future parallel architectures, they revisit a long-standing open question. Can the PRAM parallel algorithms designed by theoretical computer scientists over the last two decades be implemented efficiently? This open question has co-existed with ongoing efforts in the HPC community to develop practical parallel programming models that can simultaneously provide ease of use, expressiveness, performance, and scalability. Unfortunately, no single model has met all these competing requirements. Here they propose a parallel programming environment, PRAM C, to bridge the gap between theory and practice. This is an attempt to provide an affirmative answer to the PRAM question, and to satisfy these competing practical requirements. This environment consists of a new thin runtime layer and an ANSI C extension. The C extension has two control constructs and one additional data type concept, ''shared''. This C extension should enable easy translation from PRAM algorithms to real parallel programs, much like the translation from sequential algorithms to C programs. The thin runtime layer bundles fine-grained communication requests into coarse-grained communication to be served by message-passing. Although the PRAM represents SIMD-style fine-grained parallelism, a stand-alone PRAM C environment can support both fine-grained and coarse-grained parallel programming in either a MIMD or SPMD style, interoperate with existing MPI libraries, and use existing hardware. The PRAM C model can also be integrated easily with existing models. Unlike related efforts proposing innovative hardware with the goal to realize the PRAM, ours can be a pure software solution with the purpose to provide a practical programming environment for existing parallel machines; it also has the potential to perform well on future parallel architectures.
Genetic programming (GP) has proved to be a highly versatile and useful tool for identifying relationships in data for which a more precise theoretical construct is unavailable. In this project, we use a GP search to develop trading strategies for agent based economic models. These strategies use stock prices and technical indicators, such as the moving average convergence/divergence and various exponentially weighted moving averages, to generate buy and sell signals. We analyze the effect of complexity constraints on the strategies as well as the relative performance of various indicators. We also present innovations in the classical genetic programming algorithm that appear to improve convergence for this problem. Technical strategies developed by our GP algorithm can be used to control the behavior of agents in economic simulation packages, such as ASPEN-D, adding variety to the current market fundamentals approach. The exploitation of arbitrage opportunities by technical analysts may help increase the efficiency of the simulated stock market, as it does in the real world. By improving the behavior of simulated stock markets, we can better estimate the effects of shocks to the economy due to terrorism or natural disasters.
In this paper we present an analysis of a new configuration for achieving spin stabilized magnetic levitation. In the classical configuration, the rotor spins about a vertical axis; and the spin stabilizes the lateral instability of the top in the magnetic field. In this new configuration the rotor spins about a horizontal axis; and the spin stabilizes the axial instability of the top in the magnetic field.
ML is a multigrid preconditioning package intended to solve linear systems of equations Ax = b where A is a user supplied n x n sparse matrix, b is a user supplied vector of length n and x is a vector of length n to be computed. ML should be used on large sparse linear systems arising from partial differential equation (PDE) discretizations. While technically any linear system can be considered, ML should be used on linear systems that correspond to things that work well with multigrid methods (e.g. elliptic PDEs). ML can be used as a stand-alone package or to generate preconditioners for a traditional iterative solver package (e.g. Krylov methods). We have supplied support for working with the Aztec 2.1 and AztecOO iterative package [16]. However, other solvers can be used by supplying a few functions. This document describes one specific algebraic multigrid approach: smoothed aggregation. This approach is used within several specialized multigrid methods: one for the eddy current formulation for Maxwell's equations, and a multilevel and domain decomposition method for symmetric and nonsymmetric systems of equations (like elliptic equations, or compressible and incompressible fluid dynamics problems). Other methods exist within ML but are not described in this document. Examples are given illustrating the problem definition and exercising multigrid options.
This paper presents solution verification studies applicable to a class of problems involving wave propagation, frictional contact, geometrical complexity, and localized incompressibility. The studies are in support of a validation exercise of a phenomenological screw failure model. The numerical simulations are performed using a fully explicit transient dynamics finite element code, employing both standard four-node tetrahedral and eight-node mean quadrature hexahedral elements. It is demonstrated that verifying the accuracy of the simulation involves not only consideration of the mesh discretization error, but also the effect of the hourglass control and the contact enforcement. In particular, the proper amount of hourglass control and the behavior of the contact search and enforcement algorithms depend greatly on the mesh resolution. We carry out the solution verification exercise using mesh refinement studies and describe our systematic approach to handling the complicating issues. It is shown that hourglassing and contact must both be carefully monitored as the mesh is refined, and it is often necessary to make adjustments to the hourglass and contact user input parameters to accommodate finer meshes. We introduce in this paper the hourglass energy, which is used as an 'error indicator' for the hourglass control. If the hourglass energy does not tend to zero with mesh refinement, then an hourglass control parameter is changed and the calculation is repeated.
An important challenge encountered during post-processing of finite element analyses is the visualizing of three-dimensional fields of real-valued second-order tensors. Namely, as finite element meshes become more complex and detailed, evaluation and presentation of the principal stresses becomes correspondingly problematic. In this paper, we describe techniques used to visualize simulations of perturbed in-situ stress fields associated with hypothetical salt bodies in the Gulf of Mexico. We present an adaptation of the Mohr diagram, a graphical paper and pencil method used by the material mechanics community for estimating coordinate transformations for stress tensors, as a new tensor glyph for dynamically exploring tensor variables within three-dimensional finite element models. This interactive glyph can be used as either a probe or a filter through brushing and linking.
The purpose of the present work is to increase our understanding of which properties of geomaterials most influence the penetration process with a goal of improving our predictive ability. Two primary approaches were followed: development of a realistic, constitutive model for geomaterials and designing an experimental approach to study penetration from the target's point of view. A realistic constitutive model, with parameters based on measurable properties, can be used for sensitivity analysis to determine the properties that are most important in influencing the penetration process. An immense literature exists that is devoted to the problem of predicting penetration into geomaterials or similar man-made materials such as concrete. Various formulations have been developed that use an analytic or more commonly, numerical, solution for the spherical or cylindrical cavity expansion as a sort of Green's function to establish the forces acting on a penetrator. This approach has had considerable success in modeling the behavior of penetrators, both as to path and depth of penetration. However the approach is not well adapted to the problem of understanding what is happening to the material being penetrated. Without a picture of the stress and strain state imposed on the highly deformed target material, it is not easy to determine what properties of the target are important in influencing the penetration process. We developed an experimental arrangement that allows greater control of the deformation than is possible in actual penetrator tests, yet approximates the deformation processes imposed by a penetrator. Using explosive line charges placed in a central borehole, we loaded cylindrical specimens in a manner equivalent to an increment of penetration, allowing the measurement of the associated strains and accelerations and the retrieval of specimens from the more-or-less intact cylinder. Results show clearly that the deformation zone is highly concentrated near the borehole, with almost no damage occurring beyond 1/2 a borehole diameter. This implies penetration is not strongly influenced by anything but the material within a diameter or so of the penetration. For penetrator tests, target size should not matter strongly once target diameters exceed some small multiple of the penetrator diameter. Penetration into jointed rock should not be much affected unless a discontinuity is within a similar range. Accelerations measured at several points along a radius from the borehole are consistent with highly-concentrated damage and energy absorption; At the borehole wall, accelerations were an order of magnitude higher than at 1/2 a diameter, but at the outer surface, 8 diameters away, accelerations were as expected for propagation through an elastic medium. Accelerations measured at the outer surface of the cylinders increased significantly with cure time for the concrete. As strength increased, less damage was observed near the explosively-driven borehole wall consistent with the lower energy absorption expected and observed for stronger concrete. As it is the energy absorbing properties of a target that ultimately stop a penetrator, we believe this may point the way to a more readily determined equivalent of the S number.
Biosecurity must be implemented without impeding biomedical and bioscience research. Existing security literature and regulatory requirements do not present a comprehensive approach or clear model for biosecurity, nor do they wholly recognize the operational issues within laboratory environments. To help address these issues, the concept of Biosecurity Levels should be developed. Biosecurity Levels would have increasing levels of security protections depending on the attractiveness of the pathogens to adversaries. Pathogens and toxins would be placed in a Biosecurity Level based on their security risk. Specifically, the security risk would be a function of an agent's weaponization potential and consequences of use. To demonstrate the concept, examples of security risk assessments for several human, animal, and plant pathogens will be presented. Higher security than that currently mandated by federal regulations would be applied for those very few agents that represent true weapons threats and lower levels for the remainder.
By means of coupled-cluster theory, molecular properties can be computed with an accuracy often exceeding that of experiment. The high-degree polynomial scaling of the coupled-cluster method, however, remains a major obstacle in the accurate theoretical treatment of mainstream chemical problems, despite tremendous progress in computer architectures. Although it has long been recognized that this super-linear scaling is non-physical, the development of efficient reduced-scaling algorithms for massively parallel computers has not been realized. We here present a locally correlated, reduced-scaling, massively parallel coupled-cluster algorithm. A sparse data representation for handling distributed, sparse multidimensional arrays has been implemented along with a set of generalized contraction routines capable of handling such arrays. The parallel implementation entails a coarse-grained parallelization, reducing interprocessor communication and distributing the largest data arrays but replicating as many arrays as possible without introducing memory bottlenecks. The performance of the algorithm is illustrated by several series of runs for glycine chains using a Linux cluster with an InfiniBand interconnect.
We explore stability of Random Boolean Networks as a model of biological interaction networks. We introduce surface-to-volume ratio as a measure of stability of the network. Surface is defined as the set of states within a basin of attraction that maps outside the basin by a bit-flip operation. Volume is defined as the total number of states in the basin. We report development of an object-oriented Boolean network analysis code (Attract) to investigate the structure of stable vs. unstable networks. We find two distinct types of stable networks. The first type is the nearly trivial stable network with a few basins of attraction. The second type contains many basins. We conclude that second type stable networks are extremely rare.
The efficiency of neuronal encoding in sensory and motor systems has been proposed as a first principle governing response properties within the central nervous system. We present a continuation of a theoretical study presented by Zhang and Sejnowski, where the influence of neuronal tuning properties on encoding accuracy is analyzed using information theory. When a finite stimulus space is considered, we show that the encoding accuracy improves with narrow tuning for one- and two-dimensional stimuli. For three dimensions and higher, there is an optimal tuning width.
Human behavior is a function of an iterative interaction between the stimulus environment and past experience. It is not simply a matter of the current stimulus environment activating the appropriate experience or rule from memory (e.g., if it is dark and I hear a strange noise outside, then I turn on the outside lights and investigate). Rather, it is a dynamic process that takes into account not only things one would generally do in a given situation, but things that have recently become known (e.g., there have recently been coyotes seen in the area and one is known to be rabid), as well as other immediate environmental characteristics (e.g., it is snowing outside, I know my dog is outside, I know the police are already outside, etc.). All of these factors combine to inform me of the most appropriate behavior for the situation. If it were the case that humans had a rule for every possible contingency, the amount of storage that would be required to enable us to fluidly deal with most situations we encounter would rapidly become biologically untenable. We can all deal with contingencies like the one above with fairly little effort, but if it isn't based on rules, what is it based on? The assertion of the Cognitive Systems program at Sandia for the past 5 years is that at the heart of this ability to effectively navigate the world is an ability to discriminate between different contexts (i.e., Dynamic Context Discrimination, or DCD). While this assertion in and of itself might not seem earthshaking, it is compelling that this ability and its components show up in a wide variety of paradigms across different subdisciplines in psychology. We begin by outlining, at a high functional level, the basic ideas of DCD. We then provide evidence from several different literatures and paradigms that support our assertion that DCD is a core aspect of cognitive functioning. Finally, we discuss DCD and the computational model that we have developed as an instantiation of DCD in more detail. Before commencing with our overview of DCD, we should note that DCD is not necessarily a theory in the classic sense. Rather, it is a description of cognitive functioning that seeks to unify highly similar findings across a wide variety of literatures. Further, we believe that such convergence warrants a central place in efforts to computationally emulate human cognition. That is, DCD is a general principle of cognition. It is also important to note that while we are drawing parallels across many literatures, these are functional parallels and are not necessarily structural ones. That is, we are not saying that the same neural pathways are involved in these phenomena. We are only saying that the different neural pathways that are responsible for the appearance of these various phenomena follow the same functional rules - the mechanisms are the same even if the physical parts are distinct. Furthermore, DCD is not a causal mechanism - it is an emergent property of the way the brain is constructed. DCD is the result of neurophysiology (cf. John, 2002, 2003). Finally, it is important to note that we are not proposing a generic learning mechanism such that one biological algorithm can account for all situation interpretation. Rather, we are pointing out that there are strikingly similar empirical results across a wide variety of disciplines that can be understood, in part, by similar cognitive processes. It is entirely possible, even assumed in some cases (i.e., primary language acquisition) that these more generic cognitive processes are complemented and constrained by various limits which may or may not be biological in nature (cf. Bates & Elman, 1996; Elman, in press).
As part of DARPA Information Processing Technology Office (IPTO) Software for Distributed Robotics (SDR) Program, Sandia National Laboratories has developed analysis and control software for coordinating tens to thousands of autonomous cooperative robotic agents (primarily unmanned ground vehicles) performing military operations such as reconnaissance, surveillance and target acquisition; countermine and explosive ordnance disposal; force protection and physical security; and logistics support. Due to the nature of these applications, the control techniques must be distributed, and they must not rely on high bandwidth communication between agents. At the same time, a single soldier must easily direct these large-scale systems. Finally, the control techniques must be provably convergent so as not to cause undo harm to civilians. In this project, provably convergent, moderate communication bandwidth, distributed control algorithms have been developed that can be regulated by a single soldier. We have simulated in great detail the control of low numbers of vehicles (up to 20) navigating throughout a building, and we have simulated in lesser detail the control of larger numbers of vehicles (up to 1000) trying to locate several targets in a large outdoor facility. Finally, we have experimentally validated the resulting control algorithms on smaller numbers of autonomous vehicles.
We present a model for optimizing the placement of sensors in municipal water networks to detect maliciously injected contaminants. An optimal sensor configuration minimizes the expected fraction of the population at risk. We formulate this problem as a mixed-integer program, which can be solved with generally available solvers. We find optimal sensor placements for three test networks with synthetic risk and population data. Our experiments illustrate that this formulation can be solved relatively quickly and that the predicted sensor configuration is relatively insensitive to uncertainties in the data used for prediction.
As Sandia looks toward petaflops computing and other advanced architectures, it is necessary to provide a programming environment that can exploit this additional computing power while supporting reasonable development time for applications. Thus, they evaluate the Partitioned Global Address Space (PGAS) programming model as implemented in Unified Parallel C (UPC) for its applicability. They report on their experiences in implementing sorting and minimum spanning tree algorithms on a test system, a Cray T3e, with UPC support. They describe several macros that could serve as language extensions and several building-block operations that could serve as a foundation for a PGAS programming library. They analyze the limitations of the UPC implementation available on the test system, and suggest improvements necessary before UPC can be used in a production environment.
The mathematical and physical foundations and domain of applicability of Sandia's GeoModel are presented along with descriptions of the source code and user instructions. The model is designed to be used in conventional finite element architectures, and (to date) it has been installed in five host codes without requiring customizing the model subroutines for any of these different installations. Although developed for application to geological materials, the GeoModel actually applies to a much broader class of materials, including rock-like engineered materials (such as concretes and ceramics) and even to metals when simplified parameters are used. Nonlinear elasticity is supported through an empirically fitted function that has been found to be well-suited to a wide variety of materials. Fundamentally, the GeoModel is a generalized plasticity model. As such, it includes a yield surface, but the term 'yield' is generalized to include any form of inelastic material response including microcrack growth and pore collapse. The geomodel supports deformation-induced anisotropy in a limited capacity through kinematic hardening (in which the initially isotropic yield surface is permitted to translate in deviatoric stress space to model Bauschinger effects). Aside from kinematic hardening, however, the governing equations are otherwise isotropic. The GeoModel is a genuine unification and generalization of simpler models. The GeoModel can employ up to 40 material input and control parameters in the rare case when all features are used. Simpler idealizations (such as linear elasticity, or Von Mises yield, or Mohr-Coulomb failure) can be replicated by simply using fewer parameters. For high-strain-rate applications, the GeoModel supports rate dependence through an overstress model.
One problem facing today's nuclear power industry is flow-accelerated corrosion and erosion in pipe elbows. The Korean Atomic Energy Research Institute (KAERI) is performing experiments in their Flow-Accelerated Corrosion (FAC) test loop to better characterize these phenomena, and develop advanced sensor technologies for the condition monitoring of critical elbows on a continuous basis. In parallel with these experiments, Sandia National Laboratories is performing Computational Fluid Dynamic (CFD) simulations of the flow in one elbow of the FAC test loop. The simulations are being performed using the FLUENT commercial software developed and marketed by Fluent, Inc. The model geometry and mesh were created using the GAMBIT software, also from Fluent, Inc. This report documents the results of the simulations that have been made to date; baseline results employing the RNG k-e turbulence model are presented. The predicted value for the diametrical pressure coefficient is in reasonably good agreement with published correlations. Plots of the velocities, pressure field, wall shear stress, and turbulent kinetic energy adjacent to the wall are shown within the elbow section. Somewhat to our surprise, these indicate that the maximum values of both wall shear stress and turbulent kinetic energy occur near the elbow entrance, on the inner radius of the bend. Additional simulations were performed for the same conditions, but with the RNG k-e model replaced by either the standard k-{var_epsilon}, or the realizable k-{var_epsilon} turbulence model. The predictions using the standard k-{var_epsilon} model are quite similar to those obtained in the baseline simulation. However, with the realizable k-{var_epsilon} model, more significant differences are evident. The maximums in both wall shear stress and turbulent kinetic energy now appear on the outer radius, near the elbow exit, and are {approx}11% and 14% greater, respectively, than those predicted in the baseline calculation; secondary maxima in both quantities still occur near the elbow entrance on the inner radius. Which set of results better reflects reality must await experimental corroboration. Additional calculations demonstrate that whether or not FLUENT's radial equilibrium pressure distribution option is used in the PRESSURE OUTLET boundary condition has no significant impact on the flowfield near the elbow. Simulations performed with and without the chemical sensor and associated support bracket that were present in the experiments demonstrate that the latter have a negligible influence on the flow in the vicinity of the elbow. The fact that the maxima in wall shear stress and turbulent kinetic energy occur on the inner radius is therefore not an artifact of having introduced the sensor into the flow.
The peridynamic theory of continuum mechanics allows damage, fracture, and long-range forces to be treated as natural components of the deformation of a material. In this paper, the peridynamic approach is applied to small thickness two- and one-dimensional structures. For membranes, a constitutive model is described appropriate for rubbery sheets that can form cracks. This model is used to perform numerical simulations of the stretching and dynamic tearing of membranes. A similar approach is applied to one-dimensional string like structures that undergrow stretching, bending, and failure. Long-range forces similar to van der Waals interactions at the nanoscale influence the equilibrium configurations of these structures, how they deform, and possibly self-assembly.
We are extending the existing features of Aspen, a powerful economic modeling tool, and introducing new features to simulate the role of confidence in economic activity. The new model is built from a collection of autonomous agents that represent households, firms, and other relevant entities like financial exchanges and governmental authorities. We simultaneously model several interrelated markets, including those for labor, products, stocks, and bonds. We also model economic tradeoffs, such as decisions of households and firms regarding spending, savings, and investment. In this paper, we review some of the basic principles and model components and describe our approach and development strategy for emulating consumer, investor, and business confidence. The model of confidence is explored within the context of economic disruptions, such as those resulting from disasters or terrorist events.
This document is a reference guide for the UNIX Library/Standalone version of the Latin Hypercube Sampling Software. This software has been developed to generate Latin hypercube multivariate samples. This version runs on Linux or UNIX platforms. This manual covers the use of the LHS code in a UNIX environment, run either as a standalone program or as a callable library. The underlying code in the UNIX Library/Standalone version of LHS is almost identical to the updated Windows version of LHS released in 1998 (SAND98-0210). However, some modifications were made to customize it for a UNIX environment and as a library that is called from the DAKOTA environment. This manual covers the use of the LHS code as a library and in the standalone mode under UNIX.
A study was undertaken to validate the 'capability' computing needs of DOE's Office of Science. More than seventy members of the community provided information about algorithmic scaling laws, so that the impact of having access to Petascale capability computers could be assessed. We have concluded that the Office of Science community has described credible needs for Petascale capability computing.
Two heuristic strategies intended to enhance the performance of the generalized global basis (GGB) method [H. Waisman, J. Fish, R.S. Tuminaro, J. Shadid, The Generalized Global Basis (GGB) method, International Journal for Numerical Methods in Engineering 61(8), 1243-1269] applied to nonlinear systems are presented. The standard GGB accelerates a multigrid scheme by an additional coarse grid correction that filters out slowly converging modes. This correction requires a potentially costly eigen calculation. This paper considers reusing previously computed eigenspace information. The GGB? scheme enriches the prolongation operator with new eigenvectors while the modified method (MGGB) selectively reuses the same prolongation. Both methods use the criteria of principal angles between subspaces spanned between the previous and current prolongation operators. Numerical examples clearly indicate significant time savings in particular for the MGGB scheme.
We consider linear systems arising from the use of the finite element method for solving scalar linear elliptic problems. Our main result is that these linear systems, which are symmetric and positive semidefinite, are well approximated by symmetric diagonally dominant matrices. Our framework for defining matrix approximation is support theory. Significant graph theoretic work has already been developed in the support framework for preconditioners in the diagonally dominant case, and in particular it is known that such systems can be solved with iterative methods in nearly linear time. Thus, our approximation result implies that these graph theoretic techniques can also solve a class of finite element problems in nearly linear time. We show that the support number bounds, which control the number of iterations in the preconditioned iterative solver, depend on mesh quality measures but not on the problem size or shape of the domain.
Analytic solutions are useful for code verification. Structural vibration codes approximate solutions to the eigenvalue problem for the linear elasticity equations (Navier's equations). Unfortunately the verification method of 'manufactured solutions' does not apply to vibration problems. Verification books (for example [2]) tabulate a few of the lowest modes, but are not useful for computations of large numbers of modes. A closed form solution is presented here for all the eigenvalues and eigenfunctions for a cuboid solid with isotropic material properties. The boundary conditions correspond physically to a greased wall.
It seems well understood that supercomputer simulation is an enabler for scientific discoveries, weapons, and other activities of value to society. It also seems widely believed that Moore's Law will make progressively more powerful supercomputers over time and thus enable more of these contributions. This paper seeks to add detail to these arguments, revealing them to be generally correct but not a smooth and effortless progression. This paper will review some key problems that can be solved with supercomputer simulation, showing that more powerful supercomputers will be useful up to a very high yet finite limit of around 1021 FLOPS (1 Zettaflops) . The review will also show the basic nature of these extreme problems. This paper will review work by others showing that the theoretical maximum supercomputer power is very high indeed, but will explain how a straightforward extrapolation of Moore's Law will lead to technological maturity in a few decades. The power of a supercomputer at the maturity of Moore's Law will be very high by today's standards at 1016-1019 FLOPS (100 Petaflops to 10 Exaflops), depending on architecture, but distinctly below the level required for the most ambitious applications. Having established that Moore's Law will not be that last word in supercomputing, this paper will explore the nearer term issue of what a supercomputer will look like at maturity of Moore's Law. Our approach will quantify the maximum performance as permitted by the laws of physics for extension of current technology and then find a design that approaches this limit closely. We study a 'multi-architecture' for supercomputers that combines a microprocessor with other 'advanced' concepts and find it can reach the limits as well. This approach should be quite viable in the future because the microprocessor would provide compatibility with existing codes and programming styles while the 'advanced' features would provide a boost to the limits of performance.
We compare inexact Newton and coordinate descent methods for optimizing the quality of a mesh by repositioning the vertices, where quality is measured by the harmonic mean of the mean-ratio metric. The effects of problem size, element size heterogeneity, and various vertex displacement schemes on the performance of these algorithms are assessed for a series of tetrahedral meshes.
There is currently a large research and development effort within the high-performance computing community on advanced parallel programming models. This research can potentially have an impact on parallel applications, system software, and computing architectures in the next several years. Given Sandia's expertise and unique perspective in these areas, particularly on very large-scale systems, there are many areas in which Sandia can contribute to this effort. This technical report provides a survey of past and present parallel programming model research projects and provides a detailed description of the Partitioned Global Address Space (PGAS) programming model. The PGAS model may offer several improvements over the traditional distributed memory message passing model, which is the dominant model currently being used at Sandia. This technical report discusses these potential benefits and outlines specific areas where Sandia's expertise could contribute to current research activities. In particular, we describe several projects in the areas of high-performance networking, operating systems and parallel runtime systems, compilers, application development, and performance evaluation.
We give processor-allocation algorithms for grid architectures, where the objective is to select processors from a set of available processors to minimize the average number of communication hops. The associated clustering problem is as follows: Given n points in R{sup d}, find a size-k subset with minimum average pairwise L{sub 1} distance.We present a natural approximation algorithm and show that it is a 7/4-approximation for 2D grids. In d dimensions, the approximation guarantee is 2 - 1/2d, which is tight. We also give a polynomial-time approximation scheme (PTAS) for constant dimension d and report on experimental results.
The Trilinos{trademark} Project is an effort to facilitate the design, development, integration and ongoing support of mathematical software libraries. AztecOO{trademark} is a package within Trilinos that enables the use of the Aztec solver library [19] with Epetra{trademark} [13] objects. AztecOO provides access to Aztec preconditioners and solvers by implementing the Aztec 'matrix-free' interface using Epetra. While Aztec is written in C and procedure-oriented, AztecOO is written in C++ and is object-oriented. In addition to providing access to Aztec capabilities, AztecOO also provides some signficant new functionality. In particular it provides an extensible status testing capability that allows expression of sophisticated stopping criteria as is needed in production use of iterative solvers. AztecOO also provides mechanisms for using Ifpack [2], ML [20] and AztecOO itself as preconditioners.
Sundance is a system of software components that allows construction of an entire parallel simulator and its derivatives using a high-level symbolic language. With this high-level problem description, it is possible to specify a weak formulation of a PDE and its discretization method in a small amount of user-level code; furthermore, because derivatives are easily available, a simulation in Sundance is immediately suitable for accelerated PDE-constrained optimization algorithms. This paper is a tutorial for setting up and solving linear and nonlinear PDEs in Sundance. With several simple examples, we show how to set up mesh objects, geometric regions for BC application, the weak form of the PDE, and boundary conditions. Each example then illustrates use of an appropriate solver and solution visualization.
This document is intended to contain a detailed description of the mathematical formulation of Xyce, a massively parallel SPICE-style circuit simulator developed at Sandia National Laboratories. The target audience of this document are people in the role of 'service provider'. An example of such a person would be a linear solver expert who is spending a small fraction of his time developing solver algorithms for Xyce. Such a person probably is not an expert in circuit simulation, and would benefit from an description of the equations solved by Xyce. In this document, modified nodal analysis (MNA) is described in detail, with a number of examples. Issues that are unique to circuit simulation, such as voltage limiting, are also described in detail.
This SAND report is the final report on Sandia's Grand Challenge LDRD Project 27328, 'A Revolution in Lighting -- Building the Science and Technology Base for Ultra-Efficient Solid-state Lighting.' This project, which for brevity we refer to as the SSL GCLDRD, is considered one of Sandia's most successful GCLDRDs. As a result, this report reviews not only technical highlights, but also the genesis of the idea for Solid-state Lighting (SSL), the initiation of the SSL GCLDRD, and the goals, scope, success metrics, and evolution of the SSL GCLDRD over the course of its life. One way in which the SSL GCLDRD was different from other GCLDRDs was that it coincided with a larger effort by the SSL community - primarily industrial companies investing in SSL, but also universities, trade organizations, and other Department of Energy (DOE) national laboratories - to support a national initiative in SSL R&D. Sandia was a major player in publicizing the tremendous energy savings potential of SSL, and in helping to develop, unify and support community consensus for such an initiative. Hence, our activities in this area, discussed in Chapter 6, were substantial: white papers; SSL technology workshops and roadmaps; support for the Optoelectronics Industry Development Association (OIDA), DOE and Senator Bingaman's office; extensive public relations and media activities; and a worldwide SSL community website. Many science and technology advances and breakthroughs were also enabled under this GCLDRD, resulting in: 55 publications; 124 presentations; 10 book chapters and reports; 5 U.S. patent applications including 1 already issued; and 14 patent disclosures not yet applied for. Twenty-six invited talks were given, at prestigious venues such as the American Physical Society Meeting, the Materials Research Society Meeting, the AVS International Symposium, and the Electrochemical Society Meeting. This report contains a summary of these science and technology advances and breakthroughs, with Chapters 1-5 devoted to the five technical task areas: 1 Fundamental Materials Physics; 2 111-Nitride Growth Chemistry and Substrate Physics; 3 111-Nitride MOCVD Reactor Design and In-Situ Monitoring; 4 Advanced Light-Emitting Devices; and 5 Phosphors and Encapsulants. Chapter 7 (Appendix A) contains a listing of publications, presentations, and patents. Finally, the SSL GCLDRD resulted in numerous actual and pending follow-on programs for Sandia, including multiple grants from DOE and the Defense Advanced Research Projects Agency (DARPA), and Cooperative Research and Development Agreements (CRADAs) with SSL companies. Many of these follow-on programs arose out of contacts developed through our External Advisory Committee (EAC). In h s and other ways, the EAC played a very important role. Chapter 8 (Appendix B) contains the full (unedited) text of the EAC reviews that were held periodically during the course of the project.
In theory, it should be possible to infer realistic genetic networks from time series microarray data. In practice, however, network discovery has proved problematic. The three major challenges are: (1) inferring the network; (2) estimating the stability of the inferred network; and (3) making the network visually accessible to the user. Here we describe a method, tested on publicly available time series microarray data, which addresses these concerns. The inference of genetic networks from genome-wide experimental data is an important biological problem which has received much attention. Approaches to this problem have typically included application of clustering algorithms [6]; the use of Boolean networks [12, 1, 10]; the use of Bayesian networks [8, 11]; and the use of continuous models [21, 14, 19]. Overviews of the problem and general approaches to network inference can be found in [4, 3]. Our approach to network inference is similar to earlier methods in that we use both clustering and Boolean network inference. However, we have attempted to extend the process to better serve the end-user, the biologist. In particular, we have incorporated a system to assess the reliability of our network, and we have developed tools which allow interactive visualization of the proposed network.
This manual describes the use of the Xyce Parallel Electronic Simulator. Xyce has been designed as a SPICE-compatible, high-performance analog circuit simulator capable of simulating electrical circuits at a variety of abstraction levels. Primarily, Xyce has been written to support the simulation needs of the Sandia National Laboratories electrical designers. This development has focused on improving capability the current state-of-the-art in the following areas: {sm_bullet} Capability to solve extremely large circuit problems by supporting large-scale parallel computing platforms (up to thousands of processors). Note that this includes support for most popular parallel and serial computers. {sm_bullet} Improved performance for all numerical kernels (e.g., time integrator, nonlinear and linear solvers) through state-of-the-art algorithms and novel techniques. {sm_bullet} Device models which are specifically tailored to meet Sandia's needs, including many radiation-aware devices. {sm_bullet} A client-server or multi-tiered operating model wherein the numerical kernel can operate independently of the graphical user interface (GUI). {sm_bullet} Object-oriented code design and implementation using modern coding practices that ensure that the Xyce Parallel Electronic Simulator will be maintainable and extensible far into the future. Xyce is a parallel code in the most general sense of the phrase - a message passing of computing platforms. These include serial, shared-memory and distributed-memory parallel implementation - which allows it to run efficiently on the widest possible number parallel as well as heterogeneous platforms. Careful attention has been paid to the specific nature of circuit-simulation problems to ensure that optimal parallel efficiency is achieved as the number of processors grows. One feature required by designers is the ability to add device models, many specific to the needs of Sandia, to the code. To this end, the device package in the Xyce These input formats include standard analytical models, behavioral models look-up Parallel Electronic Simulator is designed to support a variety of device model inputs. tables, and mesh-level PDE device models. Combined with this flexible interface is an architectural design that greatly simplifies the addition of circuit models. One of the most important feature of Xyce is in providing a platform for computational research and development aimed specifically at the needs of the Laboratory. With Xyce, Sandia now has an 'in-house' capability with which both new electrical (e.g., device model development) and algorithmic (e.g., faster time-integration methods) research and development can be performed. Ultimately, these capabilities are migrated to end users.
This document is a reference guide to the Xyce Parallel Electronic Simulator, and is a companion document to the Xyce Users' Guide. The focus of this document is (to the extent possible) exhaustively list device parameters, solver options, parser options, and other usage details of Xyce. This document is not intended to be a tutorial. Users who are new to circuit simulation are better served by the Xyce Users' Guide.
The surface micromachining processes used to manufacture MEMS devices and integrated circuits transpire at such small length scales and are sufficiently complex that a theoretical analysis of them is particularly inviting. Under development at Sandia National Laboratories (SNL) is Chemically Induced Surface Evolution with Level Sets (ChISELS), a level-set based feature-scale modeler of such processes. The theoretical models used, a description of the software and some example results are presented here. The focus to date has been of low-pressure and plasma enhanced chemical vapor deposition (low-pressure chemical vapor deposition, LPCVD and PECVD) processes. Both are employed in SNLs SUMMiT V technology. Examples of step coverage of SiO{sub 2} into a trench by each of the LPCVD and PECVD process are presented.
The diagonal-mass-matrix spectral element method has proven very successful in geophysical applications dominated by wave propagation. For these problems, the ability to run fully explicit time stepping schemes at relatively high order makes the method more competitive then finite element methods which require the inversion of a mass matrix. The method relies on Gauss-Lobatto points to be successful, since the grid points used are required to produce well conditioned polynomial interpolants, and be high quality 'Gauss-like' quadrature points that exactly integrate a space of polynomials of higher dimension than the number of quadrature points. These two requirements have traditionally limited the diagonal-mass-matrix spectral element method to use square or quadrilateral elements, where tensor products of Gauss-Lobatto points can be used. In non-tensor product domains such as the triangle, both optimal interpolation points and Gauss-like quadrature points are difficult to construct and there are few analytic results. To extend the diagonal-mass-matrix spectral element method to (for example) triangular elements, one must find appropriate points numerically. One successful approach has been to perform numerical searches for high quality interpolation points, as measured by the Lebesgue constant (Such as minimum energy electrostatic points and Fekete points). However, these points typically do not have any Gauss-like quadrature properties. In this work, we describe a new numerical method to look for Gauss-like quadrature points in the triangle, based on a previous algorithm for computing Fekete points. Performing a brute force search for such points is extremely difficult. A common strategy to increase the numerical efficiency of these searches is to reduce the number of unknowns by imposing symmetry conditions on the quadrature points. Motivated by spectral element methods, we propose a different way to reduce the number of unknowns: We look for quadrature formula that have the same number of points as the number of basis functions used in the spectral element method's transform algorithm. This is an important requirement if they are to be used in a diagonal-mass-matrix spectral element method. This restriction allows for the construction of cardinal functions (Lagrange interpolating polynomials). The ability to construct cardinal functions leads to a remarkable expression relating the variation in the quadrature weights to the variation in the quadrature points. This relation in turn leads to an analytical expression for the gradient of the quadrature error with respect to the quadrature points. Thus the quadrature weights have been completely removed from the optimization problem, and we can implement an exact steepest descent algorithm for driving the quadrature error to zero. Results from the algorithm will be presented for the triangle and the sphere.
Coupling between transient simulation codes of different fidelity can often be performed at the nonlinear solver level, if the time scales of the two codes are similar. A good example is electrical mixed-mode simulation, in which an analog circuit simulator is coupled to a PDE-based semiconductor device simulator. Semiconductor simulation problems, such as single-event upset (SEU), often require the fidelity of a mesh-based device simulator but are only meaningful when dynamically coupled with an external circuit. For such problems a mixed-level simulator is desirable, but the two types of simulation generally have different (somewhat conflicting) numerical requirements. To address these considerations, we have investigated variations of the two-level Newton algorithm, which preserves tight coupling between the circuit and the PDE device, while optimizing the numerics for both. The research was done within Xyce, a massively parallel electronic simulator under development at Sandia National Laboratories.
Dynamic memory management in C++ is one of the most common areas of difficulty and errors for amateur and expert C++ developers alike. The improper use of operator new and operator delete is arguably the most common cause of incorrect program behavior and segmentation faults in C++ programs. Here we introduce a templated concrete C++ class Teuchos::RefCountPtr<>, which is part of the Trilinos tools package Teuchos, that combines the concepts of smart pointers and reference counting to build a low-overhead but effective tool for simplifying dynamic memory management in C++. We discuss why the use of raw pointers for memory management, managed through explicit calls to operator new and operator delete, is so difficult to accomplish without making mistakes and how programs that use raw pointers for memory management can easily be modified to use RefCountPtr<>. In addition, explicit calls to operator delete is fragile and results in memory leaks in the presents of C++ exceptions. In its most basic usage, RefCountPtr<> automatically determines when operator delete should be called to free an object allocated with operator new and is not fragile in the presents of exceptions. The class also supports more sophisticated use cases as well. This document describes just the most basic usage of RefCountPtr<> to allow developers to get started using it right away. However, more detailed information on the design and advanced features of RefCountPtr<> is provided by the companion document 'Teuchos::RefCountPtr : The Trilinos Smart Reference-Counted Pointer Class for (Almost) Automatic Dynamic Memory Management in C++'.
The threat from biological weapons is assessed through both a comparative historical analysis of the patterns of biological weapons use and an assessment of the technological hurdles to proliferation and use that must be overcome. The history of biological weapons is studied to learn how agents have been acquired and what types of states and substate actors have used agents. Substate actors have generally been more willing than states to use pathogens and toxins and they have focused on those agents that are more readily available. There has been an increasing trend of bioterrorism incidents over the past century, but states and substate actors have struggled with one or more of the necessary technological steps. These steps include acquisition of a suitable agent, production of an appropriate quantity and form, and effective deployment. The technological hurdles associated with the steps present a real barrier to producing a high consequence event. However, the ever increasing technological sophistication of society continually lowers the barriers, resulting in a low but increasing probability of a high consequence bioterrorism event.
This document describes the main functionalities of the Amesos package, version 1.0. Amesos, available as part of Trilinos 4.0, provides an object-oriented interface to several serial and parallel sparse direct solvers libraries, for the solution of the linear systems of equations A X = B where A is a real sparse, distributed matrix, defined as an EpetraRowMatrix object, and X and B are defined as EpetraMultiVector objects. Amesos provides a common look-and-feel to several direct solvers, insulating the user from each package's details, such as matrix and vector formats, and data distribution.
The Trilinos Project is an effort to facilitate the design, development, integration and ongoing support of mathematical software libraries. The goal of the Trilinos Project is to develop parallel solver algorithms and libraries within an object-oriented software framework for the solution of large-scale, complex multiphysics engineering and scientific applications. The emphasis is on developing robust, scalable algorithms in a software framework, using abstract interfaces for flexible interoperability of components while providing a full-featured set of concrete classes that implement all the abstract interfaces. This document introduces the use of Trilinos, version 4.0. The presented material includes, among others, the definition of distributed matrices and vectors with Epetra, the iterative solution of linear systems with AztecOO, incomplete factorizations with IF-PACK, multilevel and domain decomposition preconditioners with ML, direct solution of linear system with Amesos, and iterative solution of nonlinear systems with NOX. The tutorial is a self-contained introduction, intended to help computational scientists effectively apply the appropriate Trilinos package to their applications. Basic examples are presented that are fit to be imitated. This document is a companion to the Trilinos User's Guide [20] and Trilinos Development Guides [21,22]. Please note that the documentation included in each of the Trilinos' packages is of fundamental importance.
ML is a multigrid preconditioning package intended to solve linear systems of equations Az = b where A is a user supplied n x n sparse matrix, b is a user supplied vector of length n and x is a vector of length n to be computed. ML should be used on large sparse linear systems arising from partial differential equation (PDE) discretizations. While technically any linear system can be considered, ML should be used on linear systems that correspond to things that work well with multigrid methods (e.g. elliptic PDEs). ML can be used as a stand-alone package or to generate preconditioners for a traditional iterative solver package (e.g. Krylov methods). We have supplied support for working with the AZTEC 2.1 and AZTECOO iterative package [15]. However, other solvers can be used by supplying a few functions. This document describes one specific algebraic multigrid approach: smoothed aggregation. This approach is used within several specialized multigrid methods: one for the eddy current formulation for Maxwell's equations, and a multilevel and domain decomposition method for symmetric and non-symmetric systems of equations (like elliptic equations, or compressible and incompressible fluid dynamics problems). Other methods exist within ML but are not described in this document. Examples are given illustrating the problem definition and exercising multigrid options.
Existing approaches in multiscale science and engineering have evolved from a range of ideas and solutions that are reflective of their original problem domains. As a result, research in multiscale science has followed widely diverse and disjoint paths, which presents a barrier to cross pollination of ideas and application of methods outside their application domains. The status of the research environment calls for an abstract mathematical framework that can provide a common language to formulate and analyze multiscale problems across a range of scientific and engineering disciplines. In such a framework, critical common issues arising in multiscale problems can be identified, explored and characterized in an abstract setting. This type of overarching approach would allow categorization and clarification of existing models and approximations in a landscape of seemingly disjoint, mutually exclusive and ad hoc methods. More importantly, such an approach can provide context for both the development of new techniques and their critical examination. As with any new mathematical framework, it is necessary to demonstrate its viability on problems of practical importance. At Sandia, lab-centric, prototype application problems in fluid mechanics, reacting flows, magnetohydrodynamics (MHD), shock hydrodynamics and materials science span an important subset of DOE Office of Science applications and form an ideal proving ground for new approaches in multiscale science.
ML development was started in 1997 by Ray Tuminaro and Charles Tong. Currently, there are several full- and part-time developers. The kernel of ML is written in ANSI C, and there is a rich C++ interface for Trilinos users and developers. ML can be customized to run geometric and algebraic multigrid; it can solve a scalar or a vector equation (with constant number of equations per grid node), and it can solve a form of Maxwell's equations. For a general introduction to ML and its applications, we refer to the Users Guide [SHT04], and to the ML web site, http://software.sandia.gov/ml.
Currently, the Egyptian Atomic Energy Authority is designing a shallow-land disposal facility for low-level radioactive waste. To insure containment and prevent migration of radionuclides from the site, the use of a reactive backfill material is being considered. One material under consideration is hydroxyapatite, Ca{sub 10}(PO{sub 4}){sub 6}(OH){sub 2}, which has a high affinity for the sorption of many radionuclides. Hydroxyapatite has many properties that make it an ideal material for use as a backfill including low water solubility (K{sub sp}>10{sup -40}), high stability under reducing and oxidizing conditions over a wide temperature range, availability, and low cost. However, there is often considerable variation in the properties of apatites depending on source and method of preparation. In this work, we characterized and compared a synthetic hydroxyapatite with hydroxyapatites prepared from cattle bone calcined at 500 C, 700 C, 900 C and 1100 C. The analysis indicated the synthetic hydroxyapatite was similar in morphology to 500 C prepared cattle hydroxyapatite. With increasing calcination temperature the crystallinity and crystal size of the hydroxyapatites increased and the BET surface area and carbonate concentration decreased. Batch sorption experiments were performed to determine the effectiveness of each material to sorb uranium. Sorption of U was strong regardless of apatite type indicating all apatite materials evaluated. Sixty day desorption experiments indicated desorption of uranium for each hydroxyapatite was negligible.
This paper develops a general framework for applying algebraic multigrid techniques to constrained systems of linear algebraic equations that arise in applications with discretized PDEs. We discuss constraint coarsening strategies for constructing multigrid coarse grid spaces and several classes of multigrid smoothers for these systems. The potential of these methods is investigated with their application to contact problems in solid mechanics. Published in 2004 by John Wiley &Sons, Ltd.
The ASCI supercomputing program is broadly defined as running physics simulations on progressively more powerful digital computers. What happens if we extrapolate the computer technology to its end? We have developed a model for key ASCI computations running on a hypothetical computer whose technology is parameterized in ways that account for advancing technology. This model includes technology information such as Moore's Law for transistor scaling and developments in cooling technology. The model also includes limits imposed by laws of physics, such as thermodynamic limits on power dissipation, limits on cooling, and the limitation of signal propagation velocity to the speed of light. We apply this model and show that ASCI computations will advance smoothly for another 10-20 years to an 'end game' defined by thermodynamic limits and the speed of light. Performance levels at the end game will vary greatly by specific problem, but will be in the Exaflops to Zetaflops range for currently anticipated problems. We have also found an architecture that would be within a constant factor of giving optimal performance at the end game. This architecture is an evolutionary derivative of the mesh-connected microprocessor (such as ASCI Red Storm or IBM Blue Gene/L). We provide designs for the necessary enhancement to microprocessor functionality and the power-efficiency of both the processor and memory system. The technology we develop in the foregoing provides a 'perfect' computer model with which we can rate the quality of realizable computer designs, both in this writing and as a way of designing future computers. This report focuses on classical computers based on irreversible digital logic, and more specifically on algorithms that simulate space computing, irreversible logic, analog computers, and other ways to address stockpile stewardship that are outside the scope of this report.
The goal of this Laboratory Directed Research & Development (LDRD) effort was to design, synthesize, and evaluate organic-inorganic nanocomposite membranes for solubility-based separations, such as the removal of higher hydrocarbons from air streams, using experiment and theory. We synthesized membranes by depositing alkylchlorosilanes on the nanoporous surfaces of alumina substrates, using techniques from the self-assembled monolayer literature to control the microstructure. We measured the permeability of these membranes to different gas species, in order to evaluate their performance in solubility-based separations. Membrane design goals were met by manipulating the pore size, alkyl group size, and alkyl surface density. We employed molecular dynamics simulation to gain further understanding of the relationship between membrane microstructure and separation performance.
We have developed infrastructure, utilities and partitioning methods to improve data partitioning in linear solvers and preconditioners. Our efforts included incorporation of data repartitioning capabilities from the Zoltan toolkit into the Trilinos solver framework, (allowing dynamic repartitioning of Trilinos matrices); implementation of efficient distributed data directories and unstructured communication utilities in Zoltan and Trilinos; development of a new multi-constraint geometric partitioning algorithm (which can generate one decomposition that is good with respect to multiple criteria); and research into hypergraph partitioning algorithms (which provide up to 56% reduction of communication volume compared to graph partitioning for a number of emerging applications). This report includes descriptions of the infrastructure and algorithms developed, along with results demonstrating the effectiveness of our approaches.
We introduce a filter-based evolutionary algorithm (FEA) for constrained optimization. The filter used by an FEA explicitly imposes the concept of dominance on a partially ordered solution set. We show that the algorithm is provably robust for both linear and nonlinear problems and constraints. FEAs use a finite pattern of mutation offsets, and our analysis is closely related to recent convergence results for pattern search methods. We discuss how properties of this pattern impact the ability of an FEA to converge to a constrained local optimum.
The views of state of art in verification and validation (V & V) in computational physics are discussed. These views are described in the framework in which predictive capability relies on V & V, as well as other factors that affect predictive capability. Some of the research topics addressed are development of improved procedures for the use of the phenomena identification and ranking table (PIRT) for prioritizing V & V activities, and the method of manufactured solutions for code verification. It also addressed development and use of hierarchical validation diagrams, and the construction and use of validation metrics incorporating statistical measures.
Given a finite set of points in Euclidean space, we can ask what is the minimum number of times a piecewise-linear path must change direction in order to pass through all of them. We prove some new upper and lower bounds for the rectilinear version of this problem in which all motion is orthogonal to the coordinate axes. We also consider the more general case of arbitrary directions.
Motivated by observations about job runtimes on the CPlant system, we use a trace-driven microsimulator to begin characterizing the performance of different classes of allocation algorithms on jobs with different communication patterns in space-shared parallel systems with mesh topology. We show that relative performance varies considerably with communication pattern. The Paging strategy using the Hilbert space-filling curve and the Best Fit heuristic performed best across several communication patterns.
CommAspen is a new agent-based model for simulating the interdependent effects of market decisions and disruptions in the telecommunications infrastructure on other critical infrastructures in the U.S. economy such as banking and finance, and electric power. CommAspen extends and modifies the capabilities of Aspen-EE, an agent-based model previously developed by Sandia National Laboratories to analyze the interdependencies between the electric power system and other critical infrastructures. CommAspen has been tested on a series of scenarios in which the communications network has been disrupted, due to congestion and outages. Analysis of the scenario results indicates that communications networks simulated by the model behave as their counterparts do in the real world. Results also show that the model could be used to analyze the economic impact of communications congestion and outages.
This document introduces the use of Trilinos, version 3.1. Trilinos has been written to support, in a rigorous manner, the solver needs of the engineering and scientific applications at Sandia National Laboratories. Aim of this manuscript is to present the basic features of some of the Trilinos packages. The presented material includes the definition of distributed matrices and vectors with Epetra, the iterative solution of linear system with AztecOO, incomplete factorizations with IFPACK, multilevel methods with ML, direct solution of linear system with Amesos, and iterative solution of nonlinear systems with NOX. With the help of several examples, some of the most important classes and methods are detailed to the inexperienced user. For the most majority, each example is largely commented throughout the text. Other comments can be found in the source of each example. This document is a companion to the Trilinos User's Guide and Trilinos Development Guides. Also, the documentation included in each of the Trilinos' packages is of fundamental importance.
Molecular analysis of cancer, at the genomic level, could lead to individualized patient diagnostics and treatments. The developments to follow will signal a significant paradigm shift in the clinical management of human cancer. Despite our initial hopes, however, it seems that simple analysis of microarray data cannot elucidate clinically significant gene functions and mechanisms. Extracting biological information from microarray data requires a complicated path involving multidisciplinary teams of biomedical researchers, computer scientists, mathematicians, statisticians, and computational linguists. The integration of the diverse outputs of each team is the limiting factor in the progress to discover candidate genes and pathways associated with the molecular biology of cancer. Specifically, one must deal with sets of significant genes identified by each method and extract whatever useful information may be found by comparing these different gene lists. Here we present our experience with such comparisons, and share methods developed in the analysis of an infant leukemia cohort studied on Affymetrix HG-U95A arrays. In particular, spatial gene clustering, hyper-dimensional projections, and computational linguistics were used to compare different gene lists. In spatial gene clustering, different gene lists are grouped together and visualized on a three-dimensional expression map, where genes with similar expressions are co-located. In another approach, projections from gene expression space onto a sphere clarify how groups of genes can jointly have more predictive power than groups of individually selected genes. Finally, online literature is automatically rearranged to present information about genes common to multiple groups, or to contrast the differences between the lists. The combination of these methods has improved our understanding of infant leukemia. While the complicated reality of the biology dashed our initial, optimistic hopes for simple answers from microarrays, we have made progress by combining very different analytic approaches.
Statistical active contour models (aka statistical pressure snakes) have attractive properties for use in mobile manipulation platforms as both a method for use in visual servoing and as a natural component of a human-computer interface. Unfortunately, the constantly changing illumination expected in outdoor environments presents problems for statistical pressure snakes and for their image gradient-based predecessors. This paper introduces a new color-based variant of statistical pressure snakes that gives superior performance under dynamic lighting conditions and improves upon the previously published results of attempts to incorporate color imagery into active deformable models.
Equilibrated melts of long chain polymers were prepared. The combination of molecular dynamic (MD) relaxation, double-bridging and slow push-off allowed the efficient and controlled preparation of equilibrated melts of short, medium, and long chains, respectively. Results were obtained for an off-lattice bead-spring model with chain lengths up to N=7000 beads.
This report documents work undertaken to endow the cognitive framework currently under development at Sandia National Laboratories with a human-like memory for specific life episodes. Capabilities have been demonstrated within the context of three separate problem areas. The first year of the project developed a capability whereby simulated robots were able to utilize a record of shared experience to perform surveillance of a building to detect a source of smoke. The second year focused on simulations of social interactions providing a queriable record of interactions such that a time series of events could be constructed and reconstructed. The third year addressed tools to promote desktop productivity, creating a capability to query episodic logs in real time allowing the model of a user to build on itself based on observations of the user's behavior.
Epetra is a package of classes for the construction and use of serial and distributed parallel linear algebra objects. It is one of the base packages in Trilinos. This document describes guidelines for Epetra coding style. The issues discussed here go beyond correct C++ syntax to address issues that make code more readable and self-consistent. The guidelines presented here are intended to aid current and future development of Epetra specifically. They reflect design decisions that were made in the early development stages of Epetra. Some of the guidelines are contrary to more commonly used conventions, but we choose to continue these practices for the purposes of self-consistency. These guidelines are intended to be complimentary to policies established in the Trilinos Developers Guide.
The Unique Signal is a key constituent of Enhanced Nuclear Detonation Safety (ENDS). Although the Unique Signal approach is well prescribed and mathematically assured, there are numerous unsolved mathematical problems that could help assess the risk of deviations from the ideal approach. Some of the mathematics-based results shown in this report are: 1. The risk that two patterns with poor characteristics (easily generated by inadvertent processes) could be combined through exclusive-or mixing to generate an actual Unique Signal pattern has been investigated and found to be minimal (not significant when compared to the incompatibility metric of actual Unique Signal patterns used in nuclear weapons). 2. The risk of generating actual Unique Signal patterns with linear feedback shift registers is minimal, but the patterns in use are not as invulnerable to inadvertent generation by dependent processes as previously thought. 3. New methods of testing pair-wise incompatibility threats have resulted in no significant problems found for the set of Unique Signal patterns currently used. Any new patterns introduced would have to be carefully assessed for compatibility with existing patterns, since some new patterns under consideration were found to be deficient when associated with other patterns in use. 4. Markov models were shown to correspond to some of the engineered properties of Unique Signal sequences. This gives new support for the original design objectives. 5. Potential dependence among events (caused by a variety of communication protocols) has been studied. New evidence has been derived of the risk associated with combined communication of multiple events, and of the improvement in abnormal-environment safety that can be achieved through separate-event communication.
Prokaryotic single-cell microbes are the simplest of all self-sufficient living organisms. Yet microbes create and use much of the molecular machinery present in more complex organisms, and the macro-molecules in microbial cells interact in regulatory, metabolic, and signaling pathways that are prototypical of the reaction networks present in all cells. We have developed a simple simulation model of a prokaryotic cell that treats proteins, protein complexes, and other organic molecules as particles which diffuse via Brownian motion and react with nearby particles in accord with chemical rate equations. The code models protein motion and chemistry within an idealized cellular geometry. It has been used to simulate several simple reaction networks and compared to more idealized models which do not include spatial effects. In this report we describe an initial version of the simulation code that was developed with FY03 funding. We discuss the motivation for the model, highlight its underlying equations, and describe simulations of a 3-stage kinase cascade and a portion of the carbon fixation pathway in the Synechococcus microbe.
Algorithms for higher order accuracy modeling of kinematic behavior within the ALEGRA framework are presented. These techniques improve the behavior of the code when kinematic errors are found, ensure orthonormality of the rotation tensor at each time step, and increase the accuracy of the Lagrangian stretch and rotation tensor update algorithm. The implementation of these improvements in ALEGRA is described. A short discussion of issues related to improving the accuracy of the stress update procedures is also included.
Solidification and blood flow seemingly have little in common, but each involves a fluid in contact with a deformable solid. In these systems, the solid-fluid interface moves as the solid advects and deforms, often traversing the entire domain of interest. Currently, these problems cannot be simulated without innumerable expensive remeshing steps, mesh manipulations or decoupling the solid and fluid motion. Despite the wealth of progress recently made in mechanics modeling, this glaring inadequacy persists. We propose a new technique that tracks the interface implicitly and circumvents the need for remeshing and remapping the solution onto the new mesh. The solid-fluid boundary is tracked with a level set algorithm that changes the equation type dynamically depending on the phases present. This novel approach to coupled mechanics problems promises to give accurate stresses, displacements and velocities in both phases, simultaneously.
High throughput instruments and analysis techniques are required in order to make good use of the genomic sequences that have recently become available for many species, including humans. These instruments and methods must work with tens of thousands of genes simultaneously, and must be able to identify the small subsets of those genes that are implicated in the observed phenotypes, or, for instance, in responses to therapies. Microarrays represent one such high throughput method, which continue to find increasingly broad application. This project has improved microarray technology in several important areas. First, we developed the hyperspectral scanner, which has discovered and diagnosed numerous flaws in techniques broadly employed by microarray researchers. Second, we used a series of statistically designed experiments to identify and correct errors in our microarray data to dramatically improve the accuracy, precision, and repeatability of the microarray gene expression data. Third, our research developed new informatics techniques to identify genes with significantly different expression levels. Finally, natural language processing techniques were applied to improve our ability to make use of online literature annotating the important genes. In combination, this research has improved the reliability and precision of laboratory methods and instruments, while also enabling substantially faster analysis and discovery.
ALEGRA is an arbitrary Lagrangian-Eulerian finite element code that emphasizes large distortion and shock propagation in inviscid fluids and solids. This document describes user options for modeling magnetohydrodynamic, thermal conduction, and radiation emission effects.
Wireless communication networks are highly resource-constrained; thus many security protocols which work in other settings may not be efficient enough for use in wireless environments. This report considers a variety of cryptographic techniques which enable secure, authenticated communication when resources such as processor speed, battery power, memory, and bandwidth are tightly limited.
We present here the details of the implementation of the parallel tempering Monte Carlo technique into a LAMMPS, a heavily used massively parallel molecular dynamics code at Sandia. This technique allows for many replicas of a system to be run at different simulation temperatures. At various points in the simulation, configurations can be swapped between different temperature environments and then continued. This allows for large regions of energy space to be sampled very quickly, and allows for minimum energy configurations to emerge in very complex systems, such as large biomolecular systems. By including this algorithm into an existing code, we immediately gain all of the previous work that had been put into LAMMPS, and allow this technique to quickly be available to the entire Sandia and international LAMMPS community. Finally, we present an example of this code applied to folding a small protein.
Genetic programming is a powerful methodology for automatically producing solutions to problems in a variety of domains. It has been used successfully to develop behaviors for RoboCup soccer players and simple combat agents. We will attempt to use genetic programming to solve a problem in the domain of strategic combat, keeping in mind the end goal of developing sophisticated behaviors for compound defense and infiltration. The simplified problem at hand is that of two armed agents in a small room, containing obstacles, fighting against each other for survival. The base case and three changes are considered: a memory of positions using stacks, context-dependent genetic programming, and strongly typed genetic programming. Our work demonstrates slight improvements from the first two techniques, and no significant improvement from the last.
This report is based on the Statement of Work (SOW) describing the various requirements for delivering 3 new supercomputer system to Sandia National Laboratories (Sandia) as part of the Department of Energy's (DOE) Accelerated Strategic Computing Initiative (ASCI) program. This system is named Red Storm and will be a distributed memory, massively parallel processor (MPP) machine built primarily out of commodity parts. The requirements presented here distill extensive architectural and design experience accumulated over a decade and a half of research, development and production operation of similar machines at Sandia. Red Storm will have an unusually high bandwidth, low latency interconnect, specially designed hardware and software reliability features, a light weight kernel compute node operating system and the ability to rapidly switch major sections of the machine between classified and unclassified computing environments. Particular attention has been paid to architectural balance in the design of Red Storm, and it is therefore expected to achieve an atypically high fraction of its peak speed of 41 TeraOPS on real scientific computing applications. In addition, Red Storm is designed to be upgradeable to many times this initial peak capability while still retaining appropriate balance in key design dimensions. Installation of the Red Storm computer system at Sandia's New Mexico site is planned for 2004, and it is expected that the system will be operated for a minimum of five years following installation.
The Sandia Petaflops Planner is a tool for projecting the design and performance of parallel supercomputers into the future. The mathematical basis of these projections is the International Technology Roadmap for Semiconductors (ITRS, or a detailed version of Moore's Law) and DOE balance factors for supercomputer procurements. The planner is capable of various forms of scenario analysis, cost estimation, and technology analysis. The tool is described along with technology conclusions regarding PFLOPS-level supercomputers in the upcoming decade.
The Computational Plant or Cplant is a commodity-based distributed-memory supercomputer under development at Sandia National Laboratories. Distributed-memory supercomputers run many parallel programs simultaneously. Users submit their programs to a job queue. When a job is scheduled to run, it is assigned to a set of available processors. Job runtime depends not only on the number of processors but also on the particular set of processors assigned to it. Jobs should be allocated to localized clusters of processors to minimize communication costs and to avoid bandwidth contention caused by overlapping jobs. This report introduces new allocation strategies and performance metrics based on space-filling curves and one dimensional allocation strategies. These algorithms are general and simple. Preliminary simulations and Cplant experiments indicate that both space-filling curves and one-dimensional packing improve processor locality compared to the sorted free list strategy previously used on Cplant. These new allocation strategies are implemented in Release 2.0 of the Cplant System Software that was phased into the Cplant systems at Sandia by May 2002. Experimental results then demonstrated that the average number of communication hops between the processors allocated to a job strongly correlates with the job's completion time. This report also gives processor-allocation algorithms for minimizing the average number of communication hops between the assigned processors for grid architectures. The associated clustering problem is as follows: Given n points in {Re}d, find k points that minimize their average pairwise L{sub 1} distance. Exact and approximate algorithms are given for these optimization problems. One of these algorithms has been implemented on Cplant and will be included in Cplant System Software, Version 2.1, to be released. In more preliminary work, we suggest improvements to the scheduler separate from the allocator.
This report summarizes the results of a three-year LDRD project on prognostics and health management. System failure over some future time interval (an alternative definition is the capability to predict the remaining useful life of a system). Prognostics are integrated with health monitoring (through inspections, sensors, etc.) to provide an overall PHM capability that optimizes maintenance actions and results in higher availability at a lower cost. Our goal in this research was to develop PHM tools that could be applied to a wide variety of equipment (repairable, non-repairable, manufacturing, weapons, battlefield equipment, etc.) and require minimal customization to move from one system to the next. Thus, our approach was to develop a toolkit of reusable software objects/components and architecture for their use. We have developed two software tools: an Evidence Engine and a Consequence Engine. The Evidence Engine integrates information from a variety of sources in order to take into account all the evidence that impacts a prognosis for system health. The Evidence Engine has the capability for feature extraction, trend detection, information fusion through Bayesian Belief Networks (BBN), and estimation of remaining useful life. The Consequence Engine involves algorithms to analyze the consequences of various maintenance actions. The Consequence Engine takes as input a maintenance and use schedule, spares information, and time-to-failure data on components, then generates maintenance and failure events, and evaluates performance measures such as equipment availability, mission capable rate, time to failure, and cost. This report summarizes the capabilities we have developed, describes the approach and architecture of the two engines, and provides examples of their use. 'Prognostics' refers to the capability to predict the probability of
A fundamental challenge for all communication systems, engineered or living, is the problem of achieving efficient, secure, and error-free communication over noisy channels. Information theoretic principals have been used to develop effective coding theory algorithms to successfully transmit information in engineering systems. Living systems also successfully transmit biological information through genetic processes such as replication, transcription, and translation, where the genome of an organism is the contents of the transmission. Decoding of received bit streams is fairly straightforward when the channel encoding algorithms are efficient and known. If the encoding scheme is unknown or part of the data is missing or intercepted, how would one design a viable decoder for the received transmission? For such systems blind reconstruction of the encoding/decoding system would be a vital step in recovering the original message. Communication engineers may not frequently encounter this situation, but for computational biologists and biotechnologist this is an immediate challenge. The goal of this work is to develop methods for detecting and reconstructing the encoder/decoder system for engineered and biological data. Building on Sandia's strengths in discrete mathematics, algorithms, and communication theory, we use linear programming and will use evolutionary computing techniques to construct efficient algorithms for modeling the coding system for minimally errored engineered data stream and genomic regulatory DNA and RNA sequences. The objective for the initial phase of this project is to construct solid parallels between biological literature and fundamental elements of communication theory. In this light, the milestones for FY2003 were focused on defining genetic channel characteristics and providing an initial approximation for key parameters, including coding rate, memory length, and minimum distance values. A secondary objective addressed the question of determining similar parameters for a received, noisy, error-control encoded data set. In addition to these goals, we initiated exploration of algorithmic approaches to determine if a data set could be approximated with an error-control code and performed initial investigations into optimization based methodologies for extracting the encoding algorithm given the coding rate of an encoded noise-free and noisy data stream.
Evidence for the existence of discrete sub-movements underlying continuous human movement has motivated many attempts to "extract" them. Although they produce visually convincing results, all of the methodologies that have been employed are prone to produce spurious decompositions. Examples of potential failures are given. A branch-and-bound algorithm for submovement extraction, capable of global nonlinear minimization (and hence capable of avoiding spurious decompositions), is developed and demonstrated.
If we are to build a supercomputer with a speed of 10{sup 15} floating operations per second (1 PetaFLOPS), interconnect technology will need to be improved considerably over what it is today. In this report, we explore one possible interconnect design for such a network. The guiding principle in this design is the optimization of all components for the finiteness of the speed of light. To achieve a linear speedup in time over well-tested supercomputers of todays' designs will require scaling up of processor power and bandwidth and scaling down of latency. Latency scaling is the most challenging: it requires a 100 ns user-to-user latency for messages traveling the full diameter of the machine. To meet this constraint requires simultaneously minimizing wire length through 3D packaging, new low-latency electrical signaling mechanisms, extremely fast routers, and new network interfaces. In this report, we outline approaches and implementations that will meet the requirements when implemented as a system. No technology breakthroughs are required.
High-resolution finite volume methods for solving systems of conservation laws have been widely embraced in research areas ranging from astrophysics to geophysics and aero-thermodynamics. These methods are typically at least second-order accurate in space and time, deliver non-oscillatory solutions in the presence of near discontinuities, e.g., shocks, and introduce minimal dispersive and diffusive effects. High-resolution methods promise to provide greatly enhanced solution methods for Sandia's mainstream shock hydrodynamics and compressible flow applications, and they admit the possibility of a generalized framework for treating multi-physics problems such as the coupled hydrodynamics, electro-magnetics and radiative transport found in Z pinch physics. In this work, we describe initial efforts to develop a generalized 'black-box' conservation law framework based on modern high-resolution methods and implemented in an object-oriented software framework. The framework is based on the solution of systems of general non-linear hyperbolic conservation laws using Godunov-type central schemes. In our initial efforts, we have focused on central or central-upwind schemes that can be implemented with only a knowledge of the physical flux function and the minimal/maximal eigenvalues of the Jacobian of the flux functions, i.e., they do not rely on extensive Riemann decompositions. Initial experimentation with high-resolution central schemes suggests that contact discontinuities with the concomitant linearly degenerate eigenvalues of the flux Jacobian do not pose algorithmic difficulties. However, central schemes can produce significant smearing of contact discontinuities and excessive dissipation for rotational flows. Comparisons between 'black-box' central schemes and the piecewise parabolic method (PPM), which relies heavily on a Riemann decomposition, shows that roughly equivalent accuracy can be achieved for the same computational cost with both methods. However, PPM clearly outperforms the central schemes in terms of accuracy at a given grid resolution and the cost of additional complexity in the numerical flux functions. Overall we have observed that the finite volume schemes, implemented within a well-designed framework, are extremely efficient with (potentially) very low memory storage. Finally, we have found by computational experiment that second and third-order strong-stability preserving (SSP) time integration methods with the number of stages greater than the order provide a useful enhanced stability region. However, we observe that non-SSP and non-optimal SSP schemes with SSP factors less than one can still be very useful if used with time-steps below the standard CFL limit. The 'well-designed' integration schemes that we have examined appear to perform well in all instances where the time step is maintained below the standard physical CFL limit.
An understanding of the dynamics of z-pinch wire array explosion and collapse is of critical interest to the development and future of pulsed power inertial confinement fusion experiments. Experimental results clearly show the extreme three-dimensional nature of the wire explosion and collapse process. The physics of this process can be approximated by the resistive magnetohydrodynamic (MHD) equations augmented by thermal and radiative transport modeling. Z-pinch MHD physics is dominated by material regions whose conductivity properties vary drastically as material passes from solid through melt into plasma regimes. At the same time void regions between the wires are modeled as regions of very low conductivity. This challenging physical situation requires a sophisticated three-dimensional modeling approach matched by sufficient computational resources to make progress in predictive modeling and improved physical understanding.
This paper describes a methodology for implementing disk-less cluster systems using the Network File System (NFS) that scales to thousands of nodes. This method has been successfully deployed and is currently in use on several production systems at Sandia National Labs. This paper will outline our methodology and implementation, discuss hardware and software considerations in detail and present cluster configurations with performance numbers for various management operations like booting.
A new capability for modeling thin-shell structures within the coupled Euler-Lagrange code, Zapotec, is under development. The new algorithm creates an artificial material interface for the Eulerian portion of the problem by expanding a Lagrangian shell element such that it has an effective thickness that spans one or more Eulerian cells. The algorithm implementation is discussed along with several examples involving blast loading on plates.
Implicit time integration coupled with SUPG discretization in space leads to additional terms that provide consistency and improve the phase accuracy for convection dominated flows. Recently, it has been suggested that for small Courant numbers these terms may dominate the streamline diffusion term, ostensibly causing destabilization of the SUPG method. While consistent with a straightforward finite element stability analysis, this contention is not supported by computational experiments and contradicts earlier Von-Neumann stability analyses of the semidiscrete SUPG equations. This prompts us to re-examine finite element stability of the fully discrete SUPG equations. A careful analysis of the additional terms reveals that, regardless of the time step size, they are always dominated by the consistent mass matrix. Consequently, SUPG cannot be destabilized for small Courant numbers. Numerical results that illustrate our conclusions are reported.
A recently developed Centroidal Voronoi Tessellation (CVT) unstructured sampling method is investigated here to assess its suitability for use in statistical sampling and function integration. CVT efficiently generates a highly uniform distribution of sample points over arbitrarily shaped M-Dimensional parameter spaces. It has recently been shown on several 2-D test problems to provide superior point distributions for generating locally conforming response surfaces. In this paper, its performance as a statistical sampling and function integration method is compared to that of Latin-Hypercube Sampling (LHS) and Simple Random Sampling (SRS) Monte Carlo methods, and Halton and Hammersley quasi-Monte-Carlo sequence methods. Specifically, sampling efficiencies are compared for function integration and for resolving various statistics of response in a 2-D test problem. It is found that on balance CVT performs best of all these sampling methods on our test problems.
Broadcasting messages through the earth is a daunting task. Indeed, broadcasting a normal telephone conversion through the earth by wireless means is impossible with todays technology. Most of us don't care, but some do. Industries that drill into the earth need wireless communication to broadcast navigation parameters. This allows them to steer their drill bits. They also need information about the natural formation that they are drilling. Measurements of parameters such as pressure, temperature, and gamma radiation levels can tell them if they have found a valuable resource such as a geothermal reservoir or a stratum bearing natural gas. Wireless communication methods are available to the drilling industry. Information is broadcast via either pressure waves in the drilling fluid or electromagnetic waves in the earth and well tubing. Data transmission can only travel one way at rates around a few baud. Given that normal Internet telephone modems operate near 20,000 baud, these data rates are truly very slow. Moreover, communication is often interrupted or permanently blocked by drilling conditions or natural formation properties. Here we describe a tool that communicates with stress waves traveling through the steel drill pipe and production tubing in the well. It's based on an old idea called Acoustic Telemetry. But what we present here is more than an idea. This tool exists, it's drilled several wells, and it works. Currently, it's the first and only acoustic telemetry tool that can withstand the drilling environment. It broadcasts one way over a limited range at much faster rates than existing methods, but we also know how build a system that can communicate both up and down wells of indefinite length.
This report presents a perspective on the role of code comparison activities in verification and validation. We formally define the act of code comparison as the Code Comparison Principle (CCP) and investigate its application in both verification and validation. One of our primary conclusions is that the use of code comparisons for validation is improper and dangerous. We also conclude that while code comparisons may be argued to provide a beneficial component in code verification activities, there are higher quality code verification tasks that should take precedence. Finally, we provide a process for application of the CCP that we believe is minimal for achieving benefit in verification processes.
We seek to understand which supercomputer architecture will be best for supercomputers at the Petaflops scale and beyond. The process we use is to predict the cost and performance of several leading architectures at various years in the future. The basis for predicting the future is an expanded version of Moore's Law called the International Technology Roadmap for Semiconductors (ITRS). We abstract leading supercomputer architectures into chips connected by wires, where the chips and wires have electrical parameters predicted by the ITRS. We then compute the cost of a supercomputer system and the run time on a key problem of interest to the DOE (radiation transport). These calculations are parameterized by the time into the future and the technology expected to be available at that point. We find the new advanced architectures have substantial performance advantages but conventional designs are likely to be less expensive (due to economies of scale). We do not find a universal ''winner'', but instead the right architectural choice is likely to involve non-technical factors such as the availability of capital and how long people are willing to wait for results.
The Trilinos Project is an effort to facilitate the design, development, integration and ongoing support of mathematical software libraries. In particular, our goal is to develop parallel solver algorithms and libraries within an object-oriented software framework for the solution of large-scale, complex multi-physics engineering and scientific applications. Our emphasis is on developing robust, scalable algorithms in a software framework, using abstract interfaces for flexible interoperability of components while providing a full-featured set of concrete classes that implement all abstract interfaces. Trilinos uses a two-level software structure designed around collections of packages. A Trilinos package is an integral unit usually developed by a small team of experts in a particular algorithms area such as algebraic preconditioners, nonlinear solvers, etc. Packages exist underneath the Trilinos top level, which provides a common look-and-feel, including configuration, documentation, licensing, and bug-tracking. Trilinos packages are primarily written in C++, but provide some C and Fortran user interface support. We provide an open architecture that allows easy integration with other solver packages and we deliver our software to the outside community via the Gnu Lesser General Public License (LGPL). This report provides an overview of Trilinos, discussing the objectives, history, current development and future plans of the project.
The Trilinos Project is an effort to facilitate the design, development, integration and ongoing support of mathematical software libraries. A new software capability is introduced into Trilinos as a package. A Trilinos package is an integral unit usually developed by a small team of experts in a particular algorithms area such as algebraic preconditioners, nonlinear solvers, etc. The Trilinos Users Guide is a resource for new and existing Trilinos users. Topics covered include how to configure and build Trilinos, what is required to integrate an existing package into Trilinos and examples of how those requirements can be met, as well as what tools and services are available to Trilinos packages. Also discussed are some common practices that are followed by many Trilinos package developers. Finally, a snapshot of current Trilinos packages and their interoperability status is provided, along with a list of supported computer platforms.
Protein microtubules (MTs) 25 nm in diameter and tens of micrometers long have been used as templates for the biomimetic mineralization of FeOOH. Exposure of MTs to anaerobic aqueous solutions of Fe{sup 2+} buffered to neutral pH followed by aerial oxidation leads to the formation of iron oxide coated MTs. The iron oxide layer was found to grow via a two-step process: initially formed 10-30 nm thick coatings were found to be amorphous in structure and comprised of several iron-containing species. Further growth resulted in MTs coated with highly crystalline layers of lepidocrocite with a controllable thickness of up to 125 nm. On the micrometer size scale, these coated MTs were observed to form large, irregular bundles containing hundreds of individually coated MTs. Iron oxide grew selectively on the MT surface, a result of the highly charged MT surface that provided an interface favorable for iron oxide nucleation. This result illustrates that MTs can be used as scaffolds for the in-situ production of high-aspect-ratio inorganic nanowires.
Currently, the Egyptian Atomic Energy Authority is designing a shallow-land disposal facility for low-level radioactive waste. To insure containment and prevent migration of radionuclides from the site, the use of a reactive backfill material is being considered. One material under consideration is hydroxyapatite, Ca{sub 10}(PO{sub 4}){sub 6}(OH){sub 2}, which has a high affinity for the sorption of many radionuclides. Hydroxyapatite has many properties that make it an ideal material for use as a backfill including low water solubility (K{sub sp} > 10{sup -40}), high stability under reducing and oxidizing conditions over a wide temperature range, availability, and low cost. However, there is often considerable variation in the properties of apatites depending on source and method of preparation. In this work, we characterized and compared a synthetic hydroxyapatite with hydroxyapatites prepared from cattle bone calcined at 500 C, 700 C, 900 C and 1100 C. The analysis indicated the synthetic hydroxyapatite was similar in morphology to 500 C prepared cattle hydroxyapatite. With increasing calcination temperature the crystallinity and crystal size of the hydroxyapatites increased and the BET surface area and carbonate concentration decreased. Batch sorption experiments were performed to determine the effectiveness of each material to sorb uranium. Sorption of U was strong regardless of apatite type indicating all apatite materials evaluated. Sixty day desorption experiments indicated desorption of uranium for each hydroxyapatite was negligible.
The maximum contact map overlap (MAX-CMO) between a pair of protein structures can be used as a measure of protein similarity. It is a purely topological measure and does not depend on the sequence of the pairs involved in the comparison. More importantly, the MAX-CMO present a very favorable mathematical structure which allows the formulation of integer, linear and Lagrangian models that can be used to obtain guarantees of optimality. It is not the intention of this paper to discuss the mathematical properties of MAX-CMO in detail as this has been dealt elsewhere. In this paper we compare three algorithms that can be used to obtain maximum contact map overlaps between protein structures. We will point to the weaknesses and strengths of each one. It is our hope that this paper will encourage researchers to develop new and improve methods for protein comparison based on MAX-CMO.
We consider the convergence properties of a non-elitist self-adaptive evolutionary strategy (ES) on multi-dimensional problems. In particular, we apply our recent convergence theory for a discretized (1,{lambda})-ES to design a related (1,{lambda})-ES that converges on a class of seperable, unimodal multi-dimensional problems. The distinguishing feature of self-adaptive evolutionary algorithms (EAs) is that the control parameters (like mutation step lengths) are evolved by the evolutionary algorithm. Thus the control parameters are adapted in an implicit manner that relies on the evolutionary dynamics to ensure that more effective control parameters are propagated during the search. Self-adaptation is a central feature of EAs like evolutionary stategies (ES) and evolutionary programming (EP), which are applied to continuous design spaces. Rudolph summarizes theoretical results concerning self-adaptive EAs and notes that the theoretical underpinnings for these methods are essentially unexplored. In particular, convergence theories that ensure convergence to a limit point on continuous spaces have only been developed by Rudolph, Hart, DeLaurentis and Ferguson, and Auger et al. In this paper, we illustrate how our analysis of a (1,{lambda})-ES for one-dimensional unimodal functions can be used to ensure convergence of a related ES on multidimensional functions. This (1,{lambda})-ES randomly selects a search dimension in each iteration, along which points generated. For a general class of separable functions, our analysis shows that the ES searches along each dimension independently, and thus this ES converges to the (global) minimum.
Blastwalls are often assumed to be the answer for facility protection from malevolent explosive assault, particularly from large vehicle bombs (LVB's). The assumption is made that the blastwall, if it is built strong enough to survive, will provide substantial protection to facilities and people on the side opposite the LVB. This paper will demonstrate through computer simulations and experimental data the behavior of explosively induced air blasts during interaction with blastwalls. It will be shown that air blasts can effectively wrap around and over blastwalls. Significant pressure reduction can be expected on the downstream side of the blastwall but substantial pressure will continue to propagate. The effectiveness of the blastwall to reduce blast overpressure depends on the geometry of the blastwall and the location of the explosive relative to the blastwall.
The purpose of this study was to investigate the impact of instructions on aircraft visual inspection performance and strategy. Forty-two inspectors from industry were asked to perform inspections of six areas of a Boeing 737. Six different instruction versions were developed for each inspection task, varying in the number and type of directed inspections. The amount of time spent inspecting, the number of calls made, and the number of the feedback calls detected all varied widely across the inspectors. However, inspectors who used instructions with a higher number of directed inspections referred to the instructions more often during and after the task, and found a higher percentage of a selected set of feedback cracks than inspectors using other instruction versions. This suggests that specific instructions can help overall inspection performance, not just performance on the defects specified. Further, instructions were shown to change the way an inspector approaches a task.
Memory may be the only system component that is more commoditized than a microprocessor. To simultaneously exploit this and address the impending memory wall, processing in memory (PIM) research efforts are considering ways to move processing into memory without significantly increasing the cost of the memory. As such, PIM devices may become the basis for future commodity clusters. Although these PIM devices may leverage new computational paradigms such as hardware support for multi-threading and traveling threads, they must provide support for legacy programming models if they are to supplant commodity clusters. This paper presents a prototype implementation of MPI over a traveling thread mechanism called parcels. A performance analysis indicates that the direct hardware support of a traveling thread model can lead to an efficient, lightweight MPI implementation.
Mechanisms for enhanced low-dose-rate sensitivity are described. In these mechanisms, bimolecular reactions dominate the kinetics at high dose rates thereby causing a sub-linear dependence on total dose, and this leads to a dose-rate dependence. These bimolecular mechanisms include electron-hole recombination, hydrogen recapture at hydrogen source sites, and hydrogen dimerization to form hydrogen molecules. The essence of each of these mechanisms is the dominance of the bimolecular reactions over the radiolysis reaction at high dose rates. However, at low dose rates, the radiolysis reaction dominates leading to a maximum effect of the radiation.
Properties of relevance for the equation of state for a high-density glass are discussed. We review the effects of failure waves, comminuted phase, and compaction on the validity of the Mie-Grueneisen EOS. The specific heat and the Grueneisen parameter at standard conditions for a {rho}{sub 0} = 5.085 g/cm{sup 3} glass ('Glass A') is then estimated to be 522 mJ/g/K and 0.1-0.3, respectively. The latter value is substantially smaller than the value of 2.1751 given in the SESAME tables for a high-density glass with {rho}{sub 0} = 5.46 g/cm{sup 3}. The present unusual value of the Grueneisen parameter is confirmed from the volume dependence determined from fitting the Mie-Grueneisen EOS to shock data in Ref. [2].
Submovements are hypothesized building blocks of human movement, discrete ballistic movements of which more complex movements are composed. Using a novel algorithm, submovements were extracted from the point-to-point movements of 41 persons recovering from stroke. Analysis of the extracted submovements showed that, over the course of therapy, patients' submovements tended to increase in peak speed and duration. The number of submovements employed to produce a given movement decreased. The time between the peaks of adjacent submovements decreased for inpatients (those less than 1 month post-stroke), but not for outpatients (those greater than 12 months post-stroke) as a group. Submovements became more overlapped for all patients, but more markedly for inpatients. The strength and consistency with which it quantified patients' recovery indicates that analysis of submovement overlap might be a useful tool for measuring learning or other changes in motor behavior in future human movement studies.
Proposed for publication in Coordinated & Multiple Views in Exploratory Visualization, Special Issue of Information Visualization Journal, Vol 2 No. 4, Palgrave/Macmillan.
PIM (Processor in Memory) architectures are being proposed for future supercomputers, because they reduce the problems that SMP MMPs have with latency. However, they do not meet the SMP MPP balance factors. Being relatively processor rich and memory starved, it is unclear whether an ASCI application could run on them, either as-is or with recoding. The KBA (Koch-Baker-Alcouffe) algorithm (Koch, 1992) for particle transport (radiation transport) is shown not to fit on PIMs as written. When redesigned with a 3-D allocation of cells to PIMs, the resulting algorithm is projected to execute an order of magnitude faster and more cost-effectively than the KBA algorithm, albeit with high initial hardware costs.
We present a series of electronic structure calculations that demonstrate a mechanism for spontaneous ionization of hydrogen at the Si-SiO{sub 2} interface. Specifically, we show that an isolated neutral hydrogen atom will spontaneously give up its charge and bond to a threefold coordinated oxygen atom. We refer to this entity as a proton. We have calculated the potential surface and found it to be entirely attractive. In contrast, hydrogen molecules will not undergo an analogous reaction. We relate these calculations both to proton generation experiments and to hydrogen plasma experiments.