An alternative theory of solid mechanics, known as the peridynamic theory, formulates problems in terms of integral equations rather than partial differential equations. This theory assumes that particles in a continuum interact with each other across a finite distance, as in molecular dynamics. Damage is incorporated in the theory at the level of these two-particle interactions, so localization and fracture occur as a natural outgrowth of the equation of motion and constitutive models. A numerical method for solving dynamic problems within the peridynamic theory is described. Accuracy and numerical stability are discussed. Examples illustrate the properties of the method for modeling brittle dynamic crack growth.
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 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.
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 paper is about making reversible logic a reality for supercomputing. Reversible logic offers a way to exceed certain basic limits on the performance of computers, yet a powerful case will have to be made to justify its substantial development expense. This paper explores the limits of current, irreversible logic for supercomputers, thus forming a threshold above which reversible logic is the only solution. Problems above this threshold are discussed, with the science and mitigation of global warming being discussed in detail. To further develop the idea of using reversible logic in supercomputing, a design for a 1 Zettaflops supercomputer as required for addressing global climate warming is presented. However, to create such a design requires deviations from the mainstream of both the software for climate simulation and research directions of reversible logic. These deviations provide direction on how to make reversible logic practical
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.
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.
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.
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.
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.
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.