This paper presents a description of the SIERRA Framework Version 3 parallel transfer operators. The high-level design including object interrelationships, as well as requirements for their use, is discussed. Transfer operators are used for moving field data from one computational mesh to another. The need for this service spans many different applications. The most common application is to enable loose coupling of multiple physics modules, such as for the coupling of a quasi-statics analysis with a thermal analysis. The SIERRA transfer operators support the transfer of nodal and element fields between meshes of different, arbitrary parallel decompositions. Also supplied are ''copy'' transfer operators for efficient transfer of fields between identical meshes. A ''copy'' transfer operator is also implemented for constraint objects. Each of these transfer operators is described. Also, two different parallel algorithms are presented for handling the geometric misalignment between different parallel-distributed meshes.
Increasing concerns for the security of the national infrastructure have led to a growing need for improved management and control of municipal water networks. To deal with this issue, optimization offers a general and extremely effective method to identify (possibly harmful) disturbances, assess the current state of the network, and determine operating decisions that meet network requirements and lead to optimal performance. This paper details an optimization strategy for the identification of source disturbances in the network. Here we consider the source inversion problem modeled as a nonlinear programming problem. Dynamic behavior of municipal water networks is simulated using EPANET. This approach allows for a widely accepted, general purpose user interface. For the source inversion problem, flows and concentrations of the network will be reconciled and unknown sources will be determined at network nodes. Moreover, intrusive optimization and sensitivity analysis techniques are identified to assess the influence of various parameters and models in the network in a computational efficient manner. A number of numerical comparisons are made to demonstrate the effectiveness of various optimization approaches.
This paper presents a high-level overview of the algorithms and supporting functionality provided by SIERRA Framework Version 3 for h-adaptive finite-element mechanics application development. Also presented is a fairly comprehensive description of what is required by the application codes to use the SIERRA h-adaptivity services. In general, the SIERRA framework provides the functionality for hierarchically subdividing elements in a distributed parallel environment, as well as dynamic load balancing. The mechanics application code is required to supply an a posteriori error indicator, prolongation and restriction operators for the field variables, hanging-node constraint handlers, and execution control code. This paper does not describe the Application Programming Interface (API), although references to SIERRA framework classes are given where appropriate.
The SIERRA Framework core services provide essential services for managing the mesh data structure, computational fields, and physics models of an application. An application using these services will supply a set of physics models, define the computational fields that are required by those models, and define the mesh upon which its physics models operate. The SIERRA Framework then manages all of the data for a massively parallel multiphysics application.
Aspen, a powerful economic modeling tool that uses agent modeling and genetic algorithms, can accurately simulate the economy. In it, individuals are hired by firms to produce a good that households then purchase. The firms decide what price to charge for this good, and based on that price, the households determine which firm to purchase from. We will attempt to discover the Nash Equilibrium price found in this model under two different methods of determining how many orders each firm receives. To keep it simple, we will assume there are only two firms in our model, and that these firms compete for the sale of one identical good.
This LDRD project has involved the development and application of Sandia's massively parallel materials modeling software to several significant biophysical systems. They have been successful in applying the molecular dynamics code LAMMPS to modeling DNA, unstructured proteins, and lipid membranes. They have developed and applied a coupled transport-molecular theory code (Tramonto) to study ion channel proteins with gramicidin A as a prototype. they have used the Towhee configurational bias Monte-Carlo code to perform rigorous tests of biological force fields. they have also applied the MP-Sala reacting-diffusion code to model cellular systems. Electroporation of cell membranes has also been studied, and detailed quantum mechanical studies of ion solvation have been performed. In addition, new molecular theory algorithms have been developed (in FasTram) that may ultimately make protein solvation calculations feasible on workstations. Finally, they have begun implementation of a combined molecular theory and configurational bias Monte-Carlo code. They note that this LDRD has provided a basis for several new internal (e.g. several new LDRD) and external (e.g. 4 NIH proposals and a DOE/Genomes to Life) proposals.
This manual describes the use of the Xyce Parallel Electronic Simulator code for simulating electrical circuits at a variety of abstraction levels. The Xyce Parallel Electronic Simulator has been written to support,in a rigorous manner, the simulation needs of the Sandia National Laboratories electrical designers. As such, the development has focused on improving the capability over the current state-of-the-art in the following areas: (1) 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. (2) Improved performance for all numerical kernels (e.g., time integrator, nonlinear and linear solvers) through state-of-the-art algorithms and novel techniques. (3) A client-server or multi-tiered operating model wherein the numerical kernel can operate independently of the graphical user interface (GUI). (4) 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. The code is a parallel code in the most general sense of the phrase--a message passing parallel implementation--which allows it to run efficiently on the widest possible number of computing platforms. These include serial, shared-memory and distributed-memory parallel as well as heterogeneous platforms. Furthermore, careful attention has been paid to the specific nature of circuit-simulation problems to ensure that optimal parallel efficiency is achieved even as the number of processors grows. Another 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 Parallel Electronic Simulator is designed to support a variety of device model inputs. These input formats include standard analytical models, behavioral models and look-up tables. Combined with this flexible interface is an architectural design that greatly simplifies the addition of circuit models. One of the most important contribution Xyce makes to the designers at Sandia National Laboratories 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. Furthermore, these capabilities will then be migrated to the end users.
This report presents a detailed multi-methods comparison of the spatial errors associated with finite difference, finite element and finite volume semi-discretizations of the scalar advection-diffusion equation. The errors are reported in terms of non-dimensional phase and group speeds, discrete diffusivity, artificial diffusivity, and grid-induced anisotropy. It is demonstrated that Fourier analysis (aka von Neumann analysis) provides an automatic process for separating the spectral behavior of the discrete advective operator into its symmetric dissipative and skew-symmetric advective components. Further it is demonstrated that streamline upwind Petrov-Galerkin and its control-volume finite element analogue, streamline upwind control-volume, produce both an artificial diffusivity and an artificial phase speed in addition to the usual semi-discrete artifacts observed in the discrete phase speed, group speed and diffusivity. For each of the numerical methods considered, asymptotic truncation error and resolution estimates are presented for the limiting cases of pure advection and pure diffusion. The Galerkin finite element method and its streamline upwind derivatives are shown to exhibit super-convergent behavior in terms of phase and group speed when a consistent mass matrix is used in the formulation. In contrast, the CVFEM method and its streamline upwind derivatives yield strictly second-order behavior. While this work can only be considered a first step in a comprehensive multi-methods analysis and comparison, it serves to identify some of the relative strengths and weaknesses of multiple numerical methods in a common mathematical framework.
ALEGRA is an arbitrary Lagrangian-Eulerian finite element code that emphasizes large distortion and shock propagation. This document describes the user input language for the code.
Three years of large-scale PDE-constrained optimization research and development are summarized in this report. We have developed an optimization framework for 3 levels of SAND optimization and developed a powerful PDE prototyping tool. The optimization algorithms have been interfaced and tested on CVD problems using a chemically reacting fluid flow simulator resulting in an order of magnitude reduction in compute time over a black box method. Sandia's simulation environment is reviewed by characterizing each discipline and identifying a possible target level of optimization. Because SAND algorithms are difficult to test on actual production codes, a symbolic simulator (Sundance) was developed and interfaced with a reduced-space sequential quadratic programming framework (rSQP++) to provide a PDE prototyping environment. The power of Sundance/rSQP++ is demonstrated by applying optimization to a series of different PDE-based problems. In addition, we show the merits of SAND methods by comparing seven levels of optimization for a source-inversion problem using Sundance and rSQP++. Algorithmic results are discussed for hierarchical control methods. The design of an interior point quadratic programming solver is presented.
A distributed reconfigurable micro-robotic system is a collection of unlimited numbers of distributed small, homogeneous robots designed to autonomously organize and reorganize in order to achieve mission-specified geometric shapes and functions. This project investigated the design, control, and planning issues for self-configuring and self-organizing robots. In the 2D space a system consisting of two robots was prototyped and successfully displayed automatic docking/undocking to operate dependently or independently. Additional modules were constructed to display the usefulness of a self-configuring system in various situations. In 3D a self-reconfiguring robot system of 4 identical modules was built. Each module connects to its neighbors using rotating actuators. An individual component can move in three dimensions on its neighbors. We have also built a self-reconfiguring robot system consisting of 9-module Crystalline Robot. Each module in this robot is actuated by expansion/contraction. The system is fully distributed, has local communication (to neighbors) capabilities and it has global sensing capabilities.
Imaging systems such as Synthetic Aperture Radar collect band-limited data from which an image of a target scene is rendered. The band-limited nature of the data generates sidelobes, or ''spilled energy'' most evident in the neighborhood of bright point-like objects. It is generally considered desirable to minimize these sidelobes, even at the expense of some generally small increase in system bandwidth. This is accomplished by shaping the spectrum with window functions prior to inversion or transformation into an image. A window function that minimizes sidelobe energy can be constructed based on prolate spheroidal wave functions. A parametric design procedure allows doing so even with constraints on allowable increases in system bandwidth. This approach is extended to accommodate spectral notches or holes, although the guaranteed minimum sidelobe energy can be quite high in this case. Interestingly, for a fixed bandwidth, the minimum-mean-squared-error image rendering of a target scene is achieved with no windowing at all (rectangular or boxcar window).
In this report we describe the performance of the ALEGRA shock wave physics code on a set of gas dynamic shock reflection problems that have associated experimental pressure data. These reflections cover three distinct regimes of oblique shock reflection in gas dynamics--regular, Mach, and double Mach reflection. For the selected data, the use of an ideal gas equation of state is appropriate, thus simplifying to a considerable degree the task of validating the shock wave computational capability of ALEGRA in the application regime of the experiments. We find good agreement of ALEGRA with reported experimental data for sufficient grid resolution. We discuss the experimental data, the nature and results of the corresponding ALEGRA calculations, and the implications of the presented experiment--calculation comparisons.
Over the past several decades, the development of computer models to predict the atmospheric transport of hazardous material across a local (on the order of 10s of km) to mesoscale (on the order of 100s of km) region has received considerable attention, for both regulatory purposes, and to guide emergency response teams. Wind inputs to these models cover a spectrum of sophistication and required resources. At one end is the interpolation/extrapolation of available observations, which can be done rapidly, but at the risk of missing important local phenomena. Such a model can also only describe the wind at the time the observations were made. At the other end are sophisticated numerical solutions based on so-called Primitive Equation models. These prognostic models, so-called because in principle they can forecast future conditions, contain the most physics, but can easily consume tens of hours, if not days, of computer time. They may also require orders of magnitude more effort to set up, as both boundary and initial conditions on all the relevant variables must be supplied. The subject of this report is two classes of models intermediate in sophistication between the interpolated and prognostic ends of the spectrum. The first, known as mass-consistent (sometimes referred to as diagnostic) models, attempt to strike a compromise between simple interpolation and the complexity of the Primitive Equation models by satisfying only the conservation of mass (continuity) equation. The second class considered here consists of the so-called linear models, which purport to satisfy both mass and momentum balances. A review of the published literature on these models over the past few decades was performed. Though diagnostic models use a variety of approaches, they tend to fall into a relatively few well-defined categories. Linear models, on the other hand, follow a more uniform methodology, though they differ in detail. The discussion considers the theoretical underpinnings of each category of the diagnostic models, and the linear models, in order to assess the advantages and disadvantages of each. It is concluded that diagnostic models are the better suited of the two for predicting the atmospheric dispersion of hazardous materials in emergency response scenarios, as the linear models are only able to accommodate gently-sloping terrain, and are predicated on several simplifying approximations which can be difficult to justify a priori. Of the various approaches used in diagnostic modeling, that based on the calculus of variations appears to be the most objective, in that it introduces the fewest number of arbitrary parameters. The strengths and weaknesses of models in this category, as they relate to the activities of Sandia's Nuclear Emergency Support Team (NEST), are further highlighted.
This report describes a 3-D fluid mechanics code for predicting flow past bluff bodies whose surfaces can be assumed to be made up of shell elements that are simply connected. Version 1.0 of the VIPAR code (Vortex Inflation PARachute code) is described herein. This version contains several first order algorithms that we are in the process of replacing with higher order ones. These enhancements will appear in the next version of VIPAR. The present code contains a motion generator that can be used to produce a large class of rigid body motions. The present code has also been fully coupled to a structural dynamics code in which the geometry undergoes large time dependent deformations. Initial surface geometry is generated from triangular shell elements using a code such as Patran and is written into an ExodusII database file for subsequent input into VIPAR. Surface and wake variable information is output into two ExodusII files that can be post processed and viewed using software such as EnSight{trademark}.