The Sandia Analysis Workbench (SAW) project has developed and deployed a production capability for SIERRA computational mechanics analysis workflows. However, the electrical analysis workflow capability requirements have only been demonstrated in early prototype states, with no real capability deployed for analysts’ use. This milestone aims to improve the electrical analysis workflow capability (via SAW and related tools) and deploy it for ongoing use. We propose to focus on a QASPR electrical analysis calibration workflow use case. We will include a number of new capabilities (versus today’s SAW), such as: 1) support for the XYCE code workflow component, 2) data management coupled to electrical workflow, 3) human-in-theloop workflow capability, and 4) electrical analysis workflow capability deployed on the restricted (and possibly classified) network at Sandia. While far from the complete set of capabilities required for electrical analysis workflow over the long term, this is a substantial first step toward full production support for the electrical analysts.
e Encore soware package is both a stand-alone application and a soware library. is guide explains the syntax of Encore input, provides examples, and is a comprehensive catalog of the Encore commands. Acting as a stand-alone application, Encore provides utilities for reading solutions from les and enables solution verication, postprocessing, eld transfers, and basic mesh renement. Acting as a soware library, Encore is a component of the uid, thermal, and solid modeling applications in the Sierra Mechanics suite. As a library, Encore provides the enclosing modeling application a superset of the stand-alone capabilities--enabled by application specic information--including physics specic postprocessors and adaptive mesh renement.
For a CASL grid-to-rod fretting problem, Sandia's Percept software was used in conjunction with the Sierra Mechanics suite to analyze the convergence behavior of the data transfer from a fluid simulation to a solid mechanics simulation. An analytic function, with properties relatively close to numerically computed fluid approximations, was chosen to represent the pressure solution in the fluid domain. The analytic pressure was interpolated on a sequence of grids on the fluid domain, and transferred onto a separate sequence of grids in the solid domain. The error in the resulting pressure in the solid domain was measured with respect to the analytic pressure. The error in pressure approached zero as both the fluid and solids meshes were refined. The convergence of the transfer algorithm was limited by whether the source grid resolution was the same or finer than the target grid resolution. In addition, using a feature coverage analysis, we found gaps in the solid mechanics code verification test suite directly relevant to the prototype CASL GTRF simulations.
We examine algorithms for the finite element approximation of thermal contact models. We focus on the implementation of thermal contact algorithms in SIERRA Mechanics. Following the mathematical formulation of models for tied contact and resistance contact, we present three numerical algorithms: (1) the multi-point constraint (MPC) algorithm, (2) a resistance algorithm, and (3) a new generalized algorithm. We compare and contrast both the correctness and performance of the algorithms in three test problems. We tabulate the convergence rates of global norms of the temperature solution on sequentially refined meshes. We present the results of a parameter study of the effect of contact search tolerances. We outline best practices in using the software for predictive simulations, and suggest future improvements to the implementation.
This report documents the results for an FY06 ASC Algorithms Level 2 milestone combining error estimation and adaptivity, uncertainty quantification, and probabilistic design capabilities applied to the analysis and design of bistable MEMS. Through the use of error estimation and adaptive mesh refinement, solution verification can be performed in an automated and parameter-adaptive manner. The resulting uncertainty analysis and probabilistic design studies are shown to be more accurate, efficient, reliable, and convenient.