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Title: Experiences using DAKOTA stochastic expansion methods in computational simulations.

Abstract

Uncertainty quantification (UQ) methods bring rigorous statistical connections to the analysis of computational and experiment data, and provide a basis for probabilistically assessing margins associated with safety and reliability. The DAKOTA toolkit developed at Sandia National Laboratories implements a number of UQ methods, which are being increasingly adopted by modeling and simulation teams to facilitate these analyses. This report disseminates results as to the performance of DAKOTA's stochastic expansion methods for UQ on a representative application. Our results provide a number of insights that may be of interest to future users of these methods, including the behavior of the methods in estimating responses at varying probability levels, and the expansion levels for the methodologies that may be needed to achieve convergence.

Authors:
;
Publication Date:
Research Org.:
Sandia National Laboratories
Sponsoring Org.:
USDOE
OSTI Identifier:
1035314
Report Number(s):
SAND2012-0107
TRN: US201205%%77
DOE Contract Number:
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; CONVERGENCE; D CODES; PERFORMANCE; PROBABILITY; RELIABILITY; SAFETY; SANDIA NATIONAL LABORATORIES; SIMULATION

Citation Formats

Templeton, Jeremy Alan, and Ruthruff, Joseph R. Experiences using DAKOTA stochastic expansion methods in computational simulations.. United States: N. p., 2012. Web. doi:10.2172/1035314.
Templeton, Jeremy Alan, & Ruthruff, Joseph R. Experiences using DAKOTA stochastic expansion methods in computational simulations.. United States. doi:10.2172/1035314.
Templeton, Jeremy Alan, and Ruthruff, Joseph R. 2012. "Experiences using DAKOTA stochastic expansion methods in computational simulations.". United States. doi:10.2172/1035314. https://www.osti.gov/servlets/purl/1035314.
@article{osti_1035314,
title = {Experiences using DAKOTA stochastic expansion methods in computational simulations.},
author = {Templeton, Jeremy Alan and Ruthruff, Joseph R.},
abstractNote = {Uncertainty quantification (UQ) methods bring rigorous statistical connections to the analysis of computational and experiment data, and provide a basis for probabilistically assessing margins associated with safety and reliability. The DAKOTA toolkit developed at Sandia National Laboratories implements a number of UQ methods, which are being increasingly adopted by modeling and simulation teams to facilitate these analyses. This report disseminates results as to the performance of DAKOTA's stochastic expansion methods for UQ on a representative application. Our results provide a number of insights that may be of interest to future users of these methods, including the behavior of the methods in estimating responses at varying probability levels, and the expansion levels for the methodologies that may be needed to achieve convergence.},
doi = {10.2172/1035314},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2012,
month = 1
}

Technical Report:

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  • Computational materials simulations have traditionally focused on individual phenomena: grain growth, crack propagation, plastic flow, etc. However, real materials behavior results from a complex interplay between phenomena. In this project, the authors explored methods for coupling mesoscale simulations of microstructural evolution and micromechanical response. In one case, massively parallel (MP) simulations for grain evolution and microcracking in alumina stronglink materials were dynamically coupled. In the other, codes for domain coarsening and plastic deformation in CuSi braze alloys were iteratively linked. this program provided the first comparison of two promising ways to integrate mesoscale computer codes. Coupled microstructural/micromechanical codes were appliedmore » to experimentally observed microstructures for the first time. In addition to the coupled codes, this project developed a suite of new computational capabilities (PARGRAIN, GLAD, OOF, MPM, polycrystal plasticity, front tracking). The problem of plasticity length scale in continuum calculations was recognized and a solution strategy was developed. The simulations were experimentally validated on stockpile materials.« less
  • This report presents the basics of a new stochastic model for seismically-generated pore pressure and shear strain potential and illustrates its use for documented case histories. Model parameters are chosen according to available information on the variability of soil properties, and it is applied to sites where liquefaction was observed and where no evidence of liquefaction was observed and where no evidence of liquefaction was observed after major seismic events. Results of the analysis are in substantial agreement with observed field behavior, indicating that this model can be used in a predictive capacity if parameters are chosen correctly. An applicationmore » of the model to a comprehensive risk analysis of seismically induced initial liquefaction is also briefly described. An example using available seismic information for a hypothetical soil site near San Francisco is presented to illustrate the use of this type of model. Two models are applied to documented case histories to demonstrate their applicability and to illustrate how the probabilistic design parameters are chosen. The probabilistic pore pressure model developed by Chameau (1980) and the probabilistic shear strain model developed by Hadj Hamou (1982) are used herein to analyze the behavior of three sites where liquefaction did and did not occur during earthquakes.« less
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