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Title: Bison Robustness and Performance Improvements

Abstract

In last year’s Bison robustness report, the authors demonstrated that improving the Jacobian matrix can drastically increase solution convergence rates. The development of an automatic differentiation (AD) capability in the MOOSE framework has created the possibility of having exact Jacobian matrices for all of our material models. A large focus in this year’s report is the application of AD to MOOSE physics modules, such as the tensor mechanics module, and the application to Bison fuel and cladding material models. A second main focus in this year’s report is the investigation of a set of algorithmic solver changes to improve the robustness and performance of Bison LWR simulations. Beyond providing increased solver robustness, the ability to solve strongly coupled multiphysics problems, and solving large deformation problems with ease, these automatically generated perfect Jacobians also drastically reduce development time for new models. In models converted to AD, Jacobian computation made up the majority of the code base. With this code removed, both readability and maintainability of the MOOSE/Bison code base are improved. The work documented here significantly improved the robustness of the Bison LWR assessment case suite. Through algorithmic changes in solver infrastructure, such as the application of predictors and improvements inmore » timestepping, tangible improvements in performance were observed. These improvements are documented using a new standard set of metrics that are collected in all assessment cases, and a suite of statistical analysis tools developed for this report.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [2]
  1. Idaho National Laboratory
  2. Southwestern Scientific
Publication Date:
Research Org.:
Idaho National Lab. (INL), Idaho Falls, ID (United States)
Sponsoring Org.:
USDOE Office of Nuclear Energy (NE)
OSTI Identifier:
1530814
Report Number(s):
INL/EXT-19-54481-Rev000
DOE Contract Number:  
AC07-05ID14517
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS; Bison; Code Robustness; Automatic Differentiation

Citation Formats

Schwen, Daniel, Spencer, Benjamin W, Pitts, Stephanie A, McDowell, Dylan James, and Stafford, Shane. Bison Robustness and Performance Improvements. United States: N. p., 2019. Web. doi:10.2172/1530814.
Schwen, Daniel, Spencer, Benjamin W, Pitts, Stephanie A, McDowell, Dylan James, & Stafford, Shane. Bison Robustness and Performance Improvements. United States. doi:10.2172/1530814.
Schwen, Daniel, Spencer, Benjamin W, Pitts, Stephanie A, McDowell, Dylan James, and Stafford, Shane. Mon . "Bison Robustness and Performance Improvements". United States. doi:10.2172/1530814. https://www.osti.gov/servlets/purl/1530814.
@article{osti_1530814,
title = {Bison Robustness and Performance Improvements},
author = {Schwen, Daniel and Spencer, Benjamin W and Pitts, Stephanie A and McDowell, Dylan James and Stafford, Shane},
abstractNote = {In last year’s Bison robustness report, the authors demonstrated that improving the Jacobian matrix can drastically increase solution convergence rates. The development of an automatic differentiation (AD) capability in the MOOSE framework has created the possibility of having exact Jacobian matrices for all of our material models. A large focus in this year’s report is the application of AD to MOOSE physics modules, such as the tensor mechanics module, and the application to Bison fuel and cladding material models. A second main focus in this year’s report is the investigation of a set of algorithmic solver changes to improve the robustness and performance of Bison LWR simulations. Beyond providing increased solver robustness, the ability to solve strongly coupled multiphysics problems, and solving large deformation problems with ease, these automatically generated perfect Jacobians also drastically reduce development time for new models. In models converted to AD, Jacobian computation made up the majority of the code base. With this code removed, both readability and maintainability of the MOOSE/Bison code base are improved. The work documented here significantly improved the robustness of the Bison LWR assessment case suite. Through algorithmic changes in solver infrastructure, such as the application of predictors and improvements in timestepping, tangible improvements in performance were observed. These improvements are documented using a new standard set of metrics that are collected in all assessment cases, and a suite of statistical analysis tools developed for this report.},
doi = {10.2172/1530814},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {7}
}

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