Stabilized FE simulation of prototype thermalhydraulics problems with integrated adjointbased capabilities
Abstract
A critical aspect of applying modern computational solution methods to complex multiphysics systems of relevance to nuclear reactor modeling, is the assessment of the predictive capability of specific proposed mathematical models. The understanding of numerical error, the sensitivity of the solution to parameters associated with input data, boundary condition uncertainty, and mathematical models is critical. Additionally, the ability to evaluate and or approximate the model efficiently, to allow development of a reasonable level of statistical diagnostics of the mathematical model and the physical system, is of central importance. In our study we report on initial efforts to apply integrated adjointbased computational analysis and automatic differentiation tools to begin to address these issues. The study is carried out in the context of a Reynolds averaged Navier–Stokes approximation to turbulent fluid flow and heat transfer using a particular spatial discretization based on implicit fullycoupled stabilized FE methods. We present the initial results that show the promise of these computational techniques in the context of nuclear reactor relevant prototype thermalhydraulics problems.
 Authors:

 Sandia National Lab. (SNLNM), Albuquerque, NM (United States). Computational Mathematics Dept. and Dept. of Mathematics and Statistics
 Sandia National Lab. (SNLNM), Albuquerque, NM (United States). Multiphysics Applications Dept.
 Sandia National Lab. (SNLNM), Albuquerque, NM (United States). Computational Mathematics Dept.
 Sandia National Lab. (SNLNM), Albuquerque, NM (United States). Optimization and UQ Dept.
 Publication Date:
 Research Org.:
 Sandia National Lab. (SNLNM), Albuquerque, NM (United States)
 Sponsoring Org.:
 USDOE Office of Nuclear Energy (NE); USDOE Office of Science (SC)
 OSTI Identifier:
 1338379
 Alternate Identifier(s):
 OSTI ID: 1329336
 Report Number(s):
 SAND201612353J
Journal ID: ISSN 00219991; 649724
 Grant/Contract Number:
 AC0494AL85000; AC0500OR22725
 Resource Type:
 Accepted Manuscript
 Journal Name:
 Journal of Computational Physics
 Additional Journal Information:
 Journal Volume: 321; Journal Issue: C; Journal ID: ISSN 00219991
 Publisher:
 Elsevier
 Country of Publication:
 United States
 Language:
 English
 Subject:
 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; 97 MATHEMATICS AND COMPUTING; Reynolds averaged Navier–Stokes; Stabilized finite elements; Adjoints; Sensitivities; Errorestimation; Uncertainty quantification
Citation Formats
Shadid, J. N., Smith, T. M., Cyr, E. C., Wildey, T. M., and Pawlowski, R. P. Stabilized FE simulation of prototype thermalhydraulics problems with integrated adjointbased capabilities. United States: N. p., 2016.
Web. doi:10.1016/j.jcp.2016.04.062.
Shadid, J. N., Smith, T. M., Cyr, E. C., Wildey, T. M., & Pawlowski, R. P. Stabilized FE simulation of prototype thermalhydraulics problems with integrated adjointbased capabilities. United States. doi:10.1016/j.jcp.2016.04.062.
Shadid, J. N., Smith, T. M., Cyr, E. C., Wildey, T. M., and Pawlowski, R. P. Fri .
"Stabilized FE simulation of prototype thermalhydraulics problems with integrated adjointbased capabilities". United States. doi:10.1016/j.jcp.2016.04.062. https://www.osti.gov/servlets/purl/1338379.
@article{osti_1338379,
title = {Stabilized FE simulation of prototype thermalhydraulics problems with integrated adjointbased capabilities},
author = {Shadid, J. N. and Smith, T. M. and Cyr, E. C. and Wildey, T. M. and Pawlowski, R. P.},
abstractNote = {A critical aspect of applying modern computational solution methods to complex multiphysics systems of relevance to nuclear reactor modeling, is the assessment of the predictive capability of specific proposed mathematical models. The understanding of numerical error, the sensitivity of the solution to parameters associated with input data, boundary condition uncertainty, and mathematical models is critical. Additionally, the ability to evaluate and or approximate the model efficiently, to allow development of a reasonable level of statistical diagnostics of the mathematical model and the physical system, is of central importance. In our study we report on initial efforts to apply integrated adjointbased computational analysis and automatic differentiation tools to begin to address these issues. The study is carried out in the context of a Reynolds averaged Navier–Stokes approximation to turbulent fluid flow and heat transfer using a particular spatial discretization based on implicit fullycoupled stabilized FE methods. We present the initial results that show the promise of these computational techniques in the context of nuclear reactor relevant prototype thermalhydraulics problems.},
doi = {10.1016/j.jcp.2016.04.062},
journal = {Journal of Computational Physics},
number = C,
volume = 321,
place = {United States},
year = {2016},
month = {5}
}
Web of Science
Works referencing / citing this record:
Dimension reduction in magnetohydrodynamics power generation models: Dimensional analysis and active subspaces: GLAWS
journal, August 2017
 Glaws, Andrew; Constantine, Paul G.; Shadid, John N.
 Statistical Analysis and Data Mining: The ASA Data Science Journal, Vol. 10, Issue 5
Dimension reduction in magnetohydrodynamics power generation models: Dimensional analysis and active subspaces: GLAWS
journal, August 2017
 Glaws, Andrew; Constantine, Paul G.; Shadid, John N.
 Statistical Analysis and Data Mining: The ASA Data Science Journal, Vol. 10, Issue 5