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Final report on LDRD project : coupling strategies for multi-physics applications

Hopkins, Matthew M.; Pawlowski, Roger P.; Moffat, Harry K.; Carnes, Brian C.; Hooper, Russell H.

Many current and future modeling applications at Sandia including ASC milestones will critically depend on the simultaneous solution of vastly different physical phenomena. Issues due to code coupling are often not addressed, understood, or even recognized. The objectives of the LDRD has been both in theory and in code development. We will show that we have provided a fundamental analysis of coupling, i.e., when strong coupling vs. a successive substitution strategy is needed. We have enabled the implementation of tighter coupling strategies through additions to the NOX and Sierra code suites to make coupling strategies available now. We have leveraged existing functionality to do this. Specifically, we have built into NOX the capability to handle fully coupled simulations from multiple codes, and we have also built into NOX the capability to handle Jacobi Free Newton Krylov simulations that link multiple applications. We show how this capability may be accessed from within the Sierra Framework as well as from outside of Sierra. The critical impact from this LDRD is that we have shown how and have delivered strategies for enabling strong Newton-based coupling while respecting the modularity of existing codes. This will facilitate the use of these codes in a coupled manner to solve multi-physic applications.

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Solution-verified reliability analysis and design of bistable MEMS using error estimation and adaptivity

Adams, Brian M.; Wittwer, Jonathan W.; Bichon, Barron J.; Carnes, Brian C.; Copps, Kevin D.; Eldred, Michael S.; Hopkins, Matthew M.; Neckels, David C.; Notz, Patrick N.; Subia, Samuel R.

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.

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Results 26–43 of 43
Results 26–43 of 43