Publications

Lin, Paul L.; Shadid, John N.; Hu, Jonathan J.; Cyr, Eric C.; Pawlowski, Roger P.; Prokopenko, Andrey V.

Abstract not provided.

Demeshko, Irina D.; Edwards, Harold C.; Heroux, Michael A.; Salinger, Andrew G.; Pawlowski, Roger P.; Phipps, Eric T.

Abstract not provided.

Cyr, Eric C.; Bettencourt, Matthew T.; Demeshko, Irina D.; Pawlowski, Roger P.

Abstract not provided.

Mathematics and Computations, Supercomputing in Nuclear Applications and Monte Carlo International Conference, M and C+SNA+MC 2015

Pawlowski, Roger P.; Clarno, K.; Montgomery, R.; Salko, R.; Evans, T.; Turner, J.; Gaston, D.

The Tiamat code is being developed by CASL (Consortium for Advanced Simulation of LWRs) as an integrated tool for predicting pellet-clad interaction and improving the high-fidelity core simulator. Tiamat is a large-scale parallel code that couples the the multi-dimensional Bison-CASL fuel performance code on every fuel rod with the COBRA-TF (CTF) sub-channel thermal-hydraulics code and either the Insilico or MPACT neutronics codes. Tiamat solves a transient problem where each time step is subcycled using Picard iteration to converge the fully-coupled nonlinear system. This report discusses the solution algorithms and software design of the simulator. Results are shown for a five assembly cross and compared against a separate core simulator developed by CASL. Tiamat has demonstrated that it can compute quantities of interest for analyzing pellet-clad interaction.

Cyr, Eric C.; Shadid, John N.; Phillips, Edward G.; Tuminaro, Raymond S.; Pawlowski, Roger P.; Lin, Paul L.; Chacon, Luis C.

Abstract not provided.

Pawlowski, Roger P.

Abstract not provided.

Demeshko, Irina D.; Edwards, Harold C.; Heroux, Michael A.; Phipps, Eric T.; Salinger, Andrew G.; Pawlowski, Roger P.

Abstract not provided.

Demeshko, Irina D.; Edwards, Harold C.; Heroux, Michael A.; Pawlowski, Roger P.; Phipps, Eric T.; Salinger, Andrew G.; Trott, Christian R.

Abstract not provided.

Pawlowski, Roger P.

Abstract not provided.

Pawlowski, Roger P.

Abstract not provided.

Keiter, Eric R.; Warrender, Christina E.; Mei, Ting M.; Russo, Thomas V.; Schiek, Richard S.; Thornquist, Heidi K.; Verley, Jason V.; Coffey, Todd S.; Pawlowski, Roger P.

This manual describes the use of the Xyce Parallel Electronic Simulator. Xyce has been designed as a SPICE-compatible, high-performance analog circuit simulator, and has been written to support the simulation needs of the Sandia National Laboratories electrical designers. This development has focused on improving capability over the current state-of-the-art in the following areas: Capability to solve extremely large circuit problems by supporting large-scale parallel computing platforms (up to thousands of processors). This includes support for most popular parallel and serial computers. A differential-algebraic-equation (DAE) formulation, which better isolates the device model package from solver algorithms. This allows one to develop new types of analysis without requiring the implementation of analysis-specific device models. Device models that are specifically tailored to meet Sandias needs, including some radiationaware devices (for Sandia users only). Object-oriented code design and implementation using modern coding practices. Xyce is a parallel code in the most general sense of the phrase a message passing parallel implementation which allows it to run efficiently a wide range of computing platforms. These include serial, shared-memory and distributed-memory parallel platforms. Attention has been paid to the specific nature of circuit-simulation problems to ensure that optimal parallel efficiency is achieved as the number of processors grows.

Keiter, Eric R.; Mei, Ting M.; Russo, Thomas V.; Pawlowski, Roger P.; Schiek, Richard S.; Coffey, Todd S.; Thornquist, Heidi K.; Verley, Jason V.; Warrender, Christina E.

This document is a reference guide to the Xyce Parallel Electronic Simulator, and is a companion document to the Xyce Users Guide [1] . The focus of this document is (to the extent possible) exhaustively list device parameters, solver options, parser options, and other usage details of Xyce. This document is not intended to be a tutorial. Users who are new to circuit simulation are better served by the Xyce Users Guide [1] .

SIAM Journal on Scientific Computing

Phillips, Edward G.; Elman, Howard C.; Cyr, Eric C.; Shadid, John N.; Pawlowski, Roger P.

The magnetohydrodynamics (MHD) equations are used to model the flow of electrically conducting fluids in such applications as liquid metals and plasmas. This system of nonself-adjoint, nonlinear PDEs couples the Navier-Stokes equations for fluids and Maxwell's equations for electromagnetics. There has been recent interest in fully coupled solvers for the MHD system because they allow for fast steady-state solutions that do not require pseudo-time-stepping. When the fully coupled system is discretized, the strong coupling can make the resulting algebraic systems difficult to solve, requiring effective preconditioning of iterative methods for efficiency. In this work, we consider a finite element discretization of an exact penalty formulation for the stationary MHD equations posed in two-dimensional domains. This formulation has the benefit of implicitly enforcing the divergence-free condition on the magnetic field without requiring a Lagrange multiplier. We consider extending block preconditioning techniques developed for the Navier-Stokes equations to the full MHD system. We analyze operators arising in block decompositions from a continuous perspective and apply arguments based on the existence of approximate commutators to develop new preconditioners that account for the physical coupling. This results in a family of parameterized block preconditioners for both Picard and Newton linearizations. We develop an automated method for choosing the relevant parameters and demonstrate the robustness of these preconditioners for a range of the physical nondimensional parameters and with respect to mesh refinement.

Shadid, John N.; Pawlowski, Roger P.; Cyr, Eric C.; Wildey, Timothy M.

This report describes work directed towards completion of the Thermal Hydraulics Methods (THM) CFD Level 3 Milestone THM.CFD.P7.05 for the Consortium for Advanced Simulation of Light Water Reactors (CASL) Nuclear Hub effort. The focus of this milestone was to demonstrate the thermal hydraulics and adjoint based error estimation and parameter sensitivity capabilities in the CFD code called Drekar::CFD. This milestone builds upon the capabilities demonstrated in three earlier milestones; THM.CFD.P4.02 [12], completed March, 31, 2012, THM.CFD.P5.01 [15] completed June 30, 2012 and THM.CFD.P5.01 [11] completed on October 31, 2012.

ACM Transaction on Mathematical Software

Salinger, Andrew G.; Ostien, Jakob O.; Pawlowski, Roger P.; Phipps, Eric T.; Sun, WaiChing S.; Gao, Xujiao G.; Hansen, Glen H.; Kalashnikova, Irina; Mota, Alejandro M.; Muller, Richard P.; Nielsen, Erik N.

Abstract not provided.

Shadid, John N.; Pawlowski, Roger P.; Cyr, Eric C.; Wildey, Timothy M.; Weber, Paula D.

Abstract not provided.

Russo, Thomas V.; Mei, Ting M.; Keiter, Eric R.; Schiek, Richard S.; Thornquist, Heidi K.; Verley, Jason V.; Coffey, Todd S.; Pawlowski, Roger P.; Warrender, Christina E.

This manual describes the use of the Xyce Parallel Electronic Simulator. Xyce has been designed as a SPICE-compatible, high-performance analog circuit simulator, and has been written to support the simulation needs of the Sandia National Laboratories electrical designers. This development has focused on improving capability over the current state-of-the-art in the following areas: Capability to solve extremely large circuit problems by supporting large-scale parallel computing platforms (up to thousands of processors). This includes support for most popular parallel and serial computers. A differential-algebraic-equation (DAE) formulation, which better isolates the device model package from solver algorithms. This allows one to develop new types of analysis without requiring the implementation of analysis-specific device models. Device models that are specifically tailored to meet Sandias needs, including some radiationaware devices (for Sandia users only). Object-oriented code design and implementation using modern coding practices. Xyce is a parallel code in the most general sense of the phrase a message passing parallel implementation which allows it to run efficiently a wide range of computing platforms. These include serial, shared-memory and distributed-memory parallel platforms. Attention has been paid to the specific nature of circuit-simulation problems to ensure that optimal parallel efficiency is achieved as the number of processors grows.

Keiter, Eric R.; Mei, Ting M.; Russo, Thomas V.; Pawlowski, Roger P.; Schiek, Richard S.; Coffey, Todd S.; Thornquist, Heidi K.; Verley, Jason V.; Warrender, Christina E.

This document is a reference guide to the Xyce Parallel Electronic Simulator, and is a companion document to the Xyce Users Guide [1] . The focus of this document is (to the extent possible) exhaustively list device parameters, solver options, parser options, and other usage details of Xyce. This document is not intended to be a tutorial. Users who are new to circuit simulation are better served by the Xyce Users Guide [1] .

Pawlowski, Roger P.

Abstract not provided.

Pawlowski, Roger P.

Abstract not provided.

Shadid, John N.; Tuminaro, Raymond S.; Pawlowski, Roger P.

Abstract not provided.

Shadid, John N.; Wildey, Timothy M.; Pawlowski, Roger P.

Abstract not provided.

Pawlowski, Roger P.; Cyr, Eric C.; Shadid, John N.

Abstract not provided.

Pawlowski, Roger P.; Cyr, Eric C.; Shadid, John N.; Tuminaro, Raymond S.; Lin, Paul L.

Abstract not provided.

Cyr, Eric C.; Shadid, John N.; Pawlowski, Roger P.

This document summarizes the results from a level 3 milestone study within the CASL VUQ effort. We compare the adjoint-based a posteriori error estimation approach with a recent variant of a data-centric verification technique. We provide a brief overview of each technique and then we discuss their relative advantages and disadvantages. We use Drekar::CFD to produce numerical results for steady-state Navier Stokes and SARANS approximations. 3