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May 31–June 3, 2026
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My Story: John L. Swanson—ANS member since 1978
. . . and in 2019, on his 90th birthday.
Swanson in 1951, the year of his college graduation . . .
My pre-college years were spent in a rural suburb of Tacoma, Wash. In 1947, I enrolled in Reed College, a small liberal arts school in Portland, Ore.; I majored in chemistry and graduated in 1951. While at Reed, I met and married a young lady with whom I would raise 3 children and spend the next 68 years of my life—almost all of them in Richland, Wash., where I still live.
I was fortunate to have a job each of my “college summers” that provided enough money to cover my college costs for the next year; I don’t think that is possible these days. My job was in the kitchen/dining hall of a salmon cannery in Alaska. Room and board were provided and the cannery was in an isolated location, so I could save almost every dollar of my salary.
Ben C. Yee, Brendan Kochunas, Edward W. Larsen
Nuclear Science and Engineering | Volume 193 | Number 7 | July 2019 | Pages 722-745
Technical Paper | doi.org/10.1080/00295639.2018.1562777
Articles are hosted by Taylor and Francis Online.
The Multilevel in Space and Energy Diffusion (MSED) method accelerates the iterative convergence of multigroup diffusion eigenvalue problems by performing work on lower-order equations with only one group and/or coarser spatial grids. It consists of two primary components: (1) a grey (one-group) diffusion eigenvalue problem that is solved via Wielandt-shifted power iteration (PI) and (2) a multigrid-in-space linear solver. In previous work, the efficiency of MSED was verified using Fourier analysis and numerical results from a one-dimensional multigroup diffusion code. Since that work, MSED has been implemented as a solver for the coarse-mesh finite difference (CMFD) system in the three-dimensional Michigan Parallel Characteristics Transport (MPACT) code. In this paper, the results from the implementation of MSED in MPACT are presented, and the changes needed to make MSED more suitable for MPACT are described. For problems without feedback, the results in this paper show that MSED can reduce the CMFD run time by an order of magnitude and the overall run time by a factor of 2 to 3 compared to the default CMFD solver in MPACT [PI with the generalized minimal residual (GMRES) method]. For problems with feedback, the convergence of the outer Picard iteration scheme is worsened by the well-converged CMFD solutions produced by the standard MSED method. To overcome this unintuitive deficiency, MSED may be run with looser convergence criteria (a modified version of the MSED method called MSED-L) to circumvent the issue until the multiphysics iteration in MPACT is improved. Results show that MSED-L can reduce the CMFD run time in MPACT by an order of magnitude, without negatively impacting the outer Picard iteration scheme.
