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2026 Annual Conference
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.
Yongping Wang, Yunzhao Li, Tengfei Zhang, E. E. Lewis, M. A. Smith, W. S. Yang, Hongchun Wu
Nuclear Science and Engineering | Volume 193 | Number 6 | June 2019 | Pages 652-662
Technical Note | doi.org/10.1080/00295639.2018.1542883
Articles are hosted by Taylor and Francis Online.
The Generalized Partitioned Matrix (GPM) acceleration method for the Variational Nodal Method (VNM) with diffusion approximation is presented. In the GPM method, the vectors of expansion coefficients of the scalar flux, source, and partial currents are divided into low-order and high-order terms. For each outer iteration, the low-order terms of the flux, fission source, and partial currents are first solved with fixed higher-order terms from the preceding outer iteration, and then a full matrix sweep through the energy groups is performed to update the full set of expansion coefficients. The GPM method increases the CPU time per outer iteration but reduces the overall computational time significantly by reducing the number of outer iterations required for convergence. The GPM acceleration method has been implemented in the NODAL code, and its performance was compared with that of the traditional Partitioned Matrix (PM) acceleration scheme for four problems: two- and three-dimensional C5G7 problems, a NuScale modular core problem, and a large pressurized water reactor problem. The numerical results show that the PM acceleration consistently reduces the computational time by a factor of 2.0 and the GPM acceleration yields two to three times higher speedup than with PM acceleration by reducing the number of outer iterations. The GPM speedups over the unaccelerated VNM range between 4.3 and 6.3. Moreover, the speedup ratio achieved with the GPM acceleration increases with an increasing dominance ratio of the problem since the required number of outer iterations increases with an increasing dominance ratio.
