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Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Remembering ANS member Gil Brown
Brown
The nuclear community is mourning the loss of Gilbert Brown, who passed away on July 11 at the age of 77 following a battle with cancer.
Brown, an American Nuclear Society Fellow and an ANS member for nearly 50 years, joined the faculty at Lowell Technological Institute—now the University of Massachusetts–Lowell—in 1973 and remained there for the rest of his career. He eventually became director of the UMass Lowell nuclear engineering program. After his retirement, he remained an emeritus professor at the university.
Sukesh Aghara, chair of the Nuclear Engineering Department Heads Organization, noted in an email to NEDHO members and others that “Gil was a relentless advocate for nuclear energy and a deeply respected member of our professional community. He was also a kind and generous friend—and one of the reasons I ended up at UMass Lowell. He served the university with great dedication. . . . Within NEDHO, Gil was a steady presence and served for many years as our treasurer. His contributions to nuclear engineering education and to this community will be dearly missed.”
Johan Cufe, Daniele Tomatis
Nuclear Science and Engineering | Volume 199 | Number 1 | April 2025 | Pages S730-S743
Research Article | doi.org/10.1080/00295639.2024.2392927
Articles are hosted by Taylor and Francis Online.
The Ronen method (RM) has been successfully applied to obtain highly accurate approximations to the solution of the neutron transport equation in one-dimensional (1D) homogeneous and heterogeneous configurations, considering both isotropic and linearly anisotropic problems. Anderson acceleration (AA)–based algorithms have recently been applied the RM iterative scheme to improve its convergence rate. Specifically, an improved version of the AA, the damped Anderson acceleration with restarts and epsilon monotonicity (DAAREM), has been implemented and employed during RM iterations. AA works on Krylov subspaces built with the residuals from successive iterations. DAAREM makes use of a restart and an optimized regularization parameter to guess the target solution by extrapolation. This kind of acceleration is crucial to finding the fixed-point solution throughout the nonlinear RM iterations and avoids the issue of slow convergence.
This work provides a detailed description of the DAAREM implementation in the RM. A full comparison of the convergence performances between nonaccelerated RM, standard AA, and DAAREM applied to RM iterations is presented for a 1D full-core benchmark. DAAREM is also improved in this work by ensuring the monotonicity of its control parameters, thus achieving higher performance. A significant reduction in the number of iterations in achieving the flux distribution within the target tolerance is always obtained for the model problems considered.
