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Remembering Joseph M. Hendrie
Joseph M. Hendrie
To those of us who knew Joe, even prior to his appointment as chair of the Nuclear Regulatory Commission, it is an understatement to say that he was a larger-than-life member of the nuclear science and technology enterprise. He was best known to the broader community for two major accomplishments: the design and construction of the High Flux Beam Reactor (HFBR) at Brookhaven National Laboratory and the creation of the standard review plan (SRP) for the U.S. Atomic Energy Commission.
In addition to the products of these endeavors becoming major fundaments to their respective communities, they were uniquely Joe. The safety analysis report for the HFBR was written essentially single-handedly by him. This was true of the SRP as well, which became the key safety review document for the NRC as it performed safety reviews for the growing number of power reactor applications in the United States. His deep technical knowledge of nuclear engineering and his extraordinary management skills made this possible.
Jeremy A. Roberts, Leidong Xu, Rabab Elzohery, Mohammad Abdo
Nuclear Science and Engineering | Volume 193 | Number 12 | December 2019 | Pages 1371-1378
Technical Paper | doi.org/10.1080/00295639.2019.1634928
Articles are hosted by Taylor and Francis Online.
An algorithm based on dynamic mode decomposition (DMD) for acceleration of the power method (PM) is presented. The PM is a simple technique for determining the dominant eigenmode of an operator A, and variants of the PM are widely used in reactor analysis. DMD is an algorithm for decomposing a time series of spatially dependent data and producing an explicit-in-time reconstruction for that data. By viewing successive PM iterates as snapshots of a time-varying system tending toward a steady state, DMD can be used to predict that steady state using (sometimes surprisingly small) iterates. The process of generating snapshots with the PM and extrapolating forward with DMD can be repeated. The resulting restarted, DMD-accelerated PM [or DMD-PM()] was applied to the two-dimensional International Atomic Energy Agency diffusion benchmark and compared to the unaccelerated PM and the Arnoldi method. Results indicate that DMD-PM() can reduce the number of power iterations required by a factor of approximately 5. However, the Arnoldi method always outperformed DMD-PM() for an equivalent number of matrix-vector products Av. In other words, DMD-PM() cannot compete with leading eigensolvers if one is not limited to snapshots produced by the PM. Contrarily, DMD-PM() can be readily applied as a postprocess to existing PM applications for which the Arnoldi method and similar methods are not directly applicable. A slight variation of the method was also found to produce reasonable approximations to the first and second harmonics without substantially affecting convergence of the dominant mode.