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Operations & Power
Members focus on the dissemination of knowledge and information in the area of power reactors with particular application to the production of electric power and process heat. The division sponsors meetings on the coverage of applied nuclear science and engineering as related to power plants, non-power reactors, and other nuclear facilities. It encourages and assists with the dissemination of knowledge pertinent to the safe and efficient operation of nuclear facilities through professional staff development, information exchange, and supporting the generation of viable solutions to current issues.
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2024 ANS Winter Conference and Expo
November 17–21, 2024
Orlando, FL|Renaissance Orlando at SeaWorld
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Remembering ANS President John Kelly
John Kelly, ANS past president (2018–19 ), passed away peacefully in his sleep on October 3, 2024, in Gilbert Ariz., at the age of 70. Kelly was born on March 9, 1954, and was the eldest of Jack and Aileen Kelly’s six children.
His career, which spanned more than 40 years, began at Sandia National Laboratories in Albuquerque, N.M., where he focused on safety and severe accident analysis. His leadership led him to Washington D.C., where he served as the deputy assistant secretary for nuclear reactor technologies at the U.S. Department of Energy. Kelly played a critical role in shaping nuclear policy and guiding the world through significant events, including the Fukushima Daiichi accident in Japan. At the end of his career, he was honored to serve as the American Nuclear Society’s president. In retirement, he was actively involved with ANS in technology events and mentoring the next generation of scientists.
Kelly is survived by his wife, Suzanne; his children Julie Kelly-Smith (Byron), John A. (Sarah), and Michael (Nicole); and grandchildren Kiri and Kyson Smith and John and Maximilian Kelly. His family was his pride and joy, including his cherished dog, Covie, who brought him happiness in recent years.
In lieu of flowers, donations may be made to the American Nuclear Society or Detroit Catholic Central High School (27225 Wixom Road, Novi, MI 48374). Please designate Memorial and specify John Kelly ’72 Memorial Fund.
In honor of Kelly's commitment to ANS and to celebrate his life, his profile from the July 2018 issue of Nuclear News is published below.
Sung Hoon Choi, Hyung Jin Shim, Chang Hyo Kim
Nuclear Science and Engineering | Volume 189 | Number 2 | February 2018 | Pages 171-187
Technical Paper | doi.org/10.1080/00295639.2017.1388089
Articles are hosted by Taylor and Francis Online.
A generalized perturbation theory (GPT) formulation suited for the Monte Carlo (MC) eigenvalue calculations is newly developed to estimate sensitivities of a general MC tally to input data. In the new GPT formulation, the tally perturbation due to an input parameter change is expressed as a sum of the perturbed operator effect and the perturbed source effect requiring the generalized adjoint function weighting. It is shown that the new GPT formulation is equivalent to the conventional first-order differential operator sampling method augmented by the fission source perturbation method. Because the GPT formulation makes it necessary to compute the generalized adjoint function, MC sensitivity estimation algorithms can consume a huge computer memory space to save historywise estimates of tallies. As a way to alleviate the memory space problem, the MC Wielandt iteration method is incorporated into the MC GPT algorithm. For the purpose of comparison, MC GPT algorithms by both the standard power iteration and the Wielandt iteration methods are implemented in the Seoul National University MC code, McCARD. Their performances are examined in two-group homogeneous problems, Godiva and the TMI-1 pin cell problem. From the nuclear data sensitivity and uncertainty analyses of these problems, it is demonstrated that the new GPT methods can predict the sensitivities of reaction rate tallies to cross-section data very well. It is also demonstrated that the incorporation of the MC Wielandt iteration method into the new GPT calculations consumes a negligibly small amount of memory required for—and thus resolves—the computer memory issue associated with the adjoint function calculations.