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Washington, D.C.|Washington Hilton
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Nuclear Science and Engineering
Fusion Science and Technology
National Museum of Nuclear Science and History explores “atomic” culture
For many of us, the toys of our childhood leave indelible marks on our consciousness, affecting our long-term perceptions and attitudes about certain things. Hot Wheels may inspire a lifelong fascination with fast, flashy automobiles, while Barbies might shape ideas about beauty and self-image. For the generation who grew up during the Atomic Age—the post–World War II era from roughly the mid-1940s to the early 1960s—the toys, games, and entertainment of their childhoods might have included things like atomic pistols, atomic trains, rings with tiny amounts of radioactive elements, and comic books, puzzles, and music about nuclear weapons.
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.