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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.
Utility Working Conference and Vendor Technology Expo (UWC 2022)
August 7–10, 2022
Marco Island, FL|JW Marriott Marco Island
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DOE tags Los Alamos to lead $9.25 million molten salt computational research program
The Department of Energy has chosen Los Alamos National Laboratory to lead a $9.25 million collaborative project to model the behavior and properties of structural materials in molten salt through the Scientific Discovery through Advanced Computing (SciDAC) program and announced the news August 9. The team working on the five-year project includes experts from LANL; Carnegie Mellon University; and Idaho, Lawrence Berkeley, and Sandia national laboratories.
Sung Hoon Choi, Hyung Jin Shim, Chang Hyo Kim
Nuclear Science and Engineering | Volume 189 | Number 2 | February 2018 | Pages 171-187
Technical Paper | dx.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.