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Two steps forward for U.K. advanced nuclear
This week, two significant announcements have emerged from the United Kingdom’s advanced reactor sector.
On June 14, Rolls-Royce, the United Kingdom National Nuclear Laboratory, and the Japan Atomic Energy Agency announced that they had signed two trilateral memorandums of cooperation to collaborate on “advanced modular reactor (AMR) technology, specifically high-temperature gas-cooled reactors (HTGR), and the coated particle fuel these reactors will use.”
Separately, on June 16, Bellevue, Wash.–based TerraPower announced that its Natrium reactor design has been formally submitted for U.K. regulatory review. The company also announced the formation of a new subsidiary, TerraPower UK Ltd.
R. J. Sheu, A. Y. Chen, Y.-W. H. Liu, S. H. Jiang
Nuclear Science and Engineering | Volume 159 | Number 1 | May 2008 | Pages 23-36
Technical Paper | doi.org/10.13182/NSE159-23
Articles are hosted by Taylor and Francis Online.
In this study, discrete ordinates and Monte Carlo methods were applied to solve the radiation transport problem for a simplified spent fuel storage cask considering fixed neutron and gamma-ray sources. The results were compared, and the causes for their differences were investigated. In addition, a hybrid method based on the Consistent Adjoint Driven Importance Sampling (CADIS) methodology has been adopted to accelerate the Monte Carlo simulations. CADIS utilizes a deterministic adjoint function for variance reduction through source biasing and consistent transport biasing. The problem encountered and its possible solution for applying the source biasing in such a large volume source are described. Compared with the unbiased case, the computational efficiency is improved by a factor of several tens for neutron transport, and the efficiency is increased tremendously by about five orders of magnitude for gamma-ray transport. It has been demonstrated that the biasing scheme applied here is very effective in the shielding calculations for a spent fuel storage cask using the Monte Carlo method.