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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.
Ruixian Fang, Dan G. Cacuci (Univ of South Carolina)
Proceedings | 2018 International Congress on Advances in Nuclear Power Plants (ICAPP 2018) | Charlotte, NC, April 8-11, 2018 | Pages 451-459
The “predictive modeling for coupled multi-physics systems (PM_CMPS)” methodology is applied in this work to the numerical simulation model of the mechanical draft cooling tower (MDCT) located in the F-area at Savannah River National Laboratory (SRNL) in order to improve the predictions of this model by combining computational information with measurements of outlet air humidity, outlet air and outlet water temperatures. At the outlet of this cooling tower, where measurements of the quantities of interest are available, the PM_CMPS reduces the predicted uncertainties for these quantities to values that are smaller than either the computed or the measured uncertainties. The PM_CMPS has also been applied to reduce the uncertainties for quantities of interest inside the tower’s fill section, where no direct measurements are available. The maximum reductions of uncertainties occur at the locations where direct measurements are available. At other locations, the predicted response uncertainties are reduced by the PM_CMPS methodology to values that are smaller than the modeling uncertainties arising from the imprecisely known model parameters.