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NRC looks to leverage previous approvals for large LWRs
During this time of resurging interest in nuclear power, many conversations have centered on one fundamental problem: Electricity is needed now, but nuclear projects (in recent decades) have taken many years to get permitted and built.
In the past few years, a bevy of new strategies have been pursued to fix this problem. Workforce programs that seek to laterally transition skilled people from other industries, plans to reuse the transmission infrastructure at shuttered coal sites, efforts to restart plants like Palisades or Duane Arnold, new reactor designs that build on the legacy of research done in the early days of atomic power—all of these plans share a common throughline: leveraging work already done instead of starting over from square one to get new plants designed and built.
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.