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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.
Scott W. Mosher, Stephen C. Wilson
Fusion Science and Technology | Volume 74 | Number 4 | November 2018 | Pages 263-276
Technical Paper | doi.org/10.1080/15361055.2018.1496691
Articles are hosted by Taylor and Francis Online.
Neutronics analyses of the ITER experimental fusion reactor rely on increasingly complex geometry models and estimates of energy-dependent neutron flux and radiation dose-rate distributions generated at ever higher resolutions. There are significant practical challenges with applying the Monte Carlo N-Particle (MCNP) continuous-energy transport code to high-resolution analyses. For models consisting of more than 100 000 surfaces and cells, geometry initialization can take several hours, thus slowing down model integration and transport analysis efforts. In multithreaded simulations, the amount of memory consumed by superimposed mesh tally data increases in proportion to the number of threads. This behavior limits either the tally resolution or the number of processor cores that can be utilized in the simulation. This paper describes algorithmic improvements that were implemented in a modified version of MCNP5 to overcome these limitations. These improvements are referred to as the Oak Ridge National Laboratory Transformative Neutronics (ORNL-TN) upgrade. A comparison of the performance and memory usage of both MCNP5 and ORNL-TN on several relevant fusion neutronics models is presented. In these tests and in actual high-resolution neutronics analyses, ORNL-TN reduces geometry processing times from hours to a few seconds and increases in-memory mesh tally capacity from the order of 108 to 1010 space-energy bins.