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Going Nuclear: Notes from the officially unofficial book tour
I work in the analytical labs at one of Europe’s oldest and largest nuclear sites: Sellafield, in northwestern England. I spend my days at the fume hood front, pipette in one hand and radiation probe in the other (and dosimeter pinned to my chest, of course). Outside the lab, I have a second job: I moonlight as a writer and public speaker. My new popular science book—Going Nuclear: How the Atom Will Save the World—came out last summer, and it feels like my life has been running at full power ever since.
Marianna Papadionysiou, Kim Seongchan, Mathieu Hursin, Alexander Vasiliev, Hakim Ferroukhi, Andreas Pautz, Han Gyu Joo
Nuclear Science and Engineering | Volume 194 | Number 11 | November 2020 | Pages 1056-1066
Technical Paper | doi.org/10.1080/00295639.2020.1753418
Articles are hosted by Taylor and Francis Online.
The current standard for computational neutronic analysis of nuclear power plants (NPPs) is the so-called conventional approach, which relies on few-group, coarse-mesh diffusion calculations. The recent evolution of computing clusters and computational techniques gives the opportunity to use codes that perform first principles–based multiphysics simulations, allowing high resolution of the calculated parameters. The goal of this work is to assess the performance of the deterministic high-resolution transport code nTRACER and the nodal code PARCS on the basis of VVER core configurations. The V1000-2D benchmarks of the NUclear REactor SIMulation (NURESIM) project framework are used to provide the neutronic and modeling data as well as reference solutions for both codes. A reference solution is also generated using Serpent2. The accuracy and limitations of the codes are illustrated together with their computational requirements. PARCS shows good agreement with the reference solutions although the results present some discrepancies due to the provided discontinuity factors. nTRACER is capable of producing high-accuracy and high-resolution solutions in a fraction of the time required by the Monte Carlo solver.