Modeling of the SPERT transients using Serpent 2 with time-dependent capabilities

Levinsky, Alex; Valtavirta, Ville; Adams, F.P.; Anghel, V.

doi:10.1016/j.anucene.2018.09.038

Cited by 15 publications

(1 citation statement)

References 19 publications

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“…These Monte Carlo codes are coupled with the SCF thermal-hydraulic solver, thus leading to the coupled codes: dynMCNP/SCF, dynTRIPOLI/SCF, dyn-SERPENT/SCF. The code extensions and modifications are described in more detail in [14,19,20]. The coupling schemes must be appropriate for massive HPC-simulations.…”

Section: Time-dependence Capabilities In Monte Carlo Methodsmentioning

confidence: 99%

Advanced numerical simulation and modelling for reactor safety − contributions from the CORTEX, HPMC, McSAFE and NURESAFE projects

Demazière

Sánchez-Espinoza

Chanaron

2020

EPJ Nuclear Sci. Technol.

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Predictive modelling capabilities have long represented one of the pillars of reactor safety. In this paper, an account of some projects funded by the European Commission within the seventh Framework Program (HPMC and NURESAFE projects) and Horizon 2020 Program (CORTEX and McSAFE) is given. Such projects aim at, among others, developing improved solution strategies for the modelling of neutronics, thermal-hydraulics, and/or thermo-mechanics during normal operation, reactor transients and/or situations involving stationary perturbations. Although the different projects have different focus areas, they all capitalize on the most recent advancements in deterministic and probabilistic neutron transport, as well as in DNS, LES, CFD and macroscopic thermal-hydraulics modelling. The goal of the simulation strategies is to model complex multi-physics and multi-scale phenomena specific to nuclear reactors. The use of machine learning combined with such advanced simulation tools is also demonstrated to be capable of providing useful information for the detection of anomalies during operation.

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Section: Time-dependence Capabilities In Monte Carlo Methodsmentioning

confidence: 99%