Application of ADVANTG to the JET3 – NEXP streaming benchmark experiment

B. Kos, S. W. Mosher, I. A. Kodeli, R. E. Grove, J. Naish, B. Obryk, R. Villari, P. Batistoni

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

An application of the ADVANTG (AutomateD VAriaNce reducTion Generator) code, which combines the Denovo deterministic transport solver with the MCNP (Monte Carlo N-Particle) Monte Carlo code, to the JET3-NEXP (Joint European Torus work package 3) streaming benchmark experiment is presented in this paper. An ADVANTG input parameter variation analysis was performed in order to find optimal input parameters for the hybrid two-step workflow. ADVANTG-accelerated calculations from three different institutions (JSI (Jožef Stefan Institute), ORNL (Oak Ridge National Laboratory), and CCFE (Culham Centre for Fusion Energy)) were compared to analog MCNP simulations confirming no bias is introduced due to the use of ADVANTG. Additionally, ADVANTG-accelerated MCNP numerical simulations of the neutron fluence were compared to experimental results performed in 2016 at JET using thermo-luminescence detectors. Calculation/Experiment values from 0.7 to 13 were calculated for the experimental positions in the SW (southwest) labyrinth and SE (southeast) chimney. Using ADVANTG-generated variance reduction parameters, speed-up factors of up to 1100 relative to analog calculations were achieved. The MCNP statistical tests on track length estimator volume averaged tallies were consistently passed for all experimental locations with ADVANTG-generated variance reduction parameters. These results demonstrate that ADVANTG is capable of accelerating tally convergence at locations far from the source in streaming-dominated transport simulations of complex fusion facility models.

Original languageEnglish
Article number111252
JournalFusion Engineering and Design
Volume147
DOIs
StatePublished - Oct 2019

Funding

This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training program 2014–2018 and 2019-2020 under grant agreement No 633053 . The views and opinions expressed herein do not necessarily reflect those of the European Commission. The authors also acknowledge the financial support from the Slovenian Research Agency (research core funding No. P2-0073 and project for training young researchers No. 1000-15-0106 ).

Keywords

  • ADVANTG
  • JET
  • MCNP
  • NEXP
  • Variance reduction

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