https://doi.org/10.1051/epjconf/202124715008
UNCERTAINTY QUANTIFICATION IN STEADY STATE SIMULATIONS OF A MOLTEN SALT SYSTEM USING POLYNOMIAL CHAOS EXPANSION ANALYSIS
1 Politecnico di Torino, Dipartimento Energia Corso Duca degli Abruzzi 24, 10129 Torino, Italy
2 Delft University of Technology, Department of Radiation Science and Technology Mekelweg 15, 2629 JB Delft, The Netherlands
s251599@polito.it
M.Tiberga@tudelft.nl
Z.Perko@tudelft.nl
sandra.dulla@polito.it
D.Lathouwers@tudelft.nl
Corresponding author: M.Tiberga@tudelft.nl
Published online: 22 February 2021
Uncertainty Quantification (UQ) of numerical simulations is highly relevant in the study and design of complex systems. Among the various approaches available, Polynomial Chaos Expansion (PCE) analysis has recently attracted great interest. It belongs to nonintrusive spectral projection methods and consists of constructing system responses as polynomial functions of the stochastic inputs. The limited number of required model evaluations and the possibility to apply it to codes without any modification make this technique extremely attractive. In this work, we propose the use of PCE to perform UQ of complex, multi-physics models for liquid fueled reactors, addressing key design aspects of neutronics and thermal fluid dynamics. Our PCE approach uses Smolyak sparse grids designed to estimate the PCE coefficients. To test its potential, the PCE method was applied to a 2D problem representative of the Molten Salt Fast Reactor physics. An in-house multi-physics tool constitutes the reference model. The studied responses are the maximum temperature and the effective multiplication factor. Results, validated by comparison with the reference model on 103 Monte-Carlo sampled points, prove the effectiveness of our PCE approach in assessing uncertainties of complex coupled models.
Key words: Polynomial Chaos Expansion / Uncertainty quantification / Sensitivity analysis / Multi-physics / Molten salt reactor / Sparse grids / Non-intrusive
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
