EPJ Nuclear Sci. Technol.
Volume 5, 2019
|Number of page(s)||32|
|Published online||28 February 2019|
The Uranie platform: an open-source software for optimisation, meta-modelling and uncertainty analysis
Den-Service de thermo-hydraulique et de mécanique des fluides (STMF), CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
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Received in final form: 27 November 2018
Accepted: 6 December 2018
Published online: 28 February 2019
The high-performance computing resources and the constant improvement of both numerical simulation accuracy and the experimental measurements with which they are confronted bring a new compulsory step to strengthen the credence given to the simulation results: uncertainty quantification. This can have different meanings, according to the requested goals (rank uncertainty sources, reduce them, estimate precisely a critical threshold or an optimal working point), and it could request mathematical methods with greater or lesser complexity. This paper introduces the Uranie platform, an open-source framework developed at the Alternative Energies and Atomic Energy Commission (CEA), in the nuclear energy division, in order to deal with uncertainty propagation, surrogate models, optimisation issues, code calibration, etc. This platform benefits from both its dependencies and from personal developments, to offer an efficient data handling model, a C++ and Python interface, advanced graphi graphical tools, several parallelisation solutions, etc. These methods can then be applied to many kinds of code (considered as black boxes by Uranie) so to many fields of physics as well. In this paper, the example of thermal exchange between a plate-sheet and a fluid is introduced to show how Uranie can be used to perform a large range of analysis.
© J.-B. Blanchard et al., published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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