EPJ Web Conf.
Volume 153, 2017ICRS-13 & RPSD-2016, 13th International Conference on Radiation Shielding & 19th Topical Meeting of the Radiation Protection and Shielding Division of the American Nuclear Society - 2016
|Number of page(s)||7|
|Section||6. Calculation Methods Monte Carlo & Deterministic|
|Published online||25 September 2017|
AREVA Developments for an Efficient and Reliable use of Monte Carlo codes for Radiation Transport Applications
1 AREVA NP Inc, Solomon Pond Park, Marlborough, MA 01752
2 AREVA NP, 10-12 Rue Juliette Recamier, 69456 LYON CEDEX 06
3 AREVA, 1 Place Jean MILLIER, 92084 PARIS LA DEFENSE CEDEX
* Corresponding author: firstname.lastname@example.org
Published online: 25 September 2017
In the context of the rising of Monte Carlo transport calculations for any kind of application, AREVA recently improved its suite of engineering tools in order to produce efficient Monte Carlo workflow. Monte Carlo codes, such as MCNP or TRIPOLI, are recognized as reference codes to deal with a large range of radiation transport problems. However the inherent drawbacks of theses codes - laboring input file creation and long computation time - contrast with the maturity of the treatment of the physical phenomena. The goals of the recent AREVA developments were to reach similar efficiency as other mature engineering sciences such as finite elements analyses (e.g. structural or fluid dynamics). Among the main objectives, the creation of a graphical user interface offering CAD tools for geometry creation and other graphical features dedicated to the radiation field (source definition, tally definition) has been reached. The computations times are drastically reduced compared to few years ago thanks to the use of massive parallel runs, and above all, the implementation of hybrid variance reduction technics. From now engineering teams are capable to deliver much more prompt support to any nuclear projects dealing with reactors or fuel cycle facilities from conceptual phase to decommissioning.
© The Authors, published by EDP Sciences, 2017
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