EPJ Nuclear Sci. Technol.
Volume 7, 2021
A tribute to Massimo Salvatores' scientific work
|Number of page(s)||9|
|Published online||06 May 2021|
Nuclear data assimilation, scientific basis and current status
1 Institute for Radioprotection and Nuclear Safety, Fontenay-aux-Roses, France
2 Alternative Energies and Atomic Energy Commission, CEA DAM DIF, F-91297 Arpajon, France
3 The University of Tennessee, Knoxville, USA
* e-mail: email@example.com
Received in final form: 24 March 2021
Accepted: 9 April 2021
Published online: 6 May 2021
The use of Data Assimilation methodologies, known also as a data adjustment, liaises the results of theoretical and experimental studies improving an accuracy of simulation models and giving a confidence to designers and regulation bodies. From the mathematical point of view, it approaches an optimized fit to experimental data revealing unknown causes by known consequences that would be crucial for data calibration and validation. Data assimilation adds value in a ND evaluation process, adjusting nuclear data to particular application providing so-called optimized design-oriented library, calibrating nuclear data involving IEs since all theories and differential experiments provide the only relative values, and providing an evidence-based background for validation of Nuclear data libraries substantiating the UQ process. Similarly, it valorizes experimental data and the experiments, as such involving them in a scientific turnover extracting essential information inherently contained in legacy and newly set up experiments, and prioritizing dedicated basic experimental programs. Given that a number of popular algorithms, including deterministic like Generalized Linear Least Square methodology and stochastic ones like Backward and Hierarchic or Total Monte-Carlo, Hierarchic Monte-Carlo, etc., being different in terms of particular numerical formalism are, though, commonly grounded on the Bayesian theoretical basis. They demonstrated sufficient maturity, providing optimized design-oriented data libraries or evidence-based backgrounds for a science-driven validation of general-purpose libraries in a wide range of practical applications.
© E. Ivanov et al., Published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.