using_efficient_global_optimization_algorithm_to_assist_nuclear_criticality_safety_assessment_submitted_to_nuclear_science_and_engineering [Promethee]

Using Efficient Global Optimization Algorithm to assist Nuclear Criticality Safety Assessment (submitted to Nuclear Science and Engineering)

by Y. Richet, G. Caplin, J. Crevel, D. Ginsbourger, V. Picheny


Nuclear criticality safety assessment often requires group-wise Monte Carlo simulations of k-effective in order to check sub-criticality of the system of interest. A typical task to be performed by safety assessors is hence to find the worse combination of input parameters of the criticality Monte Carlo code (i.e. leading to maximum reactivity) over the whole operating range. Then, checking sub-criticality can be done by solving a maximization problem where the input-output map defined by the Monte Carlo code stands for the objective function, or “parametric” model. This straightforward view of criticality parametric calculations complies with recent works in Design of Computer Experiments, an active research field in applied stochastics. This framework provides a robust support to enhance and consolidate good practices in criticality safety assessment. Indeed, supplementing the standard “expert driven” assessment by an optimization algorithm may be helpful to increase the reliability of the whole process, and the robustness of its conclusions. Such a new safety practice is intended to rely on both well-suited theoretical tools (compliant optimization algorithms) and computing infrastructure (a flexible grid computing environment). This paper presents an efficient solution to this two-sided theoretical and technical challenge.


Criticality, Monte Carlo, Design of Experiments, Optimization, Kriging ++
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