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Article Dans Une Revue Nuclear Engineering and Design Année : 2021

Sequential Design of Mixture Experiments with an Empirically Determined Input Domain and an Application to Burn-up Credit Penalization of Nuclear Fuel Rods

Résumé

This paper presents methodologies for solving a common nuclear engineering problem using a suitable mathematical framework. Besides its potential for more general applications, this abstract formalization of the problem provides an improved robustness to the solution compared to the empirical treatment used in industrial practice of today. The essence of the paper proposes a sequential design for a stochastic simulator experiment to maximize a computer output y(x). The complications present in applications of interest are (1) the input x is an element of an unknown subset of a positive hyperplane and (2) y(x) is measured with error. The training data for this problem are a collection of historical inputs x corresponding to runs of a physical system that is linked to the simulator and the associated y(x). Two methods are provided for estimating the input domain. An extension of the well-known efficient global optimization (EGO) algorithm is presented to solve the optimization problem. An example of application of the method is given in which patterns of the "combustion rate" of fissile spent fuel rods are determined to maximize the computed k-effective taken to be the "criticality coefficient".
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Dates et versions

hal-02535987 , version 1 (07-04-2020)

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Paternité - Pas d'utilisation commerciale - Pas de modification

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François Bachoc, Théo Barthe, Thomas Santner, Yann Richet. Sequential Design of Mixture Experiments with an Empirically Determined Input Domain and an Application to Burn-up Credit Penalization of Nuclear Fuel Rods. Nuclear Engineering and Design, 2021, 374, pp.111034. ⟨10.1016/j.nucengdes.2020.111034⟩. ⟨hal-02535987⟩
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