Simulation and design of an IPHI-based neutron source, first steps toward SONATE

Author:

Mom Borana1,Thulliez Loïc1,Dumonteil Éric1,Binois Mickaël2,Richet Yann3,Schwindling Jérôme1,Drouart Antoine1

Affiliation:

1. IRFU, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France

2. INRIA, Université Côte d’Azur, F-06902 Sophia Antipolis, France

3. LNR, IRSN, F-92260 Fontenay-aux-Roses, France

Abstract

Facilities providing bright thermal neutron beams are of primary importance for various research topics. At CEA-Saclay, a compact accelerator driven neutron source, SONATE, is investigated in taking advantage of the IPHI accelerator able to deliver a 3 MeV proton beam with an intensity up to 100 mA. To optimize the performances of such a neutron source, it is necessary to maximize the thermal neutron flux while minimizing the contribution of other particles. In this work, optimization has been performed using the Monte Carlo code TOUCANS, a neutron transport code based on Geant4 developed at CEA-Saclay. This latter has been coupled to PROMETHEE, a software allowing multi-objective optimization for many simulation software. In this work the Kriging metamodel based approach is used to optimize a neutron beamdump. To take into account the various constraints, noise on the detection system and radiation protection issues, several beamdump configurations are evaluated. The variation of beamdump parameters makes it possible to identify the set of optimal solutions, the Pareto front. It allows to focus on the set of best choices and to choose wisely the best configurations. After describing the validation of TOUCANS on experimental tests performed from 2016 to 2022, the capability of such an approach will be presented.

Publisher

IOS Press

Subject

Nuclear Energy and Engineering,Nuclear and High Energy Physics

Reference19 articles.

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2. jMetalPy: A Python framework for multi-objective optimization with metaheuristics

3. GPareto: An R package for Gaussian-process-based multi-objective optimization and analysis;Binois;Journal of Statistical Software,2019

4. M. Binois and V. Picheny, GPareto: Gaussian Processes for Pareto Front Estimation and Optimization, 2019, R package version 1.1.6.

5. TRIPOLI-4®, CEA, EDF and AREVA reference Monte Carlo code;Brun;Annals of Nuclear Energy,2015

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. TOUCANS: A versatile Monte Carlo neutron transport code based on GEANT4;Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment;2023-06

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