Experimental validation of advanced regulations for superconducting magnet cooling undergoing periodic heat loads

Lagier, B.; Rousset, Bernard; Hoa, C.; Bonnay, Patrick

doi:10.1063/1.4860898

Cited by 4 publications

(1 citation statement)

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“…As a consequence, the smoothing of the heat load from the SC magnets to the cryoplant is being investigated [3,4,5], also in consideration of the fact that the SC magnet system and related cryogenics are responsible for 25-30% of the total cost of the plant. A dedicated experimental facility (HELIOS) was realized at CEA Grenoble (France) [6,7] and different strategies are under consideration, both from the experimental [8,9] and the computational [10,11,12,13] points of view.…”

Section: Introductionmentioning

confidence: 99%

Design and optimization of Artificial Neural Networks for the modelling of superconducting magnets operation in tokamak fusion reactors

Froio

Bonifetto

Carli

et al. 2016

Journal of Computational Physics

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In superconducting tokamaks, the cryoplant provides the helium needed to cool different clients, among which by far the most important one is the superconducting magnet system. The evaluation of the transient heat load from the magnets to the cryoplant is fundamental for the design of the latter and the assessment of suitable strategies to smooth the heat load pulses, induced by the intrinsically pulsed plasma scenarios characteristic of today's tokamaks, is crucial for both suitable sizing and stable operation of the cryoplant. For that evaluation, accurate but expensive system-level models, as implemented in e.g. the validated state-of-the-art 4C code, were developed in the past, including both the magnets and the respective external cryogenic cooling circuits. Here we show how these models can be successfully substituted with cheaper ones, where the magnets are described by suitably trained Artificial Neural Networks (ANNs) for the evaluation of the heat load to the cryoplant. First, two simplified thermal-hydraulic models for an ITER Toroidal Field (TF) magnet and for the ITER Central Solenoid (CS) are developed, based on ANNs, and a detailed analysis of the chosen networks' topology and parameters is presented and discussed. The ANNs are then inserted into the 4C model of the ITER TF and CS cooling circuits, which also includes active controls to achieve a smoothing of the variation of the heat load to the cryoplant. The training of the ANNs is achieved using the results of full 4C simulations (including detailed models of the magnets) for conventional sigmoid-like waveforms of the drivers and the predictive capabilities of the ANN-based models in the case of actual ITER operating scenarios are demonstrated by comparison with the results of full 4C runs, both with and without active smoothing, in terms of both accuracy and computational time. Exploiting the low computational effort requested by the ANN-based models, a demonstrative optimization study has been finally carried out, with the aim of choosing among different smoothing strategy for the standard ITER plasma operation.

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