Dynamic Simulation of Sub-Scale ITER CS/STR Cooling Loop

Maekawa, R.; Oba, K.; Takami, S.; Iwamoto, A.; Chang, Hyuk; Forgeas, A.; Serio, L.; Vallocorba, R.; Rousset, Bernard; Hoa, C.; Monteiro, Lionel

doi:10.1109/tasc.2012.2184513

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…Process simulation for Tokamak operation has been conducted as a technical service contract between NIFS and ITER since 2011. The work scope was focused on the assessment of transient thermo-hydraulic behaviour of magnet system for the DT phase [1][2] [3]. The modeling has been completed after benchmarking against the numerical analyses [4] and validating with CS model coil experiment results [5].…”

Section: Introductionmentioning

confidence: 99%

Co-simulation of cool down process from 300 to 80 K for ITER Tokamak

Takami,

Iwamoto,

Maekawa

et al. 2024

IOP Conf. Ser.: Mater. Sci. Eng.

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Dynamic simulation of Tokamak superconducting magnet system has been conducted to investigate the cool-down process from 300 to 80 K. The simulation focuses on the cool-down speed variations with respect to the global temperature gradients in the different coils; Toroidal Field (TF) coil, TF STructure (TF-ST), Central Solenoid (CS) and Poloidal Field (PF)/Correction Coil (CC) systems. As imposing the maximum temperature gradients dT max ≤ 50K, the speed should be adjusted, ensuring the limited mechanical stresses due to thermal contractions. So far, the process simulation of Tokamak cryogenic system has been concentrated on the Deuterium Tritium (DT) operation phase; therefore, it is necessary to revise the model to extend its capability for the cool-down; for example, the thermo-hydraulic properties of Cable-in-Conduit Conductor (CICC) for each coil, mechanical properties of materials for the magnet system. The cool-down process is implemented at the helium refrigerator by utilizing LN2 heat exchanger up to 80 K. Its speed is set at -0.8 K/hr as a baseline, which can be controlled by the global temperature gradients in the magnet system. The process will be on hold as dT max ≥ 50K, and resumed once dT max < 50K. The paper discusses the cool-down processes of the Tokamak and identifies the impact on the speed, dT i/dt, of each component.

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Section: Introductionmentioning

confidence: 99%

Co-simulation of cool down process from 300 to 80 K for ITER Tokamak

Takami,

Iwamoto,

Maekawa

et al. 2024

IOP Conf. Ser.: Mater. Sci. Eng.

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“…As an alternative, fast models have been developed to predict the heat load to the cryoplant, based on the simplification of the set of partial differential equations (PDEs) describing the physics of the system [15,16,17,18].…”

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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“…On this track, different strategies are being developed both through experiments, e.g. in the HELIOS facility [3][4] at CEA Grenoble, France, and through dedicated analysis [5][6][7][8][9], performed with different computational tools. The 4C code [10], developed for and dedicated to the detailed simulation of the magnet thermal-hydraulic transients during, for instance, plasma operating scenarios [11][12], can be adopted to test the measure of success of different control strategies.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network (ANN) modeling of the pulsed heat load during ITER CS magnet operation

et al. 2014

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Artificial Neural Networks (ANNs) are applied to the development of a simplified transient model of the ITER Central Solenoid (CS), aiming at predicting the evolution of the pulsed heat load from the CS to the LHe bath during plasma operation. The ANNs are trained using the thermal-hydraulic evolution in the CS, computed with the 4C code, due to AC losses,. The capability of the ANN model to predict the heat load to the LHe bath is successfully demonstrated in the case of different transients, among which a nominal plasma operating scenario. The gain in speed of the simplified model with respect to the 4C code results is by order of magnitudes, with a small loss of accuracy.

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Dynamic Simulation of Sub-Scale ITER CS/STR Cooling Loop

Cited by 7 publications

References 4 publications

Co-simulation of cool down process from 300 to 80 K for ITER Tokamak

Co-simulation of cool down process from 300 to 80 K for ITER Tokamak

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

Artificial Neural Network (ANN) modeling of the pulsed heat load during ITER CS magnet operation

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