Impurity seeding in ITER DT plasmas in a carbon-free environment

Pacher, H.D.; Kukushkin, A.S.; Pacher, G.W.; Kotov, V.; Pitts, R.A.; Reiter, D.

doi:10.1016/j.jnucmat.2014.11.104

Cited by 81 publications

(105 citation statements)

References 7 publications

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“…with e = 1.6 10 −19 C . This relation was obtained by fitting SOLPS data 28,29 . The factor 2.2 was applied to take into account other heat sources such as radiative flux.…”

Section: Resultsmentioning

confidence: 99%

Parametric study of hydrogenic inventory in the ITER divertor based on machine learning

Hodille

Mougenot

Temmerman

et al. 2020

Sci Rep

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A parametric study is performed with the 2D FESTIM code for the ITER monoblock geometry. The influence of the monoblock surface temperature, the incident ion energy and particle flux on the monoblock hydrogen inventory is investigated. The simulated data is analysed with a Gaussian regression process and an inventory map as a function of ion energy and incident flux is given. Using this inventory map, the hydrogen inventory in the divertor is easily derived for any type of scenario. Here, the case of a detached ITER scenario with inputs from the SOLPS code is presented. For this scenario, the hydrogen inventory per monoblock is highly dependent of surface temperature and ranges from $$10^{18}$$ 10 18 to $$6 \times 10^{19}$$ 6 × 10 19 H after a $$10^{7}$$ 10 7 s exposure. The inventory evolves as a power law of time and is lower at strike points where the surface temperature is high. Hydrogen inventory in the whole divertor after a $$10^{7}$$ 10 7 s exposure is estimated at approximately 8 g.

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“…with e = 1.6 10 −19 C . This relation was obtained by fitting SOLPS data 28,29 . The factor 2.2 was applied to take into account other heat sources such as radiative flux.…”

Section: Resultsmentioning

confidence: 99%

Parametric study of hydrogenic inventory in the ITER divertor based on machine learning

Hodille

Mougenot

Temmerman

et al. 2020

Sci Rep

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“…Borrass et al 1997a;Krasheninnikov et al 1999). If the engineering analysis is in the focus, then the variables that can be controlled in a tokamak are preferablesuch as the power input to the SOL, the neutral pressure in the divertor, the helium production rate and the concentration of seeded impurity for the ITER studies (Pacher et al 2015). When the calculations are used to fit the transport coefficients in order to reproduce the experimental measurements, then these variables are the parameters best measured in the experiment, such as the density and temperature profiles upstream and at the targets, the bolometer signals, the saturation currents on the Langmuir probes and other diagnostic signals.…”

Section: Input Parameters For 2-d Modellingmentioning

confidence: 99%

“…Today these conclusions are widely accepted by edge plasma community and both impurity radiation and plasma recombination effects are incorporated in all existing 2-D edge plasma codes and, in particular, were used in assessment of the performance of the ITER divertor design (e.g. Pacher et al 2015).…”

Section: Modelling Of High-recycling and Detached Divertor Regimesmentioning

confidence: 99%

Physics of ultimate detachment of a tokamak divertor plasma

Krasheninnikov¹,

2017

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The basic physics of the processes playing the most important role in divertor plasma detachment is reviewed. The models used in the two-dimensional edge plasma transport codes that are widely used to address different issues of the edge plasma physics and to simulate the experimental data, as well as the numerical schemes and convergence issues, are described. The processes leading to ultimate divertor plasma detachment, the transition to and the stability of the detached regime, as well as the impact of the magnetic configuration and divertor geometry on detachment, are considered. A consistent, integral physical picture of ultimate detachment of a tokamak divertor plasma is developed.

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“…It is noteworthy that plasma parameters affecting the particle transport cannot be prescribed arbitrarily and controlled fully independently. In particular, boundary conditions and gas penetration through separatrix to the core depend on the fluxes of particles and power to the SOL [3,21]. Core fuelling is integrated with ELM pacing, divertor detachment, and ion cyclotron heating (ICH) coupling control as well as gas pumping [4].…”

Section: Impact Of the Integrated Plasma Performance On The Particle mentioning

confidence: 99%

On benchmarking of simulations of particle transport in ITER

Koechl

Polevoi

et al. 2019

Nucl. Fusion

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We report results of benchmarking of core particle transport simulations by a collection of codes widely used in transport modelling of tokamak plasmas. Our analysis includes formulation of transport equations, difference between electron and ion solvers, comparison of modules of the 2 pellet and edge gas fuelling on the ITER baseline scenario. During the first phase of benchmarking we address the particle transport effects in the stationary phase. Firstly, simulations are performed with identical sources, sinks, transport coefficients, and boundary conditions prescribed in the flattop H-mode phase. The transformation of ion particle transport equations is introduced so to directly compare their results to electron transport solvers. Secondly, the pellet fuelling models are benchmarked in various conditions to evaluate the dependency of the pellet deposition on the pellet volume, injection side, pedestal and separatrix parameters. Thirdly, edge gas fuelling is benchmarked to assess sensitivities of source profile predictions to uncertainties in plasma conditions and detailed model assumptions. At the second phase, we address particle transport effects in the time-evolving plasma including the current ramp-up to the ramp-down phase. The ion and the electron solvers are benchmarked together. Differences between the simulation results of the solvers are investigated in terms of equilibrium, grid resolution, radial coordinate, radial grid distribution, and plasma volume evolution term. We found that the selection of the radial coordinate can yield prominent differences between the solvers mainly due to differences in the edge grid distribution. The simulations reveal that electron and ion solvers predict noticeably different density peaking for the same diffusion and pinch velocity while with the peaked profile of helium, expected in fusion reactors. The fuelling benchmarking shows that gas puffing is not efficient for core fuelling in H-modes and density control should be done by the high field side pellet injection in contrast to present machines. mode in the current flattop phase depends sensitively on the particle balance of the mixed D-T fuels, He and impurities. In ITER, the neutral beam injection (NBI) does not play a noticeable role neither in the global particle balance [7], nor for the central fuelling. Moreover, the SOLPS modelling [7] predicts dramatic reduction of the gas penetrated from the edge, making the pellet injection the main tool for the density control in the H-mode plasmas, though the gas penetrated from the edge still can play the dominant role for the L-mode operation. Features like the recycling and penetration of He and the fuel into the core plasma are central to understanding the dilution and tritium burnup. The SOL/divertor plasma and its interactions with plasma facing components will set the boundary conditions for the core transport. Eventually, the particle transport alters the heat and the momentum transport so all these non-linear connections need to be understood simultaneously t...

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Impurity seeding in ITER DT plasmas in a carbon-free environment

Cited by 81 publications

References 7 publications

Parametric study of hydrogenic inventory in the ITER divertor based on machine learning

Parametric study of hydrogenic inventory in the ITER divertor based on machine learning

Physics of ultimate detachment of a tokamak divertor plasma

On benchmarking of simulations of particle transport in ITER

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