Turbulent Transport in Tokamak Plasmas: bridging theory and experiment

In fusion devices such as tokamaks, the achievement of good energy confinement is a key issue. The energy, particle and angular momentum transport is dominated by turbulent mechanims. To understand, model and predict temperature, density and rotation is existing and future tokamaks, a numerical tool, bridging theory and experiments, is introduced. .Nonlinear gyrokinetic codes allow for detailed understanding of turbulent transport. However, their computational demand precludes their use for predictive profile modelling. An alternative approach is required to bridge the gap between theoretical understanding and prediction of experiments. A quasi-linear gyrokinetic model, QuaLiKiz, allows for a 1 million speedup while retaining key physics. Indeed, in the tokamak plasma core, relatively low levels of turbulence are reported, typically below 10%. It is further shown that the nonlinear phase shift is close to the linear phase shift and that the frequency broadening observed in nonlinear simulations typically follows the linear growth rate. Therefore quasilinear gyrokinetic turbulent transport can be used to efficiently model fluxes in integrated modelling platforms. The saturated potential is constructed based on nonlinear simulation results and turbulence measurements. The predicted particle, heat and angular momentum fluxes have been compared successfully to nonlinear fluxes in a wide range of parameters using the quasilinear gyrokinetic code QuaLiKiz. In terms of CPU time, a factor one million is gained compared with nonlinear modelling. This allows for extensive interpretative and predictive applications.The simplified model also stimulates the development of theoretical understanding, since its construction relies on a deep understanding of the nonlinear physical mechanisms. Such work is hence at the cross-roads between experimental observations and detailed theoretical investigations.The quasilinear fluxes are compared to experimental observations at a given time. In particular, experimental observations of trace Nickel transport in the Tore Supra tokamak are successfully compared to the quasilinear predictions. Finally the model transport quantities (heat, particles and angular momentum) are used in a time evolving platform to predict temperature, density and rotation profiles. Predicted temperature and density profiles are successfully compared to experiments carried out on the JET tokamak. The successes and limits of the quasilinear approach are reviewed. Perspectives are given in the discussion section

Quasilinear gyrokinetic theory: A derivation of QuaLiKiz

In order to predict and analyze turbulent transport in tokamaks, it is important to model transport that arises from microinstabilities. For this task, quasilinear codes have been developed that seek to calculate particle, angular momentum, and heat fluxes both quickly and accurately. In this tutorial, we present a derivation of one such code known as QuaLiKiz, a quasilinear gyrokinetic transport code. The goal of this derivation is to provide a self-contained and complete description of the underlying physics and mathematics of QuaLiKiz from first principles. This work serves both as a comprehensive overview of QuaLiKiz specifically as well as an illustration for deriving quasilinear models in general.Comment: 52 page

Fast modeling of turbulent transport in fusion plasmas using neural networks

We present an ultrafast neural network (NN) model, QLKNN, which predicts core tokamak transport heat and particle fluxes. QLKNN is a surrogate model based on a database of 300 million flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. The database covers a wide range of realistic tokamak core parameters. Physical features such as the existence of a critical gradient for the onset of turbulent transport were integrated into the neural network training methodology. We have coupled QLKNN to the tokamak modelling framework JINTRAC and rapid control-oriented tokamak transport solver RAPTOR. The coupled frameworks are demonstrated and validated through application to three JET shots covering a representative spread of H-mode operating space, predicting turbulent transport of energy and particles in the plasma core. JINTRAC-QLKNN and RAPTOR-QLKNN are able to accurately reproduce JINTRAC-QuaLiKiz T i,e and n e profiles, but 3 to 5 orders of magnitude faster. Simulations which take hours are reduced down to only a few tens of seconds. The discrepancy in the final source-driven predicted profiles between QLKNN and QuaLiKiz is on the order 1%-15%. Also the dynamic behaviour was well captured by QLKNN, with differences of only 4%-10% compared to JINTRAC-QuaLiKiz observed at mid-radius, for a study of density buildup following the L-H transition. Deployment of neural network surrogate models in multi-physics integrated tokamak modelling is a promising route towards enabling accurate and fast tokamak scenario optimization, Uncertainty Quantification, and control applications.Comment: 18 pages, 11 figures, Physics of Plasmas, ICDDPS 2019 conference pape

Turbulent particle transport in magnetized fusion plasma

International audienceUnderstanding the mechanisms responsible for particle transport is of the utmost importance for magnetized fusion plasmas. A peaked density profile is attractive to improve the fusion rate, which is proportional to the square of the density, and to self-generate a large fraction of non-inductive current required for continuous operation.Experiments in various tokamak devices (ASDEX Upgrade, DIII-D, JET, TCV, TEXT, TFTR) indicate the existence of a turbulent particle pinch. Recently, such a turbulent pinch has been unambiguously identified in Tore Supra very long discharges, in the absence of both collisional particle pinch and central particle source, for more than 4 min (Hoang et al 2003 Phys. Rev. Lett. 90 155002). This turbulent pinch is predicted by a quasilinear theory of particle transport (Weiland J et al 1989 Nucl. Fusion 29 1810), and confirmed by non-linear turbulence simulations (Garbet et al 2003 Phys. Rev. Lett. 91 035001) and general considerations based on the conservation of motion invariants (Baker et al 2004 Phys. Plasmas 11 992). Experimentally, the particle pinch is found to be sensitive to the magnetic field gradient in many cases (Hoang et al 2004 Phys. Rev. Lett. 93 135003, Zabolotsky et al 2003 Plasma Phys. Control. Fusion 45 735, Weisen et al 2004 Plasma Phys. Control. Fusion 46 751, Baker et al 2000 Nucl. Fusion 40 1003), to the temperature profile (Hoang et al 2004 Phys. Rev. Lett. 93 135003, Angioni et al 2004 Nucl. Fusion 44 827) and also to the collisionality that changes the nature of the microturbulence (Angioni et al 2003 Phys. Rev. Lett. 90 205003, Garzotti et al 2003 Nucl. Fusion 43 1829, Weisen et al 2004 31st EPS Conf. on Plasma Phys. (London) vol 28G (ECA) P-1.146, Lopes Cardozo N J 1995 Plasma Phys. Control. Fusion 37 799). The consistency of some of the observed dependences with the theoretical predictions gives us a clearer understanding of the particle pinch in tokamaks, allowing us to predict more accurately the density profile in ITER

Understanding the near edge physics In L mode, H mode, ELM-free regimes Recent progress in validation and core/edge integration Role of particle source

International audienceFrom Today’s experiments to ITER and beyond, 2 huge gaps:■ What our US colleagues call the Integrated Tokamak Exhaust and Performance gap (most of sessions Today, Wed. and Thursday)■ Burning plasma physics: P$_\alpha$>P$_{aux}$ impact on nonlinear turb/MHD interplay (session Friday)TTF being a workshop, the goal here is to trigger stimulating discussions for our future works.Not aiming at an (impossible) exhaustive review. This talk would have been much better if done after the workshop ;

ANALYSE DE STABILITE DE PLASMAS DE TOKAMAK

GRENOBLE1-BU Sciences (384212103) / SudocSudocFranceF

Turbulent transport driven by kinetic ballooning modes in the inner core of JET hybrid H-modes

International audienceTurbulent transport in the inner core of the high-β JET hybrid discharge 75225 is investigated extensively through linear and non-linear gyrokinetic simulations using the gyro-kinetic code GKW in the local approximation limit. Compared to previous studies [J. Citrin et al. 2015 Plasma Phys. Control. Fusion 57 014032, J. Garcia et al. 2015 Nucl. Fusion 55 053007], the analysis has been extended towards the magnetic axis, ρ < 0.3, where the turbulence characteristics remain an open question. Understanding turbulent transport in this region is crucial to predict core profile peaking that in turn will impact the fusion reactions and the tungsten neoclassical transport, in present devices as well as in ITER. At ρ = 0.15, a linear stability analysis indicates that Kinetic Ballooning Modes (KBMs) dominate, with an extended mode structure in ballooning space due to the low magnetic shear. The sensitivity of KBM stability to main plasma parameters is investigated. In the non-linear regime, the turbulence induced by these KBMs drives a significant ion and electron heat flux. Standard quasi-linear models are compared to the non-linear results. The standard reduced quasi-linear models work well for the E × B fluxes, but fail to capture magnetic flutter contribution to the electron heat flux induced by the non-linear excitation of low k θ ρ i micro-tearing modes that are linearly stable. An extension of the quasi-linear models is proposed allowing better capturing the magnetic flutter flux

Core radiative collapse in WEST LHCD plasmas: characterization and integrated modelling

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Systematic analysis of turbulence : component extraction of the density fluctuations and study of their dynamics for different regimes

International audienceDatadriven approach database digging for systematic analysis of experiments trend, universalityTwo approaches to reduce the dimensionality of density fluctuation measurements database•Parametrization the reflectometry frequency spectrum is reduced 3 components [Sun 18 19•Component extraction by combining signal processing with machine learning [Salazar 22 23Bothrely on the decomposition of the frequency spectrum in component