Upgrade of the JET far infrared interferometer diagnostic

Boboc, A.; Gil, C.; Pastor, P.; Spuig, P.; Edlington, T.; Dorling, S.; Contributors, Jet‐Efda

doi:10.1063/1.4737420

Cited by 25 publications

(21 citation statements)

References 5 publications

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“…Figure28. Radiation powers of divertor, core (inside a roughly estimated pedestal top position, ρ pol < 0.95) and pedestal (between pedestal and separatrix, ρ pol < 0.95) as function of Γ Ne .…”

mentioning

confidence: 99%

Characterisation of highly radiating neon seeded plasmas in JET-ILW

et al. 2019

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To study the impact of strong impurity radiation on the energy confinement and the discharge stability, at JET-ILW dedicated high-density, highly heated experiments with neon seeding have been performed. In these experiments an increase of radiation in the core and especially the pedestal region and a characteristic strong X-point radiator inside the confined region have been observed. The increased radiation inside the separatrix had no impact on the energy confinement. Only at the highest neon puff rates and highest heating powers (29 MW) a weak H-mode without back-transitions to L-mode (M-mode) could be achieved, while at lower heating powers or seeding levels L-modes and oscillations between H-mode and L-mode could be observed. In H-mode, at highest neon seeding levels the targets are in complete detachment. A degradation of the pedestal density profile was observed when neon was seeded. A correlation between this density pedestal degradation, the appearance of an X-point radiator and the detachment of the targets was found in neon seeded H-mode pulses.In ASTRA-TGLF core transport simulations it could be shown that the increase of the electron temperature in the core with neon seeding is attributed to a partial stabilisation of ITG modes.

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confidence: 99%

Characterisation of highly radiating neon seeded plasmas in JET-ILW

et al. 2019

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“…The JET far-infrared (FIR) interferometerpolarimeter system [42,43,44] launches electromagnetic waves into the plasma and measures the phase differences and polarisation angles between these injected waves and the reference wave. These phase differences and polarisation angles are proportional to the line integrated quantities n e d and n e B d , respectively, along four lateral and four vertical lines of sight (the yellow lines in Figure 3).…”

Section: The Interferometers and Polarimetersmentioning

confidence: 99%

Overview of the JET preparation for deuterium–tritium operation with the ITER like-wall

et al. 2019

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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.

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“…The JET far infrared (FIR) interferometer [23,24,25,26] diagnostic measures line integrated electron densities along the eight lines of sight (four vertical and four lateral directions as shown in Figure 3) with an approximately 10 −200 µs temporal resolution (depending on the channels). The 1 st and 4 th channels measure the line integrated densities near the first wall, constraining the electron density at the edge, and the other lines of sight cover the plasma core.…”

Section: Jet Far Infrared (Fir) Interferometer Systemmentioning

confidence: 99%

“…It is well known that electron density measurements with Thomson scattering systems require information on the calibration factors of the system [22]. The FIR interferometer diagnostic system provides line integrated electron densities, i.e., no spatial resolution, without such calibration factors [23,24,25,26]. Bayesian analysis for Thomson scattering [9,27,28], interferometer [5,29] systems and joint , the parameters we wish to determine, and observations are shown as red and blue circles, respectively.…”

Section: Introductionmentioning

confidence: 99%

Bayesian modelling of Thomson scattering and multichannel interferometer diagnostics using Gaussian processes

et al. 2020

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Electron temperature and density profiles consistent with JET high resolution Thomson scattering (HRTS) and far infrared (FIR) interferometer data are inferred by a Bayesian joint model using Gaussian processes. Forward models predicting diagnostic data including instrument effects such as optics and electronics are developed independently for both diagnostic systems in the Minerva framework, and combined as one joint model. The full joint posterior distribution of the electron temperature and density profiles, the hyperparameters of the Gaussian processes and calibration factor is explored by Markov chain Monte Carlo (MCMC) sampling. The posterior distribution of the electron density(temperature) profile is obtained by marginalising all the possible combinations of the electron temperature(density) profile, the hyperparameters and calibration factor. Therefore, the method removes profile dependency on the hyperparameters completely in addition to eliminating oftenused avoidable constraints such as monotonicity and parametrisation on the profiles. The posterior distribution of the calibration factor is also calculated explaining both the HRTS and the FIR interferometer data simultaneously. Thus, absolute electron density can be obtained from the HRTS without additional experiments measuring the calibration factor.

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Upgrade of the JET far infrared interferometer diagnostic

Cited by 25 publications

References 5 publications

Characterisation of highly radiating neon seeded plasmas in JET-ILW

Characterisation of highly radiating neon seeded plasmas in JET-ILW

Overview of the JET preparation for deuterium–tritium operation with the ITER like-wall

Bayesian modelling of Thomson scattering and multichannel interferometer diagnostics using Gaussian processes

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