Optimization of out-vessel magnetic diagnostics for plasma boundary reconstruction in tokamaks

Romero, Jesús; Svensson, J.

doi:10.1088/0029-5515/53/3/033009

Cited by 9 publications

(8 citation statements)

References 24 publications

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“…A detailed study has been performed for optimization of magnetic probe arrays inside and outside the vacuum vessel. It was found that these two magnetic probe arrays are essential for equilibrium reconstruction during a plasma current ramp as currents are induced in the vacuum vessel [17].…”

Section: Introductionmentioning

confidence: 99%

Improvements for real-time magnetic equilibrium reconstruction on ASDEX Upgrade

Giannone

Fischer

McCarthy

et al. 2015

Fusion Engineering and Design

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Real-time magnetic equilibria are needed for NTM stabilization and disruption avoidance experiments on ASDEX Upgrade. Five improvements to real-time magnetic equilibrium reconstruction on ASDEX upgrade have been investigated. The aim is to include as many features of the offline magnetic equilibrium reconstruction code in the real-time equilibrium reconstruction code. Firstly, spline current density basis functions with regularization are used in the offline equilibrium reconstruction code, CLISTE [1]. It is now possible to have the same number of spline basis functions in the real-time code. Secondly, in the presence of edge localized modes, (ELM's), it is found to be necessary to include the low pass filter effect of the vacuum vessel on the fast position control coil currents to correctly compensate the magnetic probes for current oscillations in these coils. Thirdly, the introduction of ferromagnetic tiles in ASDEX Upgrade means that a real-time algorithm for including the perturbations of the magnetic equilibrium generated by these tiles is required. A methodology based on tile surface currents is described. Fourthly, during current ramps it was seen that the difference between fitted and measured magnetic measurements in the equilibrium reconstruction were larger than in the constant current phase. External loop voltage measurements and magnetic probe pairs inside and outside the vessel wall were used to measure the vacuum vessel wall resistivity. This is the first step towards including vacuum vessel currents during the plasma current ramp in the real-time equilibrium reconstruction. Fifthly, the introduction of a constraint of the safety factor on the magnetic axis is found to be a helpful method to improve the prediction of the location of rational surfaces for NTM stabilization and disruption avoidance experiments. Soft X-ray tomography is used to assess the quality of the real-time magnetic equilibrium reconstruction using this internal constraint.

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Section: Introductionmentioning

confidence: 99%

Improvements for real-time magnetic equilibrium reconstruction on ASDEX Upgrade

Giannone

Fischer

McCarthy

et al. 2015

Fusion Engineering and Design

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“…δ ij is the Kronecker delta function. We note that Gaussian processes have been introduced to the fusion community with a non-parametric tomography for the JET FIR interferometer data [5] and used for current tomography [6,7,43,44], beam emission spectroscopy [10,11], soft x-ray spectroscopy [12], x-ray imaging crystal spectroscopy [14] and profile regressions [32]. In this work, Gaussian processes are used to model electron temperature and density profiles to constrain their smoothness (gradient scale length) without imposing any specific profile shapes or assumptions such as monotonicity.…”

Section: Gaussian Process Priormentioning

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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“…(18), and N pixel is the total number of CCD pixels for the i th channel.Σ is an N pixel × N pixel square diagonal matrix containing variances of the measured signal at each pixel of the CCD camera as iň…”

Section: Interference Filter and Instrument Functionsmentioning

confidence: 99%

Bayesian electron density inference from JET lithium beam emission spectra using Gaussian processes

et al. 2017

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Abstract. A Bayesian model to infer edge electron density profiles is developed for the JET lithium beam emission spectroscopy (Li-BES) system, measuring Li I (2p-2s) line radiation using 26 channels with ∼ 1 cm spatial resolution and 10 ∼ 20 ms temporal resolution. The density profile is modelled using a Gaussian process prior, and the uncertainty of the density profile is calculated by a Markov Chain Monte Carlo (MCMC) scheme. From the spectra measured by the transmission grating spectrometer, the Li I line intensities are extracted, and modelled as a function of the plasma density by a multi-state model which describes the relevant processes between neutral lithium beam atoms and plasma particles. The spectral model fully takes into account interference filter and instrument effects, that are separately estimated, again using Gaussian processes. The line intensities are inferred based on a spectral model consistent with the measured spectra within their uncertainties, which includes photon statistics and electronic noise. Our newly developed method to infer JET edge electron density profiles has the following advantages in comparison to the conventional method: i) providing full posterior distributions of edge density profiles, including their associated uncertainties, ii) the available radial range for density profiles is increased to the full observation range (∼ 26 cm), iii) an assumption of monotonic electron density profile is not necessary, iv) the absolute calibration factor of the diagnostic system is automatically estimated overcoming the limitation of the conventional technique and allowing us to infer the electron density profiles for all pulses without preprocessing the data or an additional boundary condition, and v) since the full spectrum is modelled, the procedure of modulating the beam to measure the background signal is only necessary for the case of overlapping of the Li I line with impurity lines.

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Optimization of out-vessel magnetic diagnostics for plasma boundary reconstruction in tokamaks

Cited by 9 publications

References 24 publications

Improvements for real-time magnetic equilibrium reconstruction on ASDEX Upgrade

Improvements for real-time magnetic equilibrium reconstruction on ASDEX Upgrade

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

Bayesian electron density inference from JET lithium beam emission spectra using Gaussian processes

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