A classification scheme for edge-localized modes based on their probability distributions

We present here an automated classification scheme which is particularly well suited to scenarios where the parameters have significant uncertainties or are stochastic quantities. To this end, the parameters are modeled with probability distributions in a metric space and classification is conducted using the notion of nearest neighbors. The presented framework is then applied to the classification of type I and type III edge-localized modes (ELMs) from a set of carbon-wall plasmas at JET. This provides a fast, standardized classification of ELM types which is expected to significantly reduce the effort of ELM experts in identifying ELM types. Further, the classification scheme is general and can be applied to various other plasma phenomena as well

Study of plasma turbulence by ultrafast sweeping reflectometry on the Tore Supra tokamak

Plasma turbulence limits the performance of fusion reactors. Measuring and character- izing the turbulence properties is therefore a crucial issue in order to understand such phenomena. The goal of this thesis is to study the properties of plasma turbulence from ultrafast sweeping reflectometry measurements performed on the Tore Supra Tokamak. Reflectometry is a radar technique that is used to measure the electron density and its fluctuations. In the first part, we compare Langmuir probe and reflectometer data and discuss the possibility to characterize turbulence properties from the reconstructed fluctuating density profiles. Then, we show that the radial variation of the time and spatial scales of the turbulence as well as its radial velocity can be estimated from a cross-correlation analysis applied to the raw reflectometer signals. The modifications of the turbulence properties observed during a parametric scan are interpreted in the light of TEM and ITG turbulence. Finally, we show that the additional heating leads to a significant increase of the radial velocity in the plasma close to the tokamak wall

A first approach toward Bayesian estimation of turbulent plasma properties from reflectometry

The possibility of inferring the properties of electron density fluctuations in tokamak plasmas from a reflectometer signal by means of Bayesian methods is investigated. Within the physical optics approximation, the interaction of the probing beam with the plasma is described as reflection from a surface with stochastic properties that is simulated numerically. A Bayesian technique is outlined to solve the inverse problem to determine the surface characteristics from the power spectrum of the reflectometer signal. It is shown that satisfactory estimates of the length and timescales and the amplitude of density fluctuations can be obtained in conditions relevant to core tokamak plasmas

Robust analysis of trends in noisy tokamak confinement data using geodesic least squares regression

Regression analysis is a very common activity in fusion science for unveiling trends and parametric dependencies, but it can be a difficult matter. We have recently developed the method of geodesic least squares (GLS) regression that is able to handle errors in all variables, is robust against data outliers and uncertainty in the regression model, and can be used with arbitrary distribution models and regression functions. We here report on first results of application of GLS to estimation of the multi-machine scaling law for the energy confinement time in tokamaks, demonstrating improved consistency of the GLS results compared to standard least squares

Parametrization of reflectometry fluctuation frequency spectra for systematic study of tokamak fusion plasma turbulence

We describe a way to parameterize power spectra extracted from fixed-frequency reflectometry data, with a view to systematic studies of turbulence properties in tokamak plasmas. Analysis of typical frequency spectra obtained from a new database suggests decomposition in a set of four key components: the direct current component, low-frequency fluctuations, broadband (BB) turbulence, and the noise level. For the decomposition in the identified components, different kinds of functions are tested and their fitting performance is analyzed to determine the optimal spectrum parametrization. In particular, for the BB turbulence, three models are compared qualitatively based on a number of representative spectrum test cases, notably the generalized Gaussian, the Voigt, and the Taylor model. In addition, quantitative performance testing is accomplished using the weighted residual sum of squares and the Bayesian information criterion in a large database including 350 000 spectra obtained in Tore Supra. Next, parametrization by the Taylor model is applied to Ohmically heated plasmas, and a BB energy basin is systematically observed in the core plasma region, which shrinks with decreasing radial position of the q = 1 surface. This basin might be explained by a drop of the density fluctuation level inside the q = 1 surface