Fabio Pisano

Extensive statistical analysis of ELMs on JET with a carbon wall

Edge localized modes (ELMs) are bursts of instabilities which deteriorate the confinement of H mode plasmas and can cause damage to the divertor of next generation of devices. On JET individual discharges can exhibit hundreds of ELMs but typically in the literature, mainly due to the lack of automatic analysis tools, single papers investigate only the behaviour of tens of individual ELMs. In this paper, an original tool, the universal event locator (UMEL), is applied to the problem of automatically identifying the time location of ELMs. With this approach, databases of hundreds of thousands of ELMs can be built with reasonable effort. The analysis has then been focused on the investigation of the statistical distribution of the inter-ELM intervals at steady state for type I ELMs. Numerous probability distributions have been tested to perform the data analysis and different distributions provide a best fit for sets of data from different experiments. This result constitutes robust experimental confirmation that type I ELMs are not all necessarily the same type of instability. Moreover, the most likely distributions are not memoryless, meaning that the waiting time, from a particular instant until the next ELM, does depend on the time elapsed from the previous event. These properties, confirmed by this investigation on JET, pose important constraints on the models aimed at describing the ELM dynamics. This work also demonstrates the widespread applicability of the UMEL tool.

Wendelstein 7-X near real-time image diagnostic system for plasma facing components protection

Abstract The Wendelstein 7-X (W7-X) fusion experiment is aimed at proving that the stellarator concept is suitable for a future fusion reactor. Therefore, it is designed for steady-state plasmas of up to 30 min, which means that the thermal control of the plasma-facing components (PFCs) is of vital importance to prevent damage to the device. In this paper an overview of the design of the Near Real-Time Image Diagnostic System (hereinafter called “the System”) for PFCs protection in W7-X is presented. The goal of the System is to monitor the PFCs with high risk of permanent damage due to local overheating during plasma operations and to send alarms to the interlock system. The monitoring of the PFCs is based on thermographic and video cameras, and their video streams are analyzed by means of graphics processing unit–based computer vision techniques to detect the strike line, hot spots, and other thermal events. The video streams and the detected thermal events are displayed online in the control room in the form of a thermal map and permanently stored in the database. In order to determine the emissivity and maximum temperature allowed, a pixel-based correspondence between the image and the observed device part is required. The three-dimensional geometry of W7-X makes the System particularly sensitive to the spatial calibration of the cameras since hot spots can be expected anywhere, and a full segmentation of the field of view is necessary, in contrast to other regions of interest–based systems. A precise registration of the field of view and a correction of the strong lens distortion caused by the wide-angle optical system are then required. During the next operation phase the uncooled graphite divertor units will allow the System to be tested without risk of damaging the divertors in preparation for when water-cooled high-heat-flux divertors will be used.

Effect of a particular coupling on the dynamic behavior of nonlinear systems

SUMMARY In this paper, the dynamics of particular nonlinear dynamic systems of order six, characterized by two positive Lyapunov exponents, is analyzed. Transformation techniques are applied to the study of these systems; in particular, the analysis of the transverse and tangent systems showed that they are equivalent to two uncoupled identical chaotic systems of order three. Examples of polynomial and piecewise linear systems are proposed. Both numerical simulation and circuit implementation have been performed. Moreover, a systematic method to project these systems is presented. Copyright

A Fast Algorithm for Lyapunov Exponents Calculation in Piecewise Linear Systems

Lyapunov exponents of a dynamical system give information about its long‐term evolution. Exponents estimation is not an easy task; it is computationally costly and, in presence of chaotic dynamics, it exhibits numerical difficulties. In this paper, an algorithm which optimizes Lyapunov exponents estimation in piecewise linear systems has been developed. The algorithm exploits the linearity of the state equation and of the variational equation to accurately evaluate Lyapunov exponents with a reduced execution time.

Autosomal dominant nocturnal frontal lobe epilepsy seizure characterization through wavelet transform of eeg records and self organizing maps

In this paper, a Manifold Learning approach for the automatic detection of Autosomal Dominant Nocturnal Frontal Lobe Epilepsy seizures is presented, with the aim to support neurologists in the labelling efforts. Features extracted from polysomnography signals are used in order to detect and discriminate seizure epochs. This task has been addressed by mapping the electroencephalographic signal epochs in different regions of the features space. The result is a Self Organizing Map, which allows to investigate over not straightforward relations in the complex input space for the characterization of seizures.

Infrared imaging systems for wall protection in the W7-X stellarator (invited)

Wendelstein 7-X aims at quasi-steady state operation with up to 10 MW of heating power for 30 min. Power exhaust will be handled predominantly via 10 actively water cooled CFC (carbon-fiber-reinforced carbon) based divertor units designed to withstand power loads of 10 MW/m2 locally in steady state. If local loads exceed this value, a risk of local delamination of the CFC and failure of entire divertor modules arises. Infrared endoscopes to monitor all main plasma facing components are being prepared, and near real time software tools are under development to identify areas of excessive temperature rise, to distinguish them from non-critical events, and to trigger alarms. Tests with different cameras were made in the recent campaign. Long pulse operation enforces additional diagnostic design constraints: for example, the optics need to be thermally decoupled from the endoscope housing. In the upcoming experimental campaign, a graphite scraper element, in front of the island divertor throat, will be tested as a possible means to protect the divertor pumping gap edges during the transient discharge evolution.

Convolutional Neural Networks for the Identification of Filaments from Fast Visual Imaging Cameras in Tokamak Reactors

The paper proposes a region-based deep learning convolutional neural network to detect objects within images able to identify the filamentary plasma structures that arise in the boundary region of the plasma in toroidal nuclear fusion reactors. The images required to train and test the neural model have been synthetically generated from statistical distributions, which reproduce the statistical properties in terms of position and intensity of experimental filaments. The recently proposed Faster Region-based Convolutional Network algorithm has been customized to the problem of identifying the filaments both in location and size with the associated score. The results demonstrate the suitability of the deep learning approach for the filaments detection.

TIME SERIES DENOISING BASED ON WAVELET DECOMPOSITION AND CROSS-CORRELATION BETWEEN THE RESIDUALS AND THE DENOISED SIGNAL

In this paper, a new denoising method, based on the wavelet transform of the noisy signal, is described. The method implements a variable thresholding, whose optimal value is determined by analyzing the crosscorrelation between the denoised signal and the residuals and by applying different criteria depending on the particular decomposition level. The residuals are defined as the difference between the noisy signal and the denoised signal. The procedure is suitable for denoising signals in real situations when the noiseless signal is not known. The results, obtained with synthetic data generated by well-known chaotic systems, show the very competitive performance of the proposed technique.

Identification of Chaotic Systems by Neural Networks

In this paper a traditional Multi Layer Perceptron with a tapped delay line as input is trained to identify the parameters of the Chua’s circuit when fed with a sequence of values of a scalar state variable. The analysis of the a priori identifiability of the system, performed resorting to differential algebra, allows one to choose a suitable observable and the minimum number of taps. The results confirm the appropriateness of the proposed approach.