E. Peluso

Application of transfer entropy to causality detection and synchronization experiments in tokamaks

Determination of causal-effect relationships can be a difficult task even in the analysis of time series. This is particularly true in the case of complex, nonlinear systems affected by significant ...

How to assess the efficiency of synchronization experiments in tokamaks

Control of instabilities such as ELMs and sawteeth is considered an important ingredient in the development of reactor-relevant scenarios. Various forms of ELM pacing have been tried in the past to influence their behavior using external perturbations. One of the main problems with these synchronization experiments resides in the fact that ELMs are periodic or quasi-periodic in nature. Therefore, after any pulsed perturbation, if one waits long enough, an ELM is always bound to occur. To evaluate the effectiveness of ELM pacing techniques, it is crucial to determine an appropriate interval over which they can have a real influence and an effective triggering capability. In this paper, three independent statistical methods are described to address this issue: Granger causality, transfer entropy and recurrence plots. The obtained results for JET with the ITER-like wall (ILW) indicate that the proposed techniques agree very well and provide much better estimates than the traditional heuristic criteria reported in the literature. Moreover, their combined use allows for the improvement of the time resolution of the assessment and determination of the efficiency of the pellet triggering in different phases of the same discharge. Therefore, the developed methods can be used to provide a quantitative and statistically robust estimate of the triggering efficiency of ELM pacing under realistic experimental conditions.

Flow Motion and Dust Tracking Software for PIV and Dust PTV

Dust resuspension and mobilization in case of loss of vacuum accidents and loss of coolant accidents is an important safety issue for Tokamaks. The Quantum Electronics and Plasma Physics Research Group of the University of Rome Tor Vergata has produced an experimental facility, STARDUST-Upgrade, able to replicate these accidents and to obtain fluid dynamic characterization and dust mobilization information in order to validate CFD models. The authors decided to implement two non-intrusive optical methods, particle image velocimetry (PIV) and shadowgraph technique. Two software programs have been developed to compute numerical values from PIV and Shadowgraph frames, namely Flow Motion and Dust Tracking Software. Flow Motion Software has the capability to extract flow velocity field analyzing consecutive frames. Dust Tracking Software follows the path of single objects (i.e., dust particles) tracing their velocity, direction, and position over time. Two experiments have been realized for each software program in order to validate them: cigarette smoke and burning paper plume have been used for flow motion software, while tungsten dust and flour mobilization have been used for dust tracking software.

Advanced signal processing based on support vector regression for lidar applications

The LIDAR technique has recently found many applications in atmospheric physics and remote sensing. One of the main issues, in the deployment of systems based on LIDAR, is the filtering of the backscattered signal to alleviate the problems generated by noise. Improvement in the signal to noise ratio is typically achieved by averaging a quite large number (of the order of hundreds) of successive laser pulses. This approach can be effective but presents significant limitations. First of all, it implies a great stress on the laser source, particularly in the case of systems for automatic monitoring of large areas for long periods. Secondly, this solution can become difficult to implement in applications characterised by rapid variations of the atmosphere, for example in the case of pollutant emissions, or by abrupt changes in the noise. In this contribution, a new method for the software filtering and denoising of LIDAR signals is presented. The technique is based on support vector regression. The proposed new method is insensitive to the statistics of the noise and is therefore fully general and quite robust. The developed numerical tool has been systematically compared with the most powerful techniques available, using both synthetic and experimental data. Its performances have been tested for various statistical distributions of the noise and also for other disturbances of the acquired signal such as outliers. The competitive advantages of the proposed method are fully documented. The potential of the proposed approach to widen the capability of the LIDAR technique, particularly in the detection of widespread smoke, is discussed in detail.

Symbolic regression via genetic programming for data driven derivation of confinement scaling laws without any assumption on their mathematical form

Many measurements are required to control thermonuclear plasmas and to fully exploit them scientifically. In the last years JET has shown the potential to generate about 50 GB of data per shot. These amounts of data require more sophisticated data analysis methodologies to perform correct inference and various techniques have been recently developed in this respect. The present paper covers a new methodology to extract mathematical models directly from the data without any a priori assumption about their expression. The approach, based on symbolic regression via genetic programming, is exemplified using the data of the International Tokamak Physics Activity database for the energy confinement time. The best obtained scaling laws are not in power law form and suggest a revisiting of the extrapolation to ITER. Indeed the best non-power law scalings predict confinement times in ITER approximately between 2 and 3 s. On the other hand, more comprehensive and better databases are required to fully profit from the power of these new methods and to discriminate between the hundreds of thousands of models that they can generate.

A new approach to the formulation and validation of scaling expressions for plasma confinement in tokamaks

The extrapolation of the energy confinement time to the next generation of devices has been investigated both theoretically and experimentally for several decades in the tokamak community. Various scaling expressions have been proposed using dimensional and dimensionless quantities. They are all based on the assumption that the scalings are in power law form. In this paper, an innovative methodology is proposed to extract the scaling expressions for the energy confinement time in tokamaks directly from experimental databases, without any previous assumption about the mathematical form of the scalings. The approach to obtain the scaling expressions is based on genetic programming and symbolic regression. These techniques have been applied to the ITPA database of H-mode discharges and the results have been validated with a series of established statistical tools. The soundest results, using dimensional variables, are not in the form of power laws but contain a multiplicative saturation term. Also the scalings, expressed in terms of the traditional dimensionless quantities, are not in power law form and contain additive saturation terms. The extrapolation to ITER of both dimensional and dimensionless quantities indicate that the saturation effects are quite significant and could imply a non-negligible reduction in the confinement time to be expected in the next generation of devices. The results obtained with the proposed techniques therefore motivate a systematic revisiting of the scaling expressions for plasma confinement in tokamaks.

A statistical method for model extraction and model selection applied to the temperature scaling of the L–H transition

Access to the H mode of confinement in tokamaks is characterized by an abrupt transition, which has been the subject of continuous investigation for decades. Various theoretical models have been developed and multi-machine databases of experimental data have been collected. In this paper, a new methodology is reviewed for the investigation of the scaling laws for the temperature threshold to access the H mode. The approach is based on symbolic regression via genetic programming and allows first the extraction of the most statistically reliable models from the available experimental data. Nonlinear fitting is then applied to the mathematical expressions found by symbolic regression; this second step permits to easily compare the quality of the data-driven scalings with the most widely accepted theoretical models. The application of a complete set of statistical indicators shows that the data-driven scaling laws are qualitatively better than the theoretical models. The main limitations of the theoretical models are that they are all expressed as power laws, which are too rigid to fit the available experimental data and to extrapolate to ITER. The proposed method is absolutely general and can be applied to the extraction or scaling law from any experimental database of sufficient statistical relevance.

Influence of plasma diagnostics and constraints on the quality of equilibrium reconstructions on Joint European Torus

One of the main approaches to thermonuclear fusion relies on confining high temperature plasmas with properly shaped magnetic fields. The determination of the magnetic topology is, therefore, essential for controlling the experiments and for achieving the required performance. In Tokamaks, the reconstruction of the fields is typically formulated as a free boundary equilibrium problem, described by the Grad-Shafranov equation in toroidal geometry and axisymmetric configurations. Unfortunately, this results in mathematically very ill posed problems and, therefore, the quality of the equilibrium reconstructions depends sensitively on the measurements used as inputs and on the imposed constraints. In this paper, it is shown how the different diagnostics (Magnetics Measurements, Polarimetry and Motional Stark Effect), together with the edge current density and plasma pressure constraints, can have a significant impact on the quality of the equilibrium on JET. Results show that both the Polarimetry and Motional Stark Effect internal diagnostics are crucial in order to obtain reasonable safety factor profiles. The impact of the edge current density constraint is significant when the plasma is in the H-mode of confinement. In this plasma scenario the strike point positions and the plasma last closed flux surface can change even by centimetres, depending on the edge constraints, with a significant impact on the remapping of the equilibrium-dependent diagnostics and of pedestal physics studies. On the other hand and quite counter intuitively, the pressure constraint can severely affect the quality of the magnetic reconstructions in the core. These trends have been verified with several JET discharges and consistent results have been found. An interpretation of these results, as interplay between degrees of freedom and available measurements, is provided. The systematic analysis described in the paper emphasizes the importance of having sufficient diagnostic inputs and of properly validating the results of the codes with independent measurements.

A statistical investigation of the effects of edge localized modes on the equilibrium reconstruction in JET

The configuration of magnetic fields is an essential ingredient of tokamak physics. In modern day devices, the magnetic topology is normally derived from equilibrium codes, which solve the Grad–Shafranov equation with constraints imposed by the available measurements. On JET, the main code used for this purpose is EFIT and the more commonly used diagnostics are external pick-up coils. Both the code and the measurements present worse performance during edge localized modes (ELMs). To quantify this aspect, various statistical indicators, based on the values of the residuals and their probability distribution, are defined and calculated. They all show that the quality of EFIT reconstructions is clearly better in the absence of ELMs. To investigate the possible causes of the detrimental effects of ELMs on the reconstruction, the pick-up coils are characterized individually and both the spatial distribution and time behaviour of their residuals are analysed in detail. The coils with a faster time response are the ones reproduced less well by EFIT. The constraints of current and pressure at the separatrix are also varied but the effects of such modifications do not result in decisive improvements in the quality of the reconstructions. The interpretation of this experimental evidence is not absolutely compelling but strongly indicative of deficiencies in the physics model on which the JET reconstruction code is based.

Lidar and Dial application for detection and identification: A proposal to improve safety and security

Nowadays the intentional diffusion in air (both in open and confined environments) of chemical contaminants is a dramatic source of risk for the public health worldwide. The needs of a high-tech networks composed by software, diagnostics, decision support systems and cyber security tools are urging all the stakeholders (military, public, research & academic entities) to create innovative solutions to face this problem and improve both safety and security. The Quantum Electronics and Plasma Physics (QEP) Research Group of the University of Rome Tor Vergata is working since the 1960s on the development of laser-based technologies for the stand-off detection of contaminants in the air. Up to now, four demonstrators have been developed (two LIDAR-based and two DIAL-based) and have been used in experimental campaigns during all 2015. These systems and technologies can be used together to create an innovative solution to the problem of public safety and security: the creation of a network composed by detection systems: A low cost LIDAR based system has been tested in an urban area to detect pollutants coming from urban traffic, in this paper the authors show the results obtained in the city of Crotone (south of Italy). This system can be used as a first alarm and can be coupled with an identification system to investigate the nature of the threat. A laboratory dial based system has been used in order to create a database of absorption spectra of chemical substances that could be release in atmosphere, these spectra can be considered as the fingerprints of the substances that have to be identified. In order to create the database absorption measurements in cell, at different conditions, are in progress and the first results are presented in this paper.