1,721,140 research outputs found
Tungsten spectra recorded at the LHD and comparison with calculations
Full text linkWe have measured extreme ultraviolet (EUV) spectra from highly charged tungsten ions in low-density and high-temperature plasmas produced in the Large Helical Device at the National Institute for Fusion Science. The EUV spectra emitted after injection of a tungsten pellet into a hydrogen plasma were recorded at plasma temperatures of 1.5 and 3 keV and were dominated by an intense transition array in the 4.5–6.5 nm region, the profile and extent of which was different in both spectra. Some discrete lines present were identified by comparison with existing spectral data while atomic structure calculations showed that the dominant emission in both arose from Δn = 0, n = 4–n = 4 transitions and the main differences could be attributed to the appearance of the 4p–4d and 4s–4p transitions from W XXXIX to W XLVI in the higher temperature spectrum. Comparison with calculations showed that the dominant emission in both temperature regimes arose from stages where the 4f subshell was either almost or completely stripped. We also investigated if the effect of low density favours transitions to the lowest level as observed in recently reported results.Science Foundation IrelandOther funderJapan Society for the Promotion of Scienceau, sp, ab, st, en, li - TS 30.03.1
Tracking of the Plasma States in a Nuclear Fusion Device Using SOMsEngineering Applications of Neural Networks
No full textKnowledge discovery consists of finding new knowledge from data bases where dimension, complexity or amount of data is prohibitively large for human observation alone. The Self Organizing Map (SOM) is a powerful neural network method for the analysis and visualization of high-dimensional data. The need for efficient data visualization and clustering is often faced, for instance, in the analysis, monitoring, fault detection, or prediction of various engineering plants. In this paper, the use of a SOM based method for prediction of disruptions in experimental devices for nuclear fusion is investigated. The choice of the SOM size is firstly faced, which heavily affects the performance of the mapping. Then, the ASDEX Upgrade Tokamak high dimensional operational space is mapped onto the 2-dimensional SOM, and, finally, the current process state and its history in time has been visualized as a trajectory on the map, in order to predict the safe or disruptive state of the plasma
Disruption Prediction with Adaptive Neural Networks for ASDEX Upgrade
No full textIn this paper, an adaptive neural system has been built to predict the risk of disruption at ASDEX Upgrade.
The system contains a Self Organizing Map, which determines the ‘novelty’ of the input of a Multi Layer
Perceptron predictor module. The answer of the MLP predictor will be inhibited whenever a novel sample
is detected. Furthermore, it is possible that the predictor produces a wrong answer although it is fed with
known samples. In this case, a retraining procedure will be performed to update the MLP predictor in an
incremental fashion using data coming from both the novelty detection, and from wrong predictions. In
particular, a new update is performed whenever a missed alarm is triggered by the predictor.
The performance of the adaptive predictor during the more recent experimental campaigns until
November 2009 has been evaluated
Criteria and algorithms for constructing reliable databases for statistical analysis of disruptions at ASDEX Upgrade
No full textThe present understanding of disruption physics has not gone so far as to provide a mathematical model describing the onset of this instability. A disruption prediction system, based on a statistical analysis of the diagnostic signals recorded during the experiments, would allow estimating the probability of a disruption to take place. A crucial point for a good design of such a prediction system is the appropriateness of the data set. This paper reports the details of the database built to train a disruption predictor based on neural networks for ASDEX Upgrade. The criteria of pulses selection, the analyses performed on plasma parameters and the implemented pre-processing algorithms, are described. As an example of application, a short description of the disruption predictor is reported
Analysis Of The Impact Of Antenna And Plasma Models On RF Potentials Evaluation
No full textThe design of an Ion Cyclotron (IC) launcher is essentially driven by its coupling properties and its capability of maintaining low parallel electric fields in front of it, indeed providing good power transfer to plasma and avoiding unwanted phenomena such as sheath rectification or hot spots. Both aspects are deeply related to the adopted geometry and the loaded plasma model; the systematic usage of TOPICA code [1], able to precisely take into account a realistic antenna geometry and an accurate plasma description, could certainly help in understanding which elements may have a not negligible effect on the antenna design. This paper presents a detailed comparison, carried out with TOPICA code, between a simplified flat version of one of the IC antennas installed in ASDEX Upgrade experiment and its real curved geometry. The advantages and disadvantages of both geometrical representations are outlined in terms of power transferred to the plasma and with a specific focus on sheath driving potentials. To complete the overview, the importance of an accurate plasma description is also exploite
ITER Plasma Scenarios scaled from ASDEX Upgrade and JET experimental data and their impact on ITER operational space
No full textTracking of the Plasma States in a Nuclear Fusion Device using SOMs
No full textKnowledge discovery consists of finding new knowledge from data bases where dimension, complexity or amount of data is prohibitively large for human observation alone. The Self Organizing Map (SOM) is a powerful neural network method for the analysis and visualization of high-dimensional data. The need for efficient data visualization and clustering is often faced, for instance, in the analysis, monitoring, fault detection, or prediction of various engineering plants. In this paper, the use of a SOM based method for prediction of disruptions in experimental devices for nuclear fusion is investigated. The choice of the SOM size is firstly faced, which heavily affects the performance of the mapping. Then, the ASDEX Upgrade Tokamak high dimensional operational space is mapped onto the 2-dimensional SOM, and, finally, the current process state and its history in time has been visualized as a trajectory on the map, in order to predict the safe or disruptive state of the plasma. © 2009 Springer-Verlag
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