1,720,961 research outputs found
Multivariate statistical models for disruption prediction at ASDEX Upgrade
No full textIn this paper, a disruption prediction system for ASDEX Upgrade has been proposed that does not require disruption terminated experiments to be implemented. The system consists of a data-based model, which is built using only few input signals coming from successfully terminated pulses. A fault detection and isolation approach has been used, where the prediction is based on the analysis of the residuals of an auto regressive exogenous input model. The prediction performance of the proposed system is encouraging when it is applied to the same set of campaigns used to implement the model. However, the false alarms significantly increase when we tested the system on discharges coming from experimental campaigns temporally far from those used to train the model. This is due to the well know aging effect inherent in the data-based models. The main advantage of the proposed method, with respect to other data-based approaches in literature, is that it does not need data on experiments terminated with a disruption, as it uses a normal operating conditions model. This is a big advantage in the prospective of a prediction system for ITER, where a limited number of disruptions can be allowed. (C) 2013 Elsevier B.V. All rights reserved
An adaptive disruption predictor based on FDI approach for next generation Tokamaks
No full textDisruptions have the potential to create serious damage to large reactor-scale. Thus,
disruption detection is essential to allow proper mitigation actions to be triggered when
preventing disruptions is not feasible. Several contributions have been proposed using neural
network models with good performances in different tokamaks. In particular, Support Vector
Machines in JET [1, 2] and Multi-layer Perceptrons both in JET [3] and ASDEX Upgrade [4].
The main drawback of these methods is the need of a set of disruptions to implement the
predictive model. As largely known, ITER cannot wait for hundreds of disruptions to develop
a successful disruption predictor. Hence, a prediction system starting from only few safe
discharges will be required. Thus, the proposed approaches are not directly applicable. To this
purpose, the disruption prediction can be formalized as a fault detection and isolation (FDI)
problem, where the safe pulses are assumed as the normal operating condition and the
disruptions are assumed as status of fault [5]. The main advantage of the proposed FDI
methods is that the model can be developed without any information about disruptions. In this
work, in view of ITER, an adaptive disruption predictor based on FDI approach is developed.
In particular, an autoregressive model is trained to represent the normal operating conditions
(NOC) described by few safe shots. Then, the model is progressively updated as soon as a
new safe configuration is performed. The dynamic structure of each pulse is estimated
through the fitting of the NOC model, the discrepancy between the outputs provided by the
NOC model and the actual measurements (residual) is an indication of the plasma
disruptivity. The prediction performance is evaluated for JET using a set of safe and disrupted
discharges in terms of correct predictions, missed and false alarms. Preliminary results show
the suitability of the proposed method
Data visualization and dimensionality reduction methods for disruption prediction at ASDEX Upgrade
No full textThe. physical phenomena leading to disruptions are very. complex and non linear and the present state of knowledge is not sufficient to explain the intrinsic structure of the data of interest. One viable way to extract information from the complex multidimensional operational space of a tokamak is to assume that the data which describe this space lie on an embedded, possibly nonlinear, low-dimensional subspace (manifold) within the higher dimensional space.To this purpose, recently, data visualization and. dimensionality reduction methods have been actively investigated. Among nonlinear methods the most popular are the. Self Organizing Map (SOM) and its probabilistic variant, the. Generative. Topographic. Mapping(GTM. ). The SOM has been already employed as disruption predictor at ASDEX Upgrade with good results. In this study, a 2D GTM has been built to represent the 7D ASDEX Upgrade operational space described by means of. a database of disrupted and nondisrupted discharges selected in the shot range 21654- 26891 and performed in ASDEX Upgrade between May 2007 and April 2011. The GTM clearly highlights the presence of a large region with an associated low risk of. disruption and. some. small regions (located in the map margins) with an associated high risk of disruption. The GTM proves to be able to separate nondisruptive. and disruptive states of plasma. Therefore, likewise the SOM ,t.he. GTM can be. used as a disruption predictor. by. track. ing. the temporal sequence of the samples on the map, depicting the. movement of the operating point during a discharge. Following the trajectory in the GTM. , it will be possible to eventually recognize the proximity to an operational. region where the risk of a. n imminent disruption is high.. In this paper, v. arious. criteria have been studied to. associate the risk of disruption of each map region with a disruption alarm threshold. The prediction performance of the proposed predictive system has been evaluated on a test set of discharges coming from experimental campaigns carried out at ASDEX Upgrade from May 2011 to November 2012. The achieved results are encouraging and indicate the appropriateness of the method, also comparing it with those obtained using SOM. Moreover,it is worth emphasizing that, compared to other disruption prediction approaches the GTM map. provides significant additional value. Whereas the tools in the reference papers are black boxes, which provide a prediction but are very difficult to interpret, on the contrary, the map allows to follow the trajectory of the plasma and to study its behavior leading to a d. isruption. So the developed map has the potential to provide much more than a simple prediction in the understanding of the operational space and the causes of the disruptions
Adaptive mapping of the plasma operational space of ASDEX Upgrade for disruption prediction
No full textThe Self-Organizing Map is a computational method for the visualization and analysis of high-dimensional data. Self Organizing Maps have been applied to ASDEX Upgrade data to define an ordered mapping of an 8-dimensional plasma parameter space onto a regular, 2-dimensional grid. The map has been used to track the plasma trajectory during the experiments and monitor the disruption risk. In order to face with ever new operational conditions, a periodical updating of the Self Organizing Map is proposed
Manifold learning techniques and statistical approaches applied to the disruption prediction in tokamaks
Full text linkThe nuclear fusion arises as the unique clean energy source capable to meet the energy needs of the entire world in the future. On present days, several experimental fusion devices are operating to optimize the fusion process, confining the plasma by means of magnetic fields. The goal of plasma confined in a magnetic field can be achieved by linear cylindrical configurations or toroidal configurations, e.g., stellarator, reverse field pinch, or tokamak. Among the explored magnetic confinement techniques, the tokamak configuration is to date considered the most reliable. Unfortunately, the tokamak is vulnerable to instabilities that, in the most severe cases, can lead to lose the magnetic confinement; this phenomenon is called disruption. Disruptions are dangerous and irreversible events for the device during which the plasma energy is suddenly released on the first wall components and vacuum vessel causing runaway electrons, large mechanical forces and intense thermal loads, which may cause severe damage to the vessel wall and the plasma face components. Present devices are designed to resist the disruptive events; for this reason, today, the disruptions are generally tolerable. Furthermore, one of their aims is the investigation of disruptive boundaries in the operational space. However, on future devices, such as ITER, which must operate at high density and at high plasma current, only a limited number of disruptions will be tolerable. For these reasons, disruptions in tokamaks must be avoided, but, when a disruption is unavoidable, minimizing its severity is mandatory. Therefore, finding appropriate mitigating actions to reduce the damage of the reactor components is accepted as fundamental objective in the fusion community. The physical phenomena that lead plasma to disrupt are non-linear and very complex. The present understanding of disruption physics has not gone so far as to provide an analytical model describing the onset of these instabilities and the main effort has been devoted to develop data-based methods. In the present thesis the development of a reliable disruption prediction system has been investigated using several data-based approaches, starting from the strengths and the drawbacks of the methods proposed in the literature. In fact, literature reports numerous studies for disruption prediction using data-based models, such as neural networks. Even if the results are encouraging, they are not sufficient to explain the intrinsic structure of the data used to describe the complex behavior of the plasma. Recent studies demonstrated the urgency of developing sophisticated control schemes that allow exploring the operating limits of tokamak in order to increase the reactor performance.
For this reason, one of the goal of the present thesis is to identify and to develop tools for visualization and analysis of multidimensional data from numerous plasma diagnostics available in
the database of the machine. The identification of the boundaries of the disruption free plasma parameter space would lead to an increase in the knowledge of disruptions. A viable approach to understand disruptive events consists of identifying the intrinsic structure of the data used to describe the plasma operational space. Manifold learning algorithms attempt to identify these structures in order to find a low-dimensional representation of the data. Data for this thesis comes from ASDEX Upgrade (AUG). ASDEX Upgrade is a medium size tokamak experiment located at IPP Max-Planck-Institut für Plasmaphysik, Garching bei München (Germany). At present it is the largest tokamak in Germany. Among the available methods the attention has been mainly devoted to data clustering techniques. Data clustering consists on grouping a set of data in such a way that data in the same group (cluster) are more similar to each other than those in other groups. Due to the inherent predisposition for visualization, the most popular and widely used clustering technique, the Self-Organizing Map (SOM), has been firstly investigated. The SOM allows to extract information from the multidimensional operational space of AUG using 7 plasma parameters coming from successfully terminated (safe) and disruption terminated (disrupted) pulses. Data to train and test the SOM have been extracted from AUG experiments performed between July 2002 and November 2009. The SOM allowed to display the AUG operational space and to identify regions with high risk of disruption (disruptive regions) and those with low risk of disruption (safe regions). In addition to space visualization purposes, the SOM can be used also to monitor the time evolution of the discharges during an experiment. Thus, the SOM has been used as disruption predictor by introducing a suitable criterion, based on the trend of the trajectories on the map throughout the different regions. When a plasma configuration with a high risk of disruption is recognized, a disruption alarm is triggered allowing to perform disruption avoidance or mitigation actions. The data-based models, such as the SOM, are affected by the so-called "ageing effect". The ageing effect consists in the degradation of the predictor performance during the time. It is due to the fact that, during the operation of the predictor, new data may come from experiments different from those used for the training. In order to reduce such effect, a retraining of the predictor has been proposed. The retraining procedure consists of a new training procedure performed adding to the training set the new plasma configurations coming from more recent experimental campaigns. This aims to supply the novel information to the model to increase the prediction performances of the predictor.
Another drawback of the SOM, common to all the proposed data-based models in literature, is the need of a dedicated set of experiments terminated with a disruption to implement the predictive
model. Indeed, future fusion devices, like ITER, will tolerate only a limited number of disruptive events and hence the disruption database won't be available.
In order to overcome this shortcoming, a disruption prediction system for AUG built using only input signals from safe pulses has been implemented. The predictor model is based on a Fault Detection and Isolation (FDI) approach. FDI is an important and active research field which allows to monitor a system and to determine when a fault happens. The majority of model-based FDI procedures are based on a statistical analysis of residuals. Given an empirical model identified on a reference dataset, obtained under Normal Operating Conditions (NOC), the discrepancies between the new observations and those estimated by the NOCs (residuals) are calculated. The residuals are considered as a random process with known statistical properties. If a fault happens, a change of these properties is detected. In this thesis, the safe pulses are assumed as the normal operation conditions of the process and the disruptions are assumed as status of fault. Thus, only safe pulses are used to train the NOC model. In order to have a graphical representation of the trajectory of the pulses, only three plasma parameters have been used to build the NOC model. Monitoring the time evolution of the residuals by introducing an alarm criterion based on a suitable threshold on the residual values, the NOC model properly identifies an incoming disruption. Data for the training and the tests of the NOC model have been extracted from AUG experiments executed between July 2002 and November 2009. The assessment of a specific disruptive phase for each disruptive discharge represents a relevant issue in understanding the disruptive events. Up to now at AUG disruption precursors have been assumed appearing into a prefixed time window, the last 45ms for all disrupted discharges. The choice of such a fixed temporal window could limit the prediction performance. In fact, it generates ambiguous information in cases of disruptions with disruptive phase different from 45ms. In this thesis, the Mahalanobis distance is applied to define a specific disruptive phase for each disruption. In particular, a different length of the disruptive phase has been selected for each disrupted pulse in the training set by labeling each sample as safe or disruptive depending on its own Mahalanobis distance from the set of the safe discharges.
Then, with this new training set, the operational space of AUG has been mapped using the Generative Topography Mapping (GTM). The GTM is inspired by the SOM algorithm, with the aim to overcome its limitations.
The GTM has been investigated in order to identify regions with high risk of disruption and those with low risk of disruption. For comparison purposes a second SOM has been built. Hence, GTM and SOM have been tested as disruption predictors. Data for the training and the tests of the SOM
and the GTM have been extracted from AUG experiments executed from May 2007 to November 2012.
The last method studied and applied in this thesis has been the Logistic regression model (Logit). The logistic regression is a well-known statistic method to analyze problems with dichotomous dependent variables. In this study the Logit models the probability that a generic sample belongs to the non-disruptive or the disruptive phase. The time evolution of the Logit Model output (LMO) has been used as disruption proximity index by introducing a suitable threshold. Data for the training and the tests of the Logit models have been extracted from AUG experiments executed from May 2007 to November 2012. Disruptive samples have been selected through the Mahalanobis distance criterion.
Finally, in order to interpret the behavior of data-based predictors, a manual classification of disruptions has been performed for experiments occurred from May 2007 to November 2012. The manual classification has been performed by means of a visual analysis of several plasma parameters for each disruption. Moreover, the specific chains of events have been detected and used to classify disruptions and when possible, the same classes introduced for JET are adopte
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Improvements in disruption prediction at ASDEX Upgrade
No full textIn large-scale tokamaks disruptions have the potential to create serious damage to the facility. Hence disruptions must be avoided, but, when a disruption is unavoidable, minimizing its severity is mandatory. A reliable detection of a disruptive event is required to trigger proper mitigation actions. To this purpose machine learning methods have been widely studied to design disruption prediction systems at ASDEX Upgrade. The training phase of the proposed approaches is based on the availability of disrupted and non-disrupted discharges. In literature disruptive configurations were assumed appearing into the last 45 ms of each disruption. Even if the achieved results in terms of correct predictions were good, it has to be highlighted that the choice of such a fixed temporal window might have limited the prediction performance. In fact, it generates confusing information in cases of disruptions with disruptive phase different from 45 ms. The assessment of a specific disruptive phase for each disruptive discharge represents a relevant issue in understanding the disruptive events. In this paper, the Mahalanobis distance is applied to define a specific disruptive phase for each disruption, and a logistic regressor has been trained as disruption predictor. The results show that enhancements on the achieved performance on disruption prediction are possible by defining a specific disruptive phase for each disruption
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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