First operations with the new Collective Thomson Scattering diagnostic on the Frascati Tokamak Upgrade device

Bin, W.; Bruschi, A.; D'Arcangelo, O.; Castaldo, C.; Angeli, M. De; Figini, L.; Galperti, C.; Garavaglia, S.; Granucci, G.; Grosso, G.; Korsholm, S. B.; Lontano, M.; Mellera, V.; Minelli, D.; Moro, A.; Nardone, A.; Nielsen, S. K.; Rasmussen, Jesper; Simonetto, A.; Stejner, M.; Tartari, U.

doi:10.1088/1748-0221/10/10/p10007

Cited by 12 publications

(20 citation statements)

References 27 publications

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“…Data for both the fast digitizer and the photomultiplier (used to monitor the visible light level in the launching port) reported in Ref. 7 are acquired on local memories in the experimental room. Fig.…”

Section: Improvement To the Data Acquisition Systemmentioning

confidence: 99%

“…Also a new sniffer probe was installed in 2015 on a window located on the backside of the vacuum flange, as described in Ref. 7, in order to monitor the stray radiation level inside the launcher. Signal conditioning for this sensor have now been set properly and the first measurements of stray power in the CTS port have been taken.…”

Section: Improvement To the Data Acquisition Systemmentioning

confidence: 99%

“…3 The interest in studying these scenarios in the Frascati Tokamak Upgrade (FTU) device led to a substantial renewal 4,5 of the CTS diagnostic, already exploited in the past experimental campaigns for CTS measurements of thermal ion temperature. 6 The first operations of the new CTS system have been performed 7,8 in 2014 and 2015. The two main goals of ongoing experiments, carried out in the framework of the EUROfusion Enabling Research project AWP15-ENR-01/ENEA-06, are the detection of low power PDI excited by EC pumping wave in presence of tearing mode islands and the investigation of their possible effects on ECRH absorption.…”

Section: Introductionmentioning

confidence: 99%

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Advances in the FTU collective Thomson scattering system

Bin

Bruschi

D'Arcangelo

et al. 2016

Review of Scientific Instruments

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The new collective Thomson scattering diagnostic installed on the Frascati Tokamak Upgrade device started its first operations in 2014. The ongoing experiments investigate the presence of signals synchronous with rotating tearing mode islands, possibly due to parametric decay processes, and phenomena affecting electron cyclotron beam absorption or scattering measurements. The radiometric system, diagnostic layout, and data acquisition system were improved accordingly. The present status and near-term developments of the diagnostic are presented.

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Section: Improvement To the Data Acquisition Systemmentioning

confidence: 99%

Section: Improvement To the Data Acquisition Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Advances in the FTU collective Thomson scattering system

Bin

Bruschi

D'Arcangelo

et al. 2016

Review of Scientific Instruments

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“…The Collective Thomson Scattering (CTS) diagnostic on the FTU tokamak, originally designed to investigate the CTS emission by thermal ions [1], was recently renewed [2,3] for investigations on the possible excitation of low power threshold Parametric Decay Instabilities (PDI, see [4]) by ECRH beams in presence of magnetic islands, as observed in EX/P8-23 TEXTOR [5], with the aim to add spectral evidences to the current interpretations [6], and study their possible effects on the absorption of powerful microwave beams used for Electron Cyclotron (EC) heating and Current Drive.…”

Section: Introductionmentioning

confidence: 99%

Observation of short time-scale spectral emissions at millimeter wavelengths with the new CTS diagnostic on the FTU tokamak

et al. 2017

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“…The Damavand Tokamak is a small size research machine for fusion-related studies like plasma diagnostics [27,28]. In Damavand Tokamak, hydrogen plasma is sustained for 21ms with a plasma current peak of about 35 kA.…”

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confidence: 99%

Identification of Damavand tokamak using fractional order dynamic neural network

Aslipour

Yazdizadeh

2018

Transactions of the Institute of Measurement and Control

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IntroductionThere are many reasons for developing a mathematical model for an actual plant, among which we may refer to the necessity of designing an identifier in all model-based controller schemes [1]. In dynamic systems, integer order differentiation and partial order differentiation can be used to describe the behaviour of the system. In fractional order differential equations, there is an additional parameter, namely, fractional order, which is important to be selected properly to get better results while modelling and simulating the plant. The fractional order derivative operator is part of the fractional calculus that was introduced 300 years ago. The main advantage of the fractional derivative, in comparison with integer order methods, is that it provides an excellent tool for describing memory and hereditary properties of the processes. This advantage cannot be ignored and as a result, fractional modelling has been employed in different processes [2][3][4][5].Analytical modelling is basically development of a mathematical relationship between input and output based on the physical laws or based on some numerical phenomena. Neural network techniques seem to be very useful to identify different classes of nonlinear systems. Major properties that motivate the use of neural networks are as follows: their nonlinear characteristics make them appropriate for identifying nonlinear systems and their learning characteristics are ideal for adapting to different environmental conditions [6]. Neural networks can be categorized as static (Feedforward) and dynamic (recurrent or differential) neural networks. The main disadvantages of the static structure are as follows: a) slow learning rate, b) lack of possessing memory, so their outputs are uniquely dependent on the current inputs. In contrast, dynamic neural networks can successfully overcome these shortcomings and demonstrate promising behaviour in the presence of un-modelled dynamics because their structure includes an intrinsic feedback. So far, different structures for the integer order dynamic neural network have been used for different applications [7][8][9][10][11].The use of fractional-order models in system identification was initiated in the late 1990s and the beginning of this century. Several techniques are available for identification of the systems

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First operations with the new Collective Thomson Scattering diagnostic on the Frascati Tokamak Upgrade device

Cited by 12 publications

References 27 publications

Advances in the FTU collective Thomson scattering system

Advances in the FTU collective Thomson scattering system

Observation of short time-scale spectral emissions at millimeter wavelengths with the new CTS diagnostic on the FTU tokamak

Identification of Damavand tokamak using fractional order dynamic neural network

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