Simulation of open-loop plasma vertical movement response in the Damavand tokamak using closed-loop subspace system identification

Farahani, Nikoo Darestani; Davani, F. Abbasi

doi:10.1088/1748-0221/11/02/p02006

Cited by 4 publications

(7 citation statements)

References 30 publications

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“…Before selection of the method, linearity or nonlinearity of the system behavior must be determined. This subject has been discussed in Darestani Farahani and Abbasi Davani (2016). It is shown that the Damavand tokamak has a nonlinear inherent.…”

Section: Validation Of the Proposed Methods By Using Experimental Datamentioning

confidence: 99%

“…For a selected operating point, different data shots have been gathered, in a way that the coefficients of the controller and the initial conditions of the system are identical for all of them. In this paper, we use the only available data set, namely, the experimental data that are collected by authors in Darestani Farahani and Abbasi Davani (2016). In order to compare the identification accuracy achieved by the proposed FODNN model and the integer order dynamic neural network, the performance index,…”

Section: Comparison Between the Proposed Methods Based On Fodnn And Inmentioning

confidence: 99%

“…Considering the total time of plasma electrical discharge (shot) that occurs in duration of 21ms, online identification is hard. Since our proposed method is a numerical method, so regardless of the physics of the tokamak, we use the data set that has been used in Darestani Farahani and Abbasi Davani (2016). The identification process is performed for the available shots partially as training data and then the performance of the proposed FODNN is validated by the rest of the data.…”

Section: Damavand Tokamak and Experimental Setupmentioning

confidence: 99%

“…The identification process is performed for the available shots partially as training data and then the performance of the proposed FODNN is validated by the rest of the data. In order to compensate for the biases and to select the appropriate time interval of data in each shot, a data processing step is required for all acquired data (Darestani Farahani and Abbasi Davani, 2016;Rasouli et al, 2013). One shot of the processed data that has been used to train the network is shown in Figure 2.…”

Section: Damavand Tokamak and Experimental Setupmentioning

confidence: 99%

“…One shot of the processed data that has been used to train the network is shown in Figure 2. We need to express our sincere thanks to Darestani Farahani and Abbasi Davani (2016) for providing us with the practical data of running the system.…”

Section: Damavand Tokamak and Experimental Setupmentioning

confidence: 99%

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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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Section: Validation Of the Proposed Methods By Using Experimental Datamentioning

confidence: 99%

Section: Comparison Between the Proposed Methods Based On Fodnn And Inmentioning

confidence: 99%

Section: Damavand Tokamak and Experimental Setupmentioning

confidence: 99%

Section: Damavand Tokamak and Experimental Setupmentioning

confidence: 99%

Section: Damavand Tokamak and Experimental Setupmentioning

confidence: 99%

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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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Investigating different methods for calculating the lambda equilibrium parameter for the plasma column in the Damavand tokamak

Aghbolaghi

Farahani

Davani

2019

Fusion Engineering and Design

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Data-driven linear time advance operators for the acceleration of plasma physics simulation

2022

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We demonstrate the application of data-driven linear operator construction for time advance with a goal of accelerating plasma physics simulation. We apply dynamic mode decomposition (DMD) to data produced by the nonlinear SOLPS-ITER (Scrape-off Layer Plasma Simulator - International Thermonuclear Experimental Reactor) plasma boundary code suite in order to estimate a series of linear operators and monitor their predictive accuracy via online error analysis. We find that this approach defines when these dynamics can be represented by a sequence of approximate linear operators and is essential for providing consistent projections when compared to an unconstrained application. For linear diffusion and advection–diffusion fluid test problems, we construct and apply operators within explicit and implicit time advance schemes, demonstrating that stability can be robustly guaranteed in each case. We further investigate the use of the linear time advance operators within several integration methods including forward Euler, backward Euler, and the matrix exponential. The application of this method to simulation data from SOLPS-ITER, with varying levels of Markov chain Monte Carlo numerical noise, shows that constrained DMD operators yield a capability to identify, extract, and integrate a (slow) subset of the present timescales. Example applications show that for projected speedup factors of [Formula: see text], and [Formula: see text], a mean relative error of 3%, 5%, and 8% and maximum relative error less than 20% are achievable, which appears acceptable for typical SOLPS-ITER steady-state simulations.

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Simulation of open-loop plasma vertical movement response in the Damavand tokamak using closed-loop subspace system identification

Cited by 4 publications

References 30 publications

Identification of Damavand tokamak using fractional order dynamic neural network

Identification of Damavand tokamak using fractional order dynamic neural network

Investigating different methods for calculating the lambda equilibrium parameter for the plasma column in the Damavand tokamak

Data-driven linear time advance operators for the acceleration of plasma physics simulation

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