Subspace identification analysis of RFX and T2R reversed-field pinches

Olofsson, Karl; Soppelsa, A.; Bolzonella, T.; Marchiori, G.

doi:10.1016/j.conengprac.2013.03.004

Cited by 12 publications

(5 citation statements)

References 36 publications

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“…The use of closed-loop system identification techniques to enable experimental modal analysis derived solely from empirical data was extended in an experimental study in RFX-mod [547]. Contrary to EXTRAP-T2R, the stabilization diagram for RFX-mod showed unstable activity in the eigenvalue range corresponding to tearing modes.…”

Section: Experimental Demonstrations Of Rwm Stabilizationmentioning

confidence: 99%

The reversed field pinch

et al. 2021

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This paper reviews the research on the reversed field pinch (RFP) in the last three decades. Substantial experimental and theoretical progress and transformational changes have been achieved since the last review (Bodin 1990 Nucl. Fusion 30 1717–37). The experiments have been performed in devices with different sizes and capabilities. The largest are RFX-mod in Padova (Italy) and MST in Madison (USA). The experimental community includes also EXTRAP-T2R in Sweden, RELAX in Japan and KTX in China. Impressive improvements in the performance are the result of exploration of two lines: the high current operation (up to 2 MA) with the spontaneous occurrence of helical equilibria with good magnetic flux surfaces and the active control of the current profile. A crucial ingredient for the advancements obtained in the experiments has been the development of state-of-art active feedback control systems allowing the control of MHD instabilities in presence of a thin shell. The balance between achievements and still open issues leads us to the conclusion that the RFP can be a valuable and diverse contributor in the quest for fusion electricity.

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Section: Experimental Demonstrations Of Rwm Stabilizationmentioning

confidence: 99%

The reversed field pinch

et al. 2021

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“…While data-driven methods have been utilized in many contexts and for many purposes-such as for identifying error estimates in sophisticated validation studies using traditional physics simulations models [379,380], as well as being used in semi-empirical methods [381], stabilization analysis [382], the development of plasma stability control techniques [383], discharge control systems [384], deep statistical inference models on experimental data [385][386][387], as well as feedback control schemes [388]many of these techniques are frequently considered more empirical than physics-based.…”

Section: B Data-driven Physics Modelsmentioning

confidence: 99%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

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Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasmaprocessing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. CONTENTSI. Introduction 6 II. Fundamental Data Science 6 A. Introduction 6 B. Data Reduction/Compression 7 C. Dimensional reduction and sparse modeling 8 D. ML-enhanced modeling and simulation 9 E. ML Hardware and integration with models 10 F. Workflow Automation 11 G. Uncertainty Quantification 12 H. Visualization and Data Understanding 13 I. ML Control Theory 15 III. Basic Plasma Physics and Laboratory Experiments A. Introduction B. Spectroscopy, imaging and tomography C. Sparse measurement and noise D. Synthetic instruments and data E. Experimental data visualization F. High-rep rate laser experiments G. Charged particle beams H. Control and Optimisation of Plasma Accelerator Experiments I. Dusty and complex plasmas J. Physics and machine learning K. Challenges and outlook 5 IV. Magnetic Confinement Fusion 36 A. Introduction 36 B. Data-Driven Physics Models 36 C. Optimizing experimental workflows with data-driven methods 37 D. Diagnostics and Fusion Data Streams 38 E. Prediction of Tokamak Disruption 39 F. Surrogate models of fusion plasma 40 G. Magnetic Fusion Energy Data Challenges and Solutions 41 H. Data Science for extreme scale simulation 43 I. Challenges and outlook 44 V. Inertial confinement fusion and high-energy-density physics 45 A. Introduction 45 B. Representation learning for multimodal data 45 C. Transfer learning for simulation and experimen 47 D. Uncertainty quantification and Bayesian inference 47 E. High-performance computing and simulation acceleration 49 F. Design exploration and optimization 49 G. Self-driving experimental facilities 51 H. Challenges and outlook 52 VI. Space and astronomical plasmas 52 A. Introduction 52 B. Space and ground instruments 53 C. Space weather prediction 55 D. Transfer learning to improve historic data 56 E. Surrogate models of fluid closures using machine learning 56 F. Magnetic reconnection 58 G. Challenges and outlook 60 VII. Plasma tec...

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“…Control models for RWM feedback stabilization are useful in order to optimize control parameters in traditional algorithms, such as 'proportional-integral-derivative' regulators and for implementing modern model-based control algorithms. A method to obtain empirical linear control models using the superposition of a random low amplitude 'dithering' signal to the control system output during feedback operation has been developed and implemented at EXTRAP T2R and RFX-mod [26,27]. The linear models obtained in this way have then been utilized for implementation of the 'model predictive control', initially tested at EXTRAP T2R [28].…”

Section: Introductionmentioning

confidence: 99%

Experimental characterization and modelling of the resistive wall mode response in a reversed field pinch

Saad

Brunsell

2022

Plasma Phys. Control. Fusion

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Model-based control algorithms have potential advantages for Resistive Wall Mode (RWM) feedback control. In this study, a physics model of the RWM response to externally applied perturbation fields is validated against experiments in a reversed field pinch. The experimental characterization of the RWM plasma response is performed in the EXTRAP T2R device by excitation of non-axisymmetric perturbation magnetic fields utilizing an external array of saddle coils for RWM control. The modelling and experimental validation is carried out with an extended sensor array, resolving a wider spectrum of RWM compared to earlier studies, covering the relevant poloidal m=1 and toroidal -32<n<32 modes for this high aspect ratio RFP device. In addition to the non-resonant unstable modes, which are the primary target of RWM feedback control, this spectrum includes also a wide range of resonant modes. The validated resistive MHD model includes the passive stabilization effect on these modes from intrinsic plasma rotation. The inclusion of resistivity and plasma rotation in the present model provides a substantially better agreement between modelled and experimental growth rates than observed in earlier studies using the ideal MHD model. The present model provides a realistic description of the plasma response for both non-resonant and resonant modes, which is yet relatively simple and compatible with computing capabilities and latency limitations encountered in practical implementations of model based control algorithms.

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Subspace identification analysis of RFX and T2R reversed-field pinches

Cited by 12 publications

References 36 publications

The reversed field pinch

The reversed field pinch

2022 Review of Data-Driven Plasma Science

Experimental characterization and modelling of the resistive wall mode response in a reversed field pinch

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