Experimental characterization and modelling of the resistive wall mode response in a reversed field pinch
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 (RFP). The experimental characterization of...
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Veröffentlicht in: | Plasma physics and controlled fusion 2022-05, Vol.64 (5), p.55011 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | 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 (RFP). The experimental characterization of the RWM plasma response is performed in the EXTRAP T2R device by the excitation of nonaxisymmetric 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
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modes for this high aspect ratio RFP device. In addition to the nonresonant unstable modes, which are the primary target of RWM feedback control, this spectrum also includes a wide range of resonant modes. The validated resistive magnetohydrodynamics (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 that observed in earlier studies using the ideal MHD model. The present model provides a realistic description of the plasma response for both nonresonant and resonant modes, which is both relatively simple and compatible with the computing capabilities and latency limitations encountered in practical implementations of model-based control algorithms. |
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ISSN: | 0741-3335 1361-6587 1361-6587 |
DOI: | 10.1088/1361-6587/ac5cf9 |