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The Plasma Boundary and its Identification

Part of the Advances in Industrial Control book series (AIC)

Abstract

As seen in the previous chapter, the magnetic confinement of the plasma in tokamak machines is obtained via the interaction of the plasma with an external electromagnetic field. Unfortunately, when high elongation of the plasma column is required, in other words when the plasma vertical dimension is larger than the radial dimension, then the equilibrium configuration between the plasma column and the external field is unstable, i.e., small perturbations from the equilibrium may cause large movements of the plasma. Hence the use of a feedback system for position and shape control becomes mandatory. To this end, measurement of the geometrical parameters is necessary to perform feedback control. Since some of these parameters are not directly measurable, they must be estimated, starting from the set of available data. This chapter focuses on this estimation problem. To start with, the plasma boundary is precisely defined; it will be shown that the plasma boundary is a closed curve lying on the poloidal plane, corresponding to a constant level curve of the poloidal flux function. In principle an infinite number of points are necessary to completely characterize this curve from a geometrical point of view; however it turns out that a finite number of parameters can be used to control the overall plasma boundary effectively; these plasma boundary descriptors are introduced in Section 3.2. As already said, estimation of the plasma boundary descriptors has to be carried out using the available magnetic measurements. For this reason in Section 3.3 the magnetic sensors that are usually available in a modern tokamak are described. Finally, a general algorithm that can be used to solve this estimation problem is discussed.

Keywords

Eddy Current Plasma Column Geometrical Descriptor Magnetic Sensor Plasma Boundary 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer London 2008

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