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Title: Magnetic flux reconstruction methods for shaped tokamaks

Thesis/Dissertation ·
DOI:https://doi.org/10.2172/10117754· OSTI ID:10117754
 [1]
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

The use of a variational method permits the Grad-Shafranov (GS) equation to be solved by reducing the problem of solving the 2D non-linear partial differential equation to the problem of minimizing a function of several variables. This high speed algorithm approximately solves the GS equation given a parameterization of the plasma boundary and the current profile (p` and FF` functions). The author treats the current profile parameters as unknowns. The goal is to reconstruct the internal magnetic flux surfaces of a tokamak plasma and the toroidal current density profile from the external magnetic measurements. This is a classic problem of inverse equilibrium determination. The current profile parameters can be evaluated by several different matching procedures. Matching of magnetic flux and field at the probe locations using the Biot-Savart law and magnetic Green`s function provides a robust method of magnetic reconstruction. The matching of poloidal magnetic field on the plasma surface provides a unique method of identifying the plasma current profile. However, the power of this method is greatly compromised by the experimental errors of the magnetic signals. The Casing Principle provides a very fast way to evaluate the plasma contribution to the magnetic signals. It has the potential of being a fast matching method. The performance of this method is hindered by the accuracy of the poloidal magnetic field computed from the equilibrium solver. A flux reconstruction package has been implemented which integrates a vacuum field solver using a filament model for the plasma, a multi-layer perception neural network as an interface, and the volume integration of plasma current density using Green`s functions as a matching method for the current profile parameters. The flux reconstruction package is applied to compare with the ASEQ and EFIT data. The results are promising.

Research Organization:
Massachusetts Inst. of Tech., Cambridge, MA (United States). Plasma Fusion Center
Sponsoring Organization:
USDOE
DOE Contract Number:
AC02-78ET51013
OSTI ID:
10117754
Report Number(s):
DOE/ET/51013-304; PFC/RR-93-05; ON: DE94005827; TRN: 94:004516
Resource Relation:
Other Information: TH: Thesis (Ph.D.); PBD: Dec 1993
Country of Publication:
United States
Language:
English