Surrogate Approximation of the Grad-Shafranov Free Boundary Problem via Stochastic Collocation on Sparse Grids

TL;DR

This paper develops a surrogate model using sparse grid stochastic collocation to efficiently analyze how uncertainties in coil currents affect plasma boundary features in magnetic confinement fusion devices.

Contribution

It introduces a surrogate approximation method for the free boundary problem in plasma equilibrium, significantly reducing computational costs in stochastic analysis.

Findings

Surrogate model reduces Monte Carlo simulation time by factors of 7 to 30.

Accurately captures the impact of coil current variability on plasma boundary features.

Enables efficient uncertainty quantification in fusion device design.

Abstract

In magnetic confinement fusion devices, the equilibrium configuration of a plasma is determined by the balance between the hydrostatic pressure in the fluid and the magnetic forces generated by an array of external coils and the plasma itself. The location of the plasma is not known a priori and must be obtained as the solution to a free boundary problem. The partial differential equation that determines the behavior of the combined magnetic field depends on a set of physical parameters (location of the coils, intensity of the electric currents going through them, magnetic permeability, etc.) that are subject to uncertainty and variability. The confinement region is in turn a function of these stochastic parameters as well. In this work, we consider variations on the current intensities running through the external coils as the dominant source of uncertainty. This leads to a parameter space of dimension equal to the number of coils in the reactor. With the aid of a surrogate function built on a sparse grid in parameter space, a Monte Carlo strategy is used to explore the effect that stochasticity in the parameters has on important features of the plasma boundary such as the location of the x-point, the strike points, and shaping attributes such as triangularity and elongation. The use of the surrogate function reduces the time required for the Monte Carlo simulations by factors that range between 7 and over 30.