Gauss' Separation Algorithm for the Magnetic Field Decomposition of the SPARC PRD simulation

TL;DR

This paper applies Gauss' Separation Algorithm to SPARC fusion simulation data, enabling precise decomposition of magnetic fields from internal and external sources, especially during complex ramp-up phases, with potential for improved experimental diagnostics and equilibrium modeling.

Contribution

The work demonstrates the application of Gauss' Separation Algorithm to real fusion simulation data, showing its effectiveness in separating magnetic contributions during all shot phases, including ramp-up.

Findings

Successful separation of internal and external magnetic contributions during all phases.

Effective handling of ramp-up phase with significant passive structure currents.

Potential for improved diagnostic data analysis and equilibrium calculations.

Abstract

A magnetic separation algorithm originally introduced by Gauss has been revisited in recent years with application to magnetically-confined fusion experiments. The main result offered by Gauss' Separation Algorithm~(GSA) is a magnetic field computation that enables the decomposition of the contributions due to sources internal and external to a bounded volume through the generalized Virtual-Casing Principle. The present work applies GSA to simulations of the SPARC Primary Reference Discharge and demonstrates its separation capability on all phases of the shot, including the ramp-up during which significant currents on the passive structures often complicate the reconstruction of plasma equilibria. Such early results suggest a promising new way to study ramp-ups, disruptions, and vertical displacement events without any specific modeling assumption. Envisaged applications include reading magnetic data from experimental diagnostics and producing a new set of signals without external contributions to be used by downstream equilibrium codes for improved accuracy or speed.