SCOPE: Simple Coil Optimization for Plasma and Engineering

TL;DR

This paper presents SCOPE, a fast and efficient optimization method for designing superconducting coils in tokamaks, balancing multiple engineering and plasma containment requirements through combined simulated annealing and constrained quadratic/quartic optimization.

Contribution

It introduces a novel combined optimization approach that handles complex, multi-scenario coil design problems efficiently within a larger iterative workflow.

Findings

Optimizes coil sizes and positions for multiple plasma scenarios.

Performs millions of evaluations in a few hours with modest computational resources.

Supports iterative, detailed design feedback.

Abstract

Designing superconducting coils for a tokamak fusion device is a highly coupled, non-linear design problem. The coils have many disparate engineering requirements from structural to power electronics, as well strict limits placed on the system by the high temperature superconducting (HTS) cables. Simultaneously, the coils must be able to contain multiple plasma scenarios from inception, through ramp up, to flat top, and ramp down, all whilst applying a large, controlled, inductive voltage to drive current. In addition, we wish to optimize divertor separatrices to increase the likelihood of designing a suitable divertor strikepoint. Lastly, the physical limits of the entire tokamak must be taken into account and space reserved for support structures, access for maintenance schemes, and installation limits. The method outlined here uses a combined simulated annealing method to find optimal coil sizes and positions with a constrained quadratic or quartic optimization for the coil currents. The method is designed to optimize coils for multiple scenarios simultaneously, including ramp-ups, to avoid over optimization of a single design point. A key enabler is the efficient implementation that allows millions of evaluations to be performed in a few hours with modest computational power. This optimization method is part of a larger, iterative workflow which enables further, detailed design work to feedback on the optimization.