A proof-of-concept for automated AI-driven stellarator coil optimization with in-the-loop finite-element calculations

Abstract

Finding feasible coils for stellarator fusion devices is a critical challenge of realizing this concept for future power plants. Years of research work can be put into the design of even a single reactor-scale stellarator design. To rapidly speed up and automate the workflow of designing stellarator coils, we have designed an end-to-end ``runner'' for performing stellarator coil optimization. The entirety of pre and post-processing steps have been automated; the user specifies only a few basic input parameters, and final coil solutions are updated on an open-source leaderboard. Two policies are available for performing non-stop automated coil optimizations through a genetic algorithm or a context-aware LLM. Lastly, we construct a novel in-the-loop optimization of Von Mises stresses in the coils, opening up important future capabilities for in-the-loop finite-element calculations.

Figures (6)

• Figure 1: Automated code runner interactions with external codes and the genetic or LLM-proposer.
• Figure 2: Result of post-processing routines. (a) Shape gradients evaluated along final coils; (b) finite-build and Von Mises stresses; (c) coil sensitivity analysis; (d) sample Poincaré plot; (e) sample Boozer plot at $s = 0$; (f) loss fraction plot; (g) two-term quasisymmetry profile.
• Figure 3: Left: Final coilset obtained with an order-four Landreman-Paul QA coil optimization with the default parameters. Right: Same exact optimization except there is also a penalty on volume-averaged Von Mises stress above 0.01 GPa. Note that the field accuracy decreases but the maximum Von Mises stress is reduced by about an order of magnitude.
• Figure 4: Top: scatter plot showing the maximum curvature versus field accuracy achieved in the 443 optimized coilsets. Note that the values plotted here correspond to the Landreman-Paul QA equilibrium scaled to 1 m major radius. The total coil length per half-field period is plotted as well. A front forms at around $\langle \bm B\cdot \hat{\bm n}\rangle / \langle B \rangle \sim 4\times 10^{-4}$ due to the imposed threshold on the squared flux. A cluster is visible at high error magnitudes, due to the exploration policy. Bottom: one of the notable configurations from this preliminary implementation of the genetic algorithm, a 3-coil per half-field period with relatively low curvature, short coils, low field errors and high coil-to-surface distance.
• Figure 5: Convergence plots for the default settings showing (slow) convergence as the mesh resolution increases. Note the y-axes; the average displacements and average stresses do not change much in absolute terms from low to high resolution.