Stellarator Equilibrium Reconstruction with DESC

POSTER

Abstract

We present new capabilities in DESC [2,3,4,5] for the 3D stellarator equilibrium experimental reconstruction problem. The 3D equilibrium reconstruction problem conventionally requires many expensive 3D equilibrium solves in order to acquire the derivative information necessary for matching the synthetic diagnostic signals to the measured signals [1]. DESC’s automatic differentiation enables methods that use fewer solves per reconstruction iteration, resulting in more efficient, faster optimization. Results will be shown using these capabilities to perform reconstruction and compare DESC to other reconstruction codes and literature.

[1] Hanson et. al., NF (2009).

[2] Dudt, D. & Kolemen, E. PoP (2020).

[3] Panici, D. et al. JPP (2023).

[4] Conlin, R. et al. JPP (2023).

[5] Dudt, D. et al. JPP (2023).

**This work is funded through the SciDAC program by the US Department of Energy, Office of Fusion Energy Science and Office of Advanced Scientific Computing Research under contract number DE-AC02-09CH11466, DE-SC0022005, and by the Simons Foundation/SFARI (560651).

Presenters

  • Dario Panici

    • Princeton University

Authors

  • Dario Panici

    • Princeton University

  • Rory Conlin

    • University of Maryland

  • Daniel William Dudt

    • Thea Energy

  • Yigit Elmacioglu

    • Princeton University

  • Kaya E Unalmis

    • Princeton University

  • Egemen Kolemen

    • Princeton University

  • Rahul Gaur

    • University of Wisconsin-Madison