Stellarator Equilibrium Reconstruction Capabilities with DESC

POSTER

Abstract

We present newly-implemented capabilities in DESC [2,3,4,5] for solving 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 use of automatic differentiation (AD) reveals the necessary derivative information with a single equilibrium solve, advantageous for more quickly solving the reconstruction problem. Functionality for calculating synthetic magnetic diagnostic signals, similar to the DIAGNO 2.0 code [6], has been implemented in DESC with AD-compatibility, and is verified against analytic test cases. Results will be shown using these capabilities to perform reconstruction and compare the DESC result to various examples from the 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).

[6] Lazerson et al. PPCF (2013).

*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 No. DE-AC02-09CH11466, as well as DE-SC0022005, and Field Work Proposal No. 1019

Presenters

  • Dario Panici

    • Princeton University

Authors

  • Dario Panici

    • Princeton University
  • Rory Conlin

    • Princeton University
    • University of Maryland
  • Daniel William Dudt

    • Thea Energy
  • Kian Orr

    • Princeton University
  • Yigit Elmacioglu

    • Princeton University
  • Rahul Gaur

    • Princeton Univeristy
  • Egemen Kolemen

    • Princeton University