Towards the Use of Anderson Acceleration in Coupled Transport-Gyrokinetic Turbulence Simulations

TL;DR

This paper explores the integration of Anderson Acceleration into the Tango transport solver to improve convergence speed and robustness in long-term magnetically confined fusion plasma simulations, including noisy scenarios.

Contribution

It demonstrates the effective use of Anderson Acceleration with Tango, utilizing KINSOL and GPTune for rapid experimentation and parameter optimization, enhancing convergence and robustness.

Findings

AA accelerates convergence in stiff test cases

AA improves robustness with noisy fluxes

Parameter tuning via GPTune enhances performance

Abstract

Predicting the behavior of a magnetically confined fusion plasma over long time periods requires methods that can bridge the difference between transport and turbulent time scales. The nonlinear transport solver, Tango, enables simulations of very long times, in particular to steady state, by advancing each process independently with different time step sizes and couples them through a relaxed iteration scheme. We examine the use of Anderson Acceleration (AA) to reduce the total number of coupling iterations required by interfacing Tango with the AA implementation, including several extensions to AA, provided by the KINSOL nonlinear solver package in SUNDIALS. The ability to easily enable and adjust algorithmic options through KINSOL allows for rapid experimentation to evaluate different approaches with minimal effort. Additionally, we leverage the GPTune library to automate the optimization of algorithmic parameters within KINSOL. We show that AA can enable faster convergence in stiff and very stiff tests cases without noise present and in all cases, including with noisy fluxes, increases robustness and reduces sensitivity to the choice of relaxation strength.