Enhancing predictive capabilities in fusion burning plasmas through surrogate-based optimization in core transport solvers

TL;DR

This paper introduces PORTALS, a surrogate modeling framework that significantly accelerates the prediction of core plasma profiles in fusion reactors without sacrificing accuracy, enabling more efficient plasma performance optimization.

Contribution

The paper presents a novel surrogate-based optimization framework, PORTALS, for fast and accurate prediction of plasma profiles using nonlinear gyrokinetic simulations.

Findings

PORTALS achieves high accuracy comparable to full simulations.

Benchmarking shows substantial reduction in computational cost.

Application to DIII-D plasma demonstrates practical effectiveness.

Abstract

This work presents the PORTALS framework, which leverages surrogate modeling and optimization techniques to enable the prediction of core plasma profiles and performance with nonlinear gyrokinetic simulations at significantly reduced cost, with no loss of accuracy. The efficiency of PORTALS is benchmarked against standard methods, and its full potential is demonstrated on a unique, simultaneous 5-channel (electron temperature, ion temperature, electron density, impurity density and angular rotation) prediction of steady-state profiles in a DIII-D ITER Similar Shape plasma with GPU-accelerated, nonlinear CGYRO. This paper also provides general guidelines for accurate performance predictions in burning plasmas and the impact of transport modeling in fusion pilot plants studies.