Accelerating the estimation of energetic particle confinement statistics in stellarators using multifidelity Monte Carlo

TL;DR

This paper introduces a multifidelity Monte Carlo method to efficiently estimate energetic particle confinement in stellarators, significantly reducing computational costs while maintaining accuracy.

Contribution

It presents the first multifidelity Monte Carlo scheme using a guiding center model and a data-driven surrogate for stellarator confinement analysis.

Findings

Achieved up to 10x speedup over standard Monte Carlo methods.

Validated the surrogate model's high correlation with high-fidelity simulations.

Demonstrated effectiveness in a quasi-helically symmetric stellarator.

Abstract

In the design of stellarators, energetic particle confinement is a critical point of concern which remains challenging to study from a numerical point of view. Standard Monte Carlo analyses are highly expensive because a large number of particle trajectories need to be integrated over long time scales, and small time steps must be taken to accurately capture the features of the wide variety of trajectories. Even when they are based on guiding center trajectories, as opposed to full-orbit trajectories, these standard Monte Carlo studies are too expensive to be included in most stellarator optimization codes. We present the first multifidelity Monte Carlo scheme for accelerating the estimation of energetic particle confinement in stellarators. Our approach relies on a two-level hierarchy, in which a guiding center model serves as the high-fidelity model, and a data-driven linear interpolant is leveraged as the low-fidelity surrogate model. We apply multifidelity Monte Carlo to the study of energetic particle confinement in a 4-period quasi-helically symmetric stellarator, assessing various metrics of confinement. Stemming from the very high computational efficiency of our surrogate model as well as its sufficient correlation to the high-fidelity model, we obtain speedups of up to 10 with multifidelity Monte Carlo compared to standard Monte Carlo.