Impact of model uncertainty on SPARC operating scenario predictions with empirical modeling

TL;DR

This paper introduces statistical methods and Bayesian optimization to quantify and incorporate uncertainties in plasma modeling, leading to more robust predictions for SPARC tokamak operation.

Contribution

It develops statistical POPCONs with Monte Carlo analysis and a multi-fidelity Bayesian workflow to improve operating point predictions under uncertainty.

Findings

Uncertainty significantly affects optimal operating points.

Bayesian optimization accelerates the search for performance-maximizing conditions.

Accounting for uncertainties shifts the predicted optimal operating point.

Abstract

Understanding and accounting for uncertainty helps to ensure next-step tokamaks such as SPARC will robustly achieve their goals. While traditional Plasma OPerating CONtour (POPCON) analyses guide design, they often overlook the significant impact of uncertainties in scaling laws, plasma profiles, and impurity concentrations on performance predictions. This work confronts these challenges by introducing statistical POPCONs, which leverage Monte Carlo analysis to quantify the sensitivity of SPARC's operating points [1] to these crucial variables. For profiles, a physically motivated gradient-based functional form is introduced. We further develop a multi-fidelity Bayesian optimization workflow that effectively identifies operating points maximizing the probability of meeting performance goals, which gives a significant speed-up over brute force methods. Our findings reveal that accounting for these uncertainties leads to an optimal operating point different from deterministic predictions, which balances H-mode access, confinement, impurity dilution, and auxiliary power.