Speaker
Description
Despite the existence of physics-based turbulent transport models, new tokamaks have historically initially been designed using empirical scaling laws due to the large computational expense of physics-based models. However, these empirical models do not capture the full changes caused by alterations to the plasma composition and geometry. Here, we optimize the ARC tokamak (Howard, et al., JPP, Submitted 2025) with respect to effective charge state (Zeff), main ion fraction (fmain), pedestal density (neped), elongation (κ), triangularity (δ), and squareness (ζ). Modeling is performed using MAESTRO, an integrated modeling tool using TGLF as the transport model (Staebler, NF, 2021) and EPED for pedestal predictions (Snyder, NF, 2011). We find increasing the amount of impurity in the plasma can increase the fusion power performance. Bayesian optimization is employed to expedite the process of finding the best operating point with relatively expensive physics-based models. In the future, we will increase the number of free parameters, pushing towards enabling design work to start from physics-based turbulent transport models.
Acknowledgements: This work is supported by Commonwealth Fusion Systems, under RPP020.
| Country or International Organisation | United States of America |
|---|---|
| Affiliation | MIT PSFC |
| Speaker's email address | audreysa@mit.edu |
