Exploring robust, high yield ICF designs using Bayesian optimization

Inertial confinement fusion (ICF) experiments rely on complex multi-physics simulations such as the LLNL-developed HYDRA to guide design work. However, these simulations can be expensive and have several dozen design parameters, making the search for an optimal design difficult. Recently developed automated tools utilize multi-fidelity Bayesian optimization to search these high-dimensional design spaces for candidate experiments.



In this project, we tune the Bayesian optimization algorithm to optimize ICF designs for robustness and high yield. The starting point for this search is an asymmetrical implosion found by Peterson et al. [1]. This “ovoid” design uses vortical flows to suppress instabilities along the capsule surface during implosion. We present the computational approach to further optimize and investigate the ovoid implosions. The optimization tools run 2D integrated simulations in HYDRA to converge on an optimal design. We extract valuable physics information by running detailed simulations of the optimal design and designs in its vicinity.

[1] Peterson, J. L., et al., Phys. Plasmas 24, 032702 (2017)

LLNL-ABS-866070