Quantifying and Reducing Uncertainty in Inertial Confinement Fusion Experiments Using Optimal Experimental Design

Hydrodynamic instabilities are a major degradation mechanism in inertial confinement fusion (ICF) experiments, as they lead to asymmetry in target compression as well as mixing of “cold” material into the central hot spot. Understanding the growth and properties of hydrodynamic instabilities and the transition into turbulence is important in many areas of research however, examining hydrodynamic instabilities in a high energy density (HED) regime, such as ICF experiments, poses novel challenges.

These experiments are expensive and often performed at oversubscribed facilities. Additionally, there are many limitations for the available diagnostics and experiments are typically multi-physics in nature. Such complexity means that modeling can become prohibitively expensive. Improving the models from limited experimental and high-fidelity simulation data is therefore of great importance.

We will perform Bayesian inference to quantify and reduce uncertainty of model parameters inferred from RM and RT experiments. Additionally, we will use optimal experimental design (OED) techniques to better understand how our initial conditions impact our results and identify the most informative experimental set ups for a given experimental goal.