Automated design optimization for robust, high yield implosions
ORAL
Abstract
As we learn more about the sensitivity of recent megajoule (MJ) class implosions on the National Ignition Facility (NIF) to engineering defects and drive perturbations, there is increasing interest in exploring “robust” MJ yield designs. These implosions would be designed to explicitly maximize performance when subject to uncertain or variable conditions.
In this talk, we present simulation design studies that leverage new Bayesian optimization techniques to search for robust implosions.
We leverage neural network based Bayesian optimization methods to map out the design space around the current highest performing NIF designs and search for robust, high yield designs. With an eye toward potential facility upgrades, we explore the benefits and tradeoffs of increased energy or increased power when it comes to reproducible megajoule implosions.
This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-836711.
In this talk, we present simulation design studies that leverage new Bayesian optimization techniques to search for robust implosions.
We leverage neural network based Bayesian optimization methods to map out the design space around the current highest performing NIF designs and search for robust, high yield designs. With an eye toward potential facility upgrades, we explore the benefits and tradeoffs of increased energy or increased power when it comes to reproducible megajoule implosions.
This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-836711.
–
Presenters
-
Kelli D Humbird
Lawrence Livermore Natl Lab
Authors
-
Kelli D Humbird
Lawrence Livermore Natl Lab
-
Luc Peterson
Lawrence Livermore Natl Lab
-
Brian K Spears
Lawrence Livermore Natl Lab, LLNL, Lawrence Livermore National Laboratory, Lawrence Livemore Natl Lab