  We developed a parallel Bayesian optimization algorithm for large eddy
simulations. These simulations challenge optimization methods because they take
hours or days to compute, and their objective function contains noise as
turbulent statistics that are averaged over a finite time. Surrogate based
optimization methods, including Bayesian optimization, have shown promise for
noisy and expensive objective functions. Here we adapt Bayesian optimization to
minimize drag in a turbulent channel flow and to design the trailing edge of a
turbine blade to reduce turbulent heat transfer and pressure loss. Our
optimization simultaneously runs several simulations, each parallelized to
thousands of cores, in order to utilize additional concurrency offered by
today's supercomputers.
