03.1 - Aerodynamics – CFD Methods and Validation
DATA-DRIVEN ADAPTATION AND OPTIMIZATION OF TURBULENT FLOWS
K.J. Fidkowski, University of Michigan, United States
We present a data-driven approach for calculating adjoint sensitivities in high-order simulations of unsteady turbulent flows, with application to shape optimization and output-based adaptation. The approach relies on using unsteady data to train a corrected turbulence model, which then yields adjoint solutions for calculating gradients and driving mesh and order adaptation. It is non-intrusive and inexpensive, requiring only a small number of unsteady forward simulations, but sufficiently powerful to capture unsteady effects in the sensitivities.