The design of turbomachinery creates a strong demand for the simultaneous optimization of multiple blade rows with regard to different disciplines including aerodynamics, aeroelasticity, and solid mechanics. Established gradient-free methods, typically surrogatebased methods, have been successfully applied to the optimization of single blade rows and pairs of adjacent rows, typically featuring in the order of 50 design variables per blade row. Gradient-free methods become inhibitively expensive through the increased number of design variables from simultaneous optimizations of many rows. Gradients obtained from adjoint simulations can help in transitioning to larger design spaces as they provide derivatives with respect to each design variable at a computational cost that only depends on the number of objectives. For the transition from gradient-free to gradientbased optimizations, a variety of challenges had to be solved, which will be outlined in this paper.
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