Surrogate modeling for optimization of actuation parameters for active drag reduction in turbulent boundary layer flows

As environmental conditions and rising energy costs pose technological and economic challenges to air transportation, aerodynamic improvements are needed to reduce energy demand, cost, and environmental impact. A promising technique to actively reduce the aerodynamic viscous drag are spanwise traveling transversal surface waves to manipulate the near-wall turbulent boundary layer. Given the flow conditions the goal is to choose the actuation parameters, such that the drag reduction and the net power savings are optimized, while other essential aerodynamic properties, such as the lift-to-drag ratio for airfoils, are neutrally or positively affected.

The partially conflicting objectives, i.e., drag reduction and net power savings, are evaluated by wall-resolved large-eddy simulations (LES). Facing enormous computational cost of LES and the design space of actuation parameters, surrogate modeling is key for surrogate-based optimization to efficiently achieve a satisfactory optimum, insightful data and interpretative knowledge. In this study, support vector regression (SVR) and Gaussian process regression (GPR) based models are analyzed and compared in terms of their effectiveness in incorporating prior knowledge and in transferring insights from one prior-dense objective to the other prior-sparse objective in terms of prediction quality.