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We compute, model, and predict drag reduction of an actuated turbulentboundary layer at a momentum thickness based Reynolds number of Re{\theta} =1000. The actuation is performed using spanwise traveling transversal surfacewaves parameterized by wavelength, amplitude, and period. The drag reductionfor the set of actuation parameters is modeled using 71 large-eddy simulations(LES). This drag model allows to extrapolate outside the actuation domain forlarger wavelengths and amplitudes. The modeling novelty is based on combiningsupport vector regression for interpolation, a parameterized ridgeline leadingout of the data domain, scaling from Tomiyama and Fukagata (2013), and adiscovered self-similar structure of the actuation effect. The model yieldshigh prediction accuracy outside the training data range.

Actuation response model from sparse data for wall turbulence drag reduction