Actuation response model from sparse data for wall turbulence drag reduction

We compute, model, and predict drag reduction of an actuated turbulent boundary layer at a momentum-thickness-based Reynolds number of Re-theta = 1000. The actuation is performed using spanwise traveling transversal surface waves parametrized by wavelength, amplitude, and period. The drag reduction for the set of actuation parameters is modeled using 71 large-eddy simulations (LESs). This drag model allows us to extrapolate outside the actuation domain for larger wavelengths and amplitudes. The modeling novelty is based on combining support vector regression for interpolation, a parametrized ridgeline leading out of the data domain, a scaling for the drag reduction, and a discovered self-similar structure of the actuation effect. The model yields high prediction accuracy outside the training data range.