Actuation response model from sparse data for wall turbulence drag reduction
Abstract
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 parameterized by wavelength, amplitude, and period. The drag reduction for the set of actuation parameters is modeled using 71 large-eddy simulations (LES). This drag model allows to extrapolate outside the actuation domain for larger wavelengths and amplitudes. The modeling novelty is based on combining support vector regression for interpolation, a parameterized ridgeline leading out of the data domain, scaling from Tomiyama and Fukagata (2013), and a discovered self-similar structure of the actuation effect. The model yields high prediction accuracy outside the training data range.
Keywords
Cite
@article{arxiv.1909.08310,
title = {Actuation response model from sparse data for wall turbulence drag reduction},
author = {Daniel Fernex and Richard Semaan and Marian Albers and Pascal S. Meysonnat and Wolfgang Schröder and Bernd R. Noack},
journal= {arXiv preprint arXiv:1909.08310},
year = {2019}
}