Actuation response model from sparse data for wall turbulence drag reduction

TL;DR

This paper develops a predictive model for drag reduction in turbulent boundary layers actuated by spanwise traveling waves, using sparse LES data and advanced regression techniques to enable extrapolation beyond the training domain.

Contribution

It introduces a novel drag reduction model combining support vector regression, a parameterized ridgeline, and self-similar scaling, allowing accurate predictions outside the data domain.

Findings

High prediction accuracy outside training data range

Effective modeling of actuation effects using sparse LES data

Extrapolation capability for larger wavelengths and amplitudes

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.