Reducing the skin-friction drag of a turbulent boundary-layer flow with low-amplitude wall-normal blowing within a Bayesian optimization framework

Abstract

A Bayesian optimisation framework is developed to optimise low-amplitude wall-normal blowing control of a turbulent boundary-layer flow. The Bayesian optimisation framework determines the optimum blowing amplitude and blowing coverage to achieve up to a 5% net-power saving solution within 20 optimisation iterations, requiring 20 Direct Numerical Simulations (DNS). The power input required to generate the low-amplitude wall-normal blowing is measured experimentally for two different types of blowing device, and is used in the simulations to assess control performance. Wall-normal blowing with amplitudes of less than 1% of the free-stream velocity generate a skinfriction drag reduction of up to 76% over the control region, with a drag reduction which persists for up to 650δ 0 downstream of actuation (where δ 0 is the boundary-layer thickness at the start of the simulation domain). It is shown that it is the slow spatial recovery of the turbulent boundary-layer flow downstream of control which generates the net-power savings in this study. The downstream recovery of the skin-friction drag force is decomposed using the Fukagata-Iwamoto-Kasagi (FIK) identity, which shows that the generation of the net-power savings is due to changes in contributions to both the convection and streamwise development terms of the turbulent boundary-layer flow.

Aerodynamic Effects of Uniform Blowing and Suction on a NACA4412 Airfoil

“…Following this observation, Mahfoze et al (2019) employed Bayesian optimization to identify the best combination of control-region length and blowing amplitude to maximize energy-saving, also including intermittent control regions. These authors also took into account the pressure measurements across the perforated plate in the experiment performed by Kornilov and Boiko (2012) to formulate a more realistic estimate of the power consumption by blowing, and they confirmed that it is possible to obtain a net-energy saving in the range of few percent.…”

Decomposition of the mean friction drag on an NACA4412 airfoil under uniform blowing/suction

“…A slight increase of C f ,P is observed near the trailing edge for the blowing cases (see figure 3c), which is associated with the fact that the boundary layer is approaching the condition of mean separation (Atzori et al 2020). Generally, the positive variation of C f ,D is overcome by the negative influence on C f ,C and C f ,P , which consequently leads to the overall reduction of C f ,G by blowing (Mahfoze et al 2019), as shown in figures 2(c) and 2( f ). In § 3.2, we will further analyse the wall-normal distributions of these friction constituents to better relate them to control-induced changes of the boundary layer properties.…”

“…Kornilov, Kavun & Popkov (2019) employed blowing on the pressure side and suction on the suction side of an NACA0012 airfoil, and later they provided an estimation of the control energy cost under the same conditions (Kornilov 2021). Mahfoze et al (2019) used Bayesian optimization to discuss how to benefit from downstream effects of blowing when the control region is separated into individual areas. The first high-fidelity numerical simulation of a wing section with uniform blowing was reported by Vinuesa & Schlatter (2017), albeit at a low Reynolds number (Re c = 100 000).…”

Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning

“…The GP approach has been successfully employed for the control of external flows by Li et al [101] and Minelli et al [102]. Another interesting data-driven approach is Bayesian regression based on Gaussian processes [103], which was employed by Morita et al [104] in CFD optimization, and by Mahfoze et al [105] to identify the best combination of control region length and blowing amplitude to maximize the energy savings, also including intermittent control regions. Note that these authors also took into account the data by Kornilov and Boiko [106] to formulate a more realistic estimate of the power consumption by blowing, and they reported a net-energy saving of around 5%.…”