A Bayesian optimisation framework for skin-friction drag reduction and net energy saving in a turbulent boundary layer using low-intensity wall-normal blowing
Creators: name:  S. LAIZET 
name:   O. MAHFOZE 
name:   A. WYNN 
name:   R. WHALLEY 
name:   J. O'CONNOR 
Publisher: name: Proceedings of  the 9th European Conference for Aerospace Sciences. Lille, France, 27 June - 1 July, 2022
Year: 2022
Subjects: FLOW CONTROL
AEROSPACE

description: Skin-friction drag reduction is a topic of great interest due to its importance in many engineering applications. As an example, just a 3% reduction in the skin-friction of a long-range commercial aircraft would save 1.2m in jet fuel per aircraft per year and prevent the annual release of 3,000 tonnes of carbon dioxide. Despite many decades of extensive research, a practical and affordable method for skin-friction drag reduction is yet to be found and implemented in real-world applications. Various strategies, which include polymer additives, riblets, vibrators, micro electromechanical systems, gas micro bubbles, hydrophobic coating and large eddy breakup devices have been developed in the last decades to reduce skin-friction drag. Adding polymer additives to a liquid flow, for instance, can reduce skin-friction drag by more than 70%, yielding a phenomenon known as Maximum-Drag-Reduction. For air flows, however, the energy expenditure of typical active drag reduction strategies can be very high, often leading to energy losses even if substantial skin-friction drag reduction is obtained. Low-intensity wall-normal blowing is a very effective method to reduce shear stress and skin friction drag for turbulent wall-bounded flows. However, the energy expenditure can be high, potentially leading to energy losses despite high levels of skin-friction drag reduction. In the present work, we will show that a Bayesian optimisation framework can be used to generate net energy saving with low-intensity wall-normal blowing, using Direct and Large Eddy Simulations (DNS/LES) for a spatially evolving zero pressure gradient turbulent boundary layer [Mahfoze, O. A., Moody, A., Wynn, A., Whalley, R. D., Laizet, S. (2019). Reducing the skin-friction drag of a turbulent boundary-layer flow with low-amplitude wall-normal blowing within a Bayesian optimization framework. Physical Review Fluids, 4(9), 094601]. Bayesian optimisation is a derivative-free algorithm that works efficiently with expensive nonconvex objective functions and it plays a prominent role in efficiently optimising the parameters of machine learning algorithms, such as neural networks, with superior performance when compared to more standard approaches. It is yet to be fully exploited for use in fluid flow problems with very few studies combining DNS/LES and Bayesian optimization to date. Control performance is assessed by using the power consumption from two different sets of experimental data from two different types of blowing device, as the blowing is modelled as a boundary condition in the present simulations. The first type of blowing device was used in [Kornilov, V. I., Boiko, A. V. (2012). Efficiency of air microblowing through microperforated wall for flat plate drag reduction. AIAA journal, 50(3), 724-732] and conveyed compressed air into a plenum chamber then through a perforated plate into the incoming turbulent boundary-layer flow. The second type of blowing device was original and generated low-amplitude wall-normal blowing with a miniature electromagnetic speaker. The DNS/LES are performed with the high-order flow solver Incompact3d, which is based on high-order finite-difference schemes on a Cartesian mesh. Our Bayesian optimisation framework can be designed to maximise skin-friction drag reduction (up to 90%) or to maximise the net energy saving (up to 10%). Low-intensity wall-normal blowing can generate a skin-friction drag reduction which persists far downstream of the control region. It is the slow spatial recovery of the skin-friction coefficient back to its canonical counterpart which generates the net energy saving. One of the main results of our work is that increasing the Reynolds number improves the efficiency of the wall-normal blowing device which is potentially promising for practical applications.