Author:
O’Connor Joseph,Diessner Mike,Wilson Kevin,Whalley Richard D.,Wynn Andrew,Laizet Sylvain
Abstract
AbstractSkin-friction drag is a major engineering concern, with wide-ranging consequences across many industries. Active flow-control techniques targeted at minimising skin friction have the potential to significantly enhance aerodynamic efficiency, reduce operating costs, and assist in meeting emission targets. However, they are difficult to design and optimise. Furthermore, any performance benefits must be balanced against the input power required to drive the control. Bayesian optimisation is a technique that is ideally suited to problems with a moderate number of input dimensions and where the objective function is expensive to evaluate, such as with high-fidelity computational fluid dynamics simulations. In light of this, this work investigates the potential of low-intensity wall-normal blowing as a skin-friction drag reduction strategy for turbulent boundary layers by combining a high-order flow solver () with a Bayesian optimisation framework. The optimisation campaign focuses on streamwise-varying wall-normal blowing, parameterised by a cubic spline. The inputs to be optimised are the amplitudes of the spline control points, whereas the objective function is the net-energy saving (NES), which accounts for both the skin-friction drag reduction and the input power required to drive the control (with the input power estimated from real-world data). The results of the optimisation campaign are mixed, with significant drag reduction reported but no improvement over the canonical case in terms of NES. Selected cases are chosen for further analysis and the drag reduction mechanisms and flow physics are highlighted. The results demonstrate that low-intensity wall-normal blowing is an effective strategy for skin-friction drag reduction and that Bayesian optimisation is an effective tool for optimising such strategies. Furthermore, the results show that even a minor improvement in the blowing efficiency of the device used in the present work will lead to meaningful NES.
Funder
Engineering and Physical Sciences Research Council
Publisher
Springer Science and Business Media LLC
Subject
Physical and Theoretical Chemistry,General Physics and Astronomy,General Chemical Engineering
Reference51 articles.
1. Abbas, A., Bugeda, G., Ferrer, E., et al.: Drag reduction via turbulent boundary layer flow control. Sci. China Technol. Sci. 60(9), 1281–1290 (2017)
2. Atzori, M., Vinuesa, R., Fahland, G., et al.: Aerodynamic effects of uniform blowing and suction on a NACA4412 airfoil. Flow Turbul. Combust. 105(3), 735–759 (2020)
3. Bartholomew, P., Deskos, G., Frantz, R., et al.: Xcompact3D: An open-source framework for solving turbulence problems on a Cartesian mesh. SoftwareX 12(100), 550 (2020)
4. Brochu, E., Cora, V., de Freitas, N.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv Preprint arXiv:1012.2599 [cs.LG] (2010)
5. Choi, H., Moin, P., Kim, J.: Direct numerical simulation of turbulent flow over riblets. J. Fluid Mech. 255, 503–539 (1993)
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