Skin-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 (Incompact3d) 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.

皮肤摩擦阻力是一个主要的工程问题，对许多行业都有广泛的影响。以最小化表面摩擦为目标的主动流量控制技术有可能显着提高空气动力学效率，降低运营成本，并有助于实现排放目标。但是，它们很难设计和优化。此外，任何性能优势都必须与驱动控制所需的输入功率相平衡。贝叶斯优化是一种非常适合输入维度数量适中且目标函数评估成本高昂的问题的技术，例如高保真计算流体动力学模拟。有鉴于此，Incompact3d) 与贝叶斯优化框架。优化活动侧重于流向变化的壁法向吹气，由三次样条参数化。要优化的输入是样条控制点的幅度，而目标函数是净节能 (NES)，它考虑了表面摩擦阻力的减少和驱动控制所需的输入功率（使用根据实际数据估计的输入功率）。优化活动的结果喜忧参半，据报道阻力显着减少，但在 NES 方面与典型情况相比没有改善。选择选定的案例进行进一步分析，并突出显示减阻机制和流动物理学。结果表明，低强度壁法向吹气是减少表面摩擦阻力的有效策略，贝叶斯优化是优化此类策略的有效工具。此外，结果表明，即使是目前工作中使用的设备的吹气效率的微小改进也会导致有意义的 NES。