Optimisation and Analysis of Streamwise-Varying Wall-Normal Blowing in a Turbulent Boundary Layer
Optimisation and Analysis of Streamwise-Varying Wall-Normal Blowing in a Turbulent Boundary Layer
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DOI：
10.1007/s10494-023-00408-3
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发表时间：
2023-03
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影响因子：
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通讯作者：
Joseph O’Connor;Mike Diessner;Kevin Wilson;R. Whalley;A. Wynn;S. Laizet
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作者：
Joseph O’Connor;Mike Diessner;Kevin Wilson;R. Whalley;A. Wynn;S. Laizet
表面摩擦阻力是一个主要的工程问题，在许多行业中具有广泛的影响。主动流动控制技术的目标是最大限度地减少表面摩擦，有可能显着提高气动效率，降低运营成本，并有助于满足排放目标。然而，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于具有中等数量输入维度的问题的技术，并且目标函数的评估成本很高，例如高保真计算流体动力学模拟。鉴于此，这项工作调查的潜力，低强度的壁面法向吹气作为表面摩擦阻力减少湍流边界层的策略相结合的高阶流求解器（Incompact3d）与贝叶斯优化框架。优化活动的重点是沿流向变化的壁面法向吹扫，由三次样条参数化。待优化的输入是样条控制点的振幅，而目标函数是净节能（内斯），其考虑了表面摩擦阻力减小和驱动控制所需的输入功率（输入功率从真实世界数据估计）。优化活动的结果是混合的，与显着的阻力减少报告，但没有改善的典型情况下，在内斯。选定的情况下，选择进一步的分析和减阻机制和流动物理突出。结果表明，低强度的壁面法向吹气是一种有效的减阻策略，贝叶斯优化是优化这种策略的有效工具。此外，结果表明，即使在本工作中使用的设备的吹气效率的微小改进将导致有意义的内斯。
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.