用于气动表面减阻凹坑的贝叶斯优化与地形探索
Bayesian optimization and topographic exploration of drag-reducing dimples for aerodynamic surfaces
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中文总结 AI 辅助
本研究采用MixMOBO贝叶斯优化器结合大涡模拟，优化气动表面减阻凹坑的四个设计变量，得到13.2%减阻率的最优菱形凹坑，明确了凹坑拓扑等关键设计因素及流动机制。
中文摘要 AI 辅助
凹坑为降低气动表面阻力提供了极具前景的途径，但这类浅凹是否能产生净效益，高度依赖于其地形，这要求对其完整设计空间进行系统的映射与探索。本研究将凹坑设计视为包含四个设计变量的混合变量优化问题：凹坑类型、深度、平面内尺度与流向拉伸。采用贝叶斯优化器MixMOBO，结合浸入边界法的大涡模拟，对设计空间进行探索，模拟在对应摩擦雷诺数Reτ=180的平直通道、恒定流量的通道流中开展。最优解为相对较深、完全填充、流向拉伸的菱形凹坑，可实现13.2%的减阻率，显著高于此前报道的数值。高斯过程元模型敏感性分析表明，凹坑拓扑是主导因素，覆盖率与拉伸度主要通过交互作用产生影响，而深度本身并无统一的影响方向。近壁流分析显示，最优设计对应完全附着的类沟槽流动，而较差的设计往往会产生局部流动分离，引发不利的形状阻力。基于这些发现，本研究为减阻凹坑提供了关键的设计见解。
英文摘要
Dimples offer a promising route to reducing drag on aerodynamic surfaces. However, whether such shallow concavities yield a net benefit depends sensitively on their topography, which demands systematic mapping and exploration of their comprehensive design space. In this study, dimple design is examined as a mixed-variable optimization over four design variables: dimple type, depth, in-plane scale, and streamwise stretch. The design space is explored using MixMOBO, a Bayesian optimizer, coupled with immersed-boundary large eddy simulations of channel flows at a constant flow rate corresponding to a flat channel at a friction Reynolds number of 180. The optimal solution, a relatively deep, fully packed, streamwise-elongated diamond dimple, attains a 13.2% drag reduction, notably above previously reported values. A Gaussian process metamodel sensitivity analysis identifies dimple topology as the dominant factor, with coverage and elongation acting mainly through interactions, and depth itself carrying no universal sign. A near-wall flow analysis links the leading designs to fully attached, groove-like flow, whereas poorer designs tend to produce local flow separation that incurs adverse form drag. From these findings, key design insights for drag-reducing dimples are provided.
