发表机构
Tohoku University; Future University Hakodate（东北大学; 函馆未来大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过直接数值模拟和机器学习，结合同步理论自编码器，分析圆柱与翼型在雷诺数100下的相互作用，识别涡脱落抑制的振荡死亡现象，并预测流动稳定性。
AI 中文摘要
物体间相互作用长期以来一直被研究以提升各种流体机械的性能。本研究通过直接数值模拟和机器学习分析了圆柱与NACA0012翼型之间的流动相互作用。我们考虑了雷诺数为100的二维不可压缩流动，其中翼型位于圆柱的尾流中。通过扫描由圆柱与翼型之间的距离$\Delta x$、相对高度$\Delta y$以及翼型攻角$\alpha$组成的参数空间，观察到了四种特征性的涡脱落状态。本研究特别关注在特定排列下抑制的涡脱落，将其视为耦合振荡器中的振荡死亡，并在机器学习得到的低阶坐标上对流动稳定性进行理论预测。每个物体周围的独立流动被视为一个系统，在无限距离处表现出孤立的周期性涡脱落，而相互作用在较小距离处出现。我们采用基于同步理论的自编码器从气动系数中推导出两个耦合振荡器的模型。所提出的方法能够评估系统稳定性并预测涡相互作用行为，可能为从数据驱动和同步视角分析非定常物体间相互作用铺平道路。
英文摘要
Body-body interactions have long been investigated to leverage the performance of various fluid-based machines. This study analyzes the flow interaction between a cylinder and a NACA0012 airfoil using direct numerical simulation and machine learning. We consider a two-dimensional incompressible flow at Reynolds number 100, where the airfoil is positioned in the wake of a cylinder. By sweeping the parameter space, composed of the distance between the cylinder and the airfoil $Δx$, the relative height $Δy$, and the angle of attack of the airfoil $α$, four characteristic vortex-shedding regimes are observed. This study particularly focuses on suppressed vortex shedding at a certain arrangement as oscillation death in coupled oscillators, with the theoretical prediction of flow stability on a machine-learned low-order coordinate. Independent flows around each body are regarded as a system that exhibits isolated periodic vortex shedding at infinite distance, and interactions appear at smaller distances. We employ a synchronization-theoretic autoencoder to derive models for two coupled oscillators from an aerodynamic coefficient. The proposed approach, which enables the assessment of system stability and the prediction of vortical interaction behavior, may pave the way for the analysis of unsteady body-body interactions from the data-driven and synchronization perspectives.
