用机器学习提取湍流尾流-极端涡阵风相互作用的有信息涡结构
Extracting informative vortical structures of turbulent wake-extreme vortex gust interactions with machine learning
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中文总结 AI 辅助
本研究采用卷积信息论学习，在雷诺数5000下从涡阵风-翼型相互作用中提取与升力、能量传递相关的关键涡结构，为瞬态空气动力学流动研究提供数据驱动的因果分析方法。
中文摘要 AI 辅助
本研究在基于弦长的雷诺数为$5000$的条件下，考虑从极端涡阵风-翼型相互作用中提取具有因果重要性的涡结构。该提取通过卷积信息论学习，根据对任意未来目标变量的贡献，将给定的涡流动快照分解为其有信息分量和残余分量来实现。对于当前表现出瞬态和多尺度流动特性的涡-翼型相互作用，我们首先针对未来升力系数研究重要的涡结构：在涡撞击前，主要突出涡核；在大规模分离后，还会捕捉到新出现的剪切层，这一点可通过与瞬时力元分析的对比得到验证。我们进一步将尺度相关的能量传递作为未来关注变量，以研究其与升力相关结构相比对提取的有信息结构的影响：在阵风遭遇的早期，这些结构与基于升力的结构不同，但在撞击后变得相似，揭示了不同瞬态空气动力学机制下有信息结构之间的相似性。本研究提出的数据驱动方法可选择性提取负责目标物理过程的特定重要流动结构，能从因果、数据驱动的角度支持对一系列瞬态空气动力学流动的研究。
英文摘要
This study considers extracting causally important vortical structures from the extreme vortex gust-airfoil interaction at a chord-based Reynolds number of $5000$. This extraction is achieved by decomposing a given vortical flow snapshot into its informative and residual components based on the contribution to an arbitrary future target variable with convolutional information-theoretic learning. For the current vortex-airfoil interactions that exhibit transient and multiscale flow characteristics, we first examine the important vortical structures with respect to a future lift coefficient. While the vortex cores are primarily highlighted before vortex impingement, the emerging shear layers are additionally captured after the massive separation, which is evident from a comparison to an instantaneous force-element analysis. We further take the scale-dependent energy transfer as a future variable of interest to examine its impact on the extracted informative structures compared to the lift-associated structures. They are distinct from the lift-based structures in the early stage of the gust encounter yet become similar after impingement, revealing an analogy between informative structures across different transient aerodynamic mechanisms. The present data-driven approach selectively extracts the specific important flow structures responsible for the physics of interest, which can support studying a range of transient aerodynamic flows from the causal, data-driven perspective.
