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arXiv 2411.08062physics.flu-dyn

基于实验数据的聚类网络模型揭示分层湍流与时间间歇性的路径

Routes to stratified turbulence and temporal intermittency revealed by a cluster-based network model of experimental data

Adrien Lefauve, Yui Hin Marvil Cheung, Xianyang Jiang, Miles M. P. Couchman

AI总结:

本文提出结合降维和无监督聚类的数据驱动方法,识别分层倾斜管道实验的湍流骨架,揭示多种湍流类型及间歇性转变路径,为多物理场湍流降阶模型奠定基础。

摘要:

使用相干结构的“骨架”对流体湍流进行建模,传统上通过关注少数典型的实验室实验(如管道流和Taylor-Couette流)取得进展。我们在此考虑分层倾斜管道,这是一种持续的剪切流,其密度分层允许在比非分层流显著更高的雷诺数下探索大量新的相干和间歇状态。我们通过结合降维和阴影图可视化的无监督聚类数据驱动方法,自动识别了该实验潜在的湍流骨架。我们证明了在参数空间中存在多种类型的湍流及其间的间歇性循环,揭示了不同的转变路径。借助基于聚类的间歇性网络模型,我们揭示了在湍流耗散水平不断增加下转变概率和停留时间的模式。我们的方法和结果为多物理场湍流的新型降阶模型铺平了道路。

英文摘要:

Modelling fluid turbulence using a `skeleton' of coherent structures has traditionally progressed by focusing on a few canonical laboratory experiments such as pipe flow and Taylor-Couette flow. We here consider the stratified inclined duct, a sustained shear flow whose density stratification allows for the exploration of a wealth of new coherent and intermittent states at significantly higher Reynolds numbers than in unstratified flows. We automatically identify the underlying turbulent skeleton of this experiment with a data-driven method combining dimensionality reduction and unsupervised clustering of shadowgraph visualisations. We demonstrate the existence of multiple types of turbulence across parameter space and intermittent cycling between them, revealing distinct transition pathways. With a cluster-based network model of intermittency we uncover patterns in the transition probabilities and residence times under increasing levels of turbulent dissipation. Our method and results pave the way for new reduced-order models of multi-physics turbulence.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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