AI 中文总结
针对任意时变驱动下钝体尾流瞬态动力学预测问题，提出基于特征的降阶模型，含三个关键要素。应用于流体弹球验证，无需非稳态训练数据即可准确再现瞬态动力学，提供可解释且面向控制的框架，有望推广到其他配置。
AI 中文摘要
我们提出了一种基于特征的降阶模型，用于预测在任意时变驱动下钝体尾流的瞬态动力学。起点是面向控制的POD伽辽金建模，它在将时变驱动作为自由输入时面临挑战。我们的模型包括三个关键要素。首先，用相同维度的更精确的基于特征的流形取代POD模式。其次，从封装时变相干结构的动态特征中提取状态空间。第三，为瞬态驱动响应扩充该状态空间。从而得到简单的解析流形动力学。该方法应用于雷诺数为30的流体弹球，即三个相同圆柱以等边三角形排列并沉浸在均匀流中且对称驱动的典型配置。该模型针对几种代表性驱动场景进行了验证，无需非稳态训练数据就能准确再现瞬态动力学，提供了一个可解释、基于观测且面向控制的框架。所提出的驱动钝体流动描述有望推广到其他配置。
英文摘要
We propose a feature-based reduced-order model to predict the transient dynamics of bluff-body wakes under arbitrary time-varying actuation. Starting point is a control-oriented POD Galerkin modeling which is challenged by incorporating time-varying actuations as a free input. Our model includes three key enablers. First, POD modes are replaced by a more accurate feature-based manifold of same dimension. Second, a state space is distilled from dynamic features which encapsulate time-varying coherent structures. Third, this state space is augmented for the transient actuation response. Thus, a simple analytical manifold dynamics is obtained. The approach is applied to the fluidic pinball at Re=30, a canonical configuration of three identical circular cylinders arranged in an equilateral triangle and immersed in uniform flow under symmetric actuation. The model is validated against several representative actuation scenarios and accurately reproduces the transient dynamics without requiring unsteady training data, providing an interpretable, observable-based and control-oriented framework. The proposed description of actuated bluff-body flows is expected to be generalisable to other configurations.
