学习从粗粒度流场表示中获取依赖于刚度的流体结构动力学

中文摘要 AI 辅助

本文开发了一个数据驱动的框架，用于长期预测流体-结构相互作用（FSI）动力学，重点关注柔性板的流致振动（FIV）。一个刚度条件化的神经演化算子联合表示欧拉流场和拉格朗日结构状态。该板由101个有序的结构标记表示，这些标记携带节点坐标和速度，并以无量纲弯曲刚度作为全局条件变量。双向交叉注意力在混合CNN-Transformer架构内耦合流体和结构表示。通过分阶段的多步自回归展开和对称反射轨迹进行训练，单个算子捕获了三种依赖于刚度的响应状态：偏转-扑动、偏转和扑动。预测的轨迹保留了主要的流动结构、结构振荡和主频率，而盲目的1000步展开保持有界。该算子还能插值到训练中未包含的刚度值。为了减少力重构中对分辨率不足的近壁梯度的敏感性，我们开发了一个基于导数-矩变换（DMT）的可微气动力模块。传统的壁面应力表面积分被核心涡旋区域周围的封闭二维曲线积分所取代，从而能够准确重构升力和阻力。符号距离函数（SDF）和平滑的狄拉克-德尔塔公式使积分完全可微，同时保持梯度流。所提出的框架为依赖于刚度的FSI动力学提供了准确且可微的替代模型，从而能够进行高效的参数研究和未来针对流能采集应用的刚度优化。

英文摘要

This paper develops a data-driven framework for long-term prediction of fluid--structure interaction (FSI) dynamics, focusing on the flow-induced vibration (FIV) of a flexible plate. A stiffness-conditioned neural evolution operator jointly represents the Eulerian flow field and Lagrangian structural state. The plate is represented by 101 ordered structural tokens carrying nodal coordinates and velocities, with nondimensional bending stiffness as a global conditioning variable. Bidirectional cross-attention couples fluid and structural representations within a hybrid CNN-Transformer architecture. Trained with staged multi-step autoregressive rollouts and symmetry-reflected trajectories, a single operator captures three stiffness-dependent response regimes: deflected--flapping, deflected, and flapping. The predicted trajectories preserve the principal flow structures, structural oscillations, and dominant frequencies, while blind 1000-step rollouts remain bounded. The operator also interpolates to stiffness values excluded from training. To reduce sensitivity to under-resolved near-wall gradients in force reconstruction, we develop a differentiable aerodynamic-force module based on the derivative-moment transformation (DMT). Conventional wall-stress surface integrals are replaced by an enclosed 2D curve integral around the core vortex region, enabling accurate reconstruction of lift and drag. A Signed Distance Function (SDF) and smoothed Dirac-delta formulation make the integration fully differentiable while preserving gradient flow. The proposed framework provides an accurate and differentiable surrogate for stiffness-dependent FSI dynamics, enabling efficient parameter studies and future stiffness optimization for flow-energy-harvesting applications.

发表机构

• University of Science and Technology of China（中国科学技术大学）
• Beijing Institute of Aeronautical Systems Engineering（北京航空系统工程研究所）

机构由 AI 辅助整理，请以论文原文为准。