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arXiv 2511.22012physics.flu-dynphysics.comp-ph

利用超图神经网络高效准确地建模倒置弹性翼的摆动动力学

An Efficient and Accurate Surrogate Modeling of Flapping Dynamics in Inverted Elastic Foils using Hypergraph Neural Networks

Aarshana R. Parekh, Rui Gao, Rajeev K. Jaiman

AI总结:

本文提出基于超图神经网络的高效替代模型,用于准确预测倒置弹性翼在流体中的非稳态动力学,实现高精度的流-结构相互作用模拟。

摘要:

悬臂弹性翼在受到流体流动作用时可以产生自激的、大振幅摆动,这是流体-结构相互作用中广泛观察到的现象,例如飘动的树叶或鱼鳍的运动。当在稳定水流中利用时,这些振荡能够从流体中提取动能。然而,准确预测这些动力学需要高保真度的仿真,但这些仿真在用于设计优化所需广泛配置空间的进行中成本过高。为此,我们开发了一种新的图神经网络(GNN)替代模型,用于倒置翼问题,该问题建模为一个弹性安装的刚性翼在均匀流中进行尾缘pitching运动。耦合的流-结构动力学通过Petrov-Galerkin有限元方法和任意拉格朗日-欧拉公式求解,提供高保真的数据用于训练和验证。替代模型使用一种旋转等变、准单体的GNN架构:结构网格运动通过正交分解压缩并通过多层感知机进行处理。同时,GNN演进流场与系统状态保持一致。具体而言,本研究将超图框架扩展到灵活的、自振荡的翼,捕捉涡流动力学与结构运动之间的非线性耦合。GNN替代模型在数千个时间步中预测尖端位移和力系数的误差小于1.5%,同时准确再现主导的涡流脱落频率。模型在能量转移指标上与全阶仿真相差不超过3%,展示了准确性和长期稳定性。这些结果展示了一种新的、高效的替代模型,用于能量收集系统中长时间预测非稳态流-结构动力学。

英文摘要:

Cantilevered elastic foils can undergo self-induced, large-amplitude flapping when subject to fluid flow, a widely observed phenomenon of fluid-structure interaction, from fluttering leaves or the movement of fish fins. When harnessed in steady currents, these oscillations enable the extraction of kinetic energy from the flow. However, accurately predicting these dynamics requires high-fidelity simulations that are prohibitively expensive to perform across the broad configuration space needed for design optimization. To address this, we develop a novel graph neural network (GNN) surrogate for the inverted foil problem, modeled as an elastically mounted rigid foil undergoing trailing-edge pitching in uniform flow. The coupled fluid-structure dynamics are solved using a Petrov-Galerkin finite element method with an arbitrary Lagrangian-Eulerian formulation, providing high-fidelity data for training and validation. The surrogate uses a rotation-equivariant, quasi-monolithic GNN architecture: structural mesh motion is compressed via proper orthogonal decomposition and advanced through a multilayer perceptron. At the same time, the GNN evolves the flow field consistent with system states. Specifically, this study extends the hypergraph framework to flexible, self-oscillating foils, capturing the nonlinear coupling between vortex dynamics and structural motion. The GNN surrogate achieves less than 1.5% error in predicting tip displacement and force coefficients over thousands of time steps, while accurately reproducing dominant vortex-shedding frequencies. The model captures energy transfer metrics within 3% of full-order simulations, demonstrating both accuracy and long-term stability. These results show a new, efficient surrogate for long-horizon prediction of unsteady flow-structure dynamics in energy-harvesting systems.

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