发表机构
Northwestern Polytechnical University; Institute of AI for Industries, Chinese Academy of Sciences; Institute of Computing Technology, Chinese Academy of Sciences; International Joint Institute of Artificial Intelligence on Fluid Mechanics, Northwestern Polytechnical University（西北工业大学; 中国科学院工业人工智能研究所; 中国科学院计算技术研究所; 西北工业大学流体力学人工智能联合研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出纯物理驱动的P-PINN框架，通过时间周期性和参数化输入直接求解周期流，并耦合谐波平衡进行气动弹性分析，实现快速准确的参数化响应预测。
AI 中文摘要
周期性非定常流动常见于强迫运动和流固耦合问题中。其参数化分析通常需要重复进行高保真模拟，而现有的降阶模型和代理模型通常依赖于预先生成的流场或气动数据。本研究提出了一种纯物理驱动的框架，用于求解参数化周期流和有限振幅气动弹性响应。首先，开发了一种周期性物理信息神经网络（P-PINN），通过在单个运动周期上施加时间周期性直接求解周期流，从而避免了在周期状态建立之前解析长瞬态演化的需要。进一步将流动条件和运动参数作为网络输入，构建周期流场和气动力的连续参数化表示。在此基础上，通过一阶谐波平衡将参数化气动模型与结构动力学方程耦合，求解单自由度气动弹性系统的响应振幅和频率。该框架使用圆柱和翼型的强迫运动案例进行验证，与时间推进结果相比，在不同流动和运动参数下，准确再现了周期气动力、表面载荷分布和瞬时流场。此外，对亚临界雷诺数下弹性安装的圆柱进行了气动弹性分析，所得气动弹性响应与完全耦合的CFD/CSD结果吻合良好。离线训练后，参数化模型可针对不同结构参数状态重复评估，从而在数秒内在线获得完整的气动弹性响应曲线，无需重复进行长时间流固耦合时间推进。
英文摘要
Periodic unsteady flows are common in forced-motion and fluid-structure interaction problems. Their parametric analysis typically requires repeated high-fidelity simulations, whereas existing reduced-order and surrogate models generally rely on pre-generated flow-field or aerodynamic data. This study proposes a purely physics-driven framework for solving parametric periodic flows and finite-amplitude aeroelastic responses. First, a Periodic Physics-Informed Neural Network (P-PINN) is developed to directly solve periodic flows by imposing temporal periodicity over a single motion cycle, thereby avoiding the need to resolve the long transient evolution preceding the establishment of the periodic state. The flow conditions and motion parameters are further incorporated as network inputs to construct continuous parametric representations of the periodic flow field and aerodynamic forces. On this basis, the parametric aerodynamic model is coupled with the structural dynamic equation through first-order harmonic balance to solve the response amplitude and frequency of a single-degree-of-freedom aeroelastic system. This framework is validated using forced-motion cases of a circular cylinder and an airfoil, demonstrating accurate reproduction of periodic aerodynamic forces, surface load distributions, and instantaneous flow fields under different flow and motion parameters compared with time-marching results. Furthermore, aeroelastic analysis is conducted for an elastically mounted circular cylinder at subcritical Reynolds numbers, and the resulting aeroelastic response agrees well with fully coupled CFD/CSD results. Once trained offline, the parametric model can be repeatedly evaluated for different structural parameter states, enabling the complete aeroelastic response curve to be obtained online within seconds, without repeated long-time fluid-structure interaction time marching.
