AI 中文总结
针对自激气动流引发的气弹不稳定性预测难题，提出结合非线性流体振荡器等的神经微分方程降阶模型，经单个CFD模拟识别后与结构方程耦合，用于跨声速抖振预测，结果与全阶解吻合良好，为多模态气弹不稳定性提供新见解。
AI 中文摘要
自激气动流在广泛的系统中出现，会引发非线性流固相互作用和气弹不稳定性，预测具有挑战性且计算成本高。本文提出一种物理引导的神经微分方程降阶模型，结合非线性流体振荡器、有限记忆多输入沃尔泰拉级数和紧凑神经网络校正。多输入气动公式推广到m个结构模态，捕捉直接和非线性跨模态耦合。该模型从单个规定运动的CFD模拟中识别，然后与结构运动方程耦合进行高效气弹预测。应用于ONERA OAT15A翼型的跨声速抖振，时间推进ROM预测的气弹稳定性、频率锁定和极限环振幅与全阶参考解吻合良好。该ROM为涉及多个结构模态的抖振引起的气弹不稳定性提供了新的见解。
英文摘要
Self-excited aerodynamic flows arise across a broad range of systems and can drive nonlinear fluid-structure interactions and aeroelastic instabilities that are challenging and computationally expensive to predict. This paper presents a physics-guided neural differential equation (DE) reduced order model (ROM) combining a nonlinear fluid oscillator, a finite-memory multi-input Volterra series, and a compact neural network correction. The multi-input aerodynamic formulation is generalized to m structural modes, capturing direct and nonlinear cross-modal coupling. The model is identified from a single prescribed-motion CFD simulation with simultaneous excitation of all retained structural modes, and is then coupled with the structural equations of motion for efficient aeroelastic prediction. Applied to transonic buffet over the ONERA OAT15A airfoil, the time-marching ROM predicts aeroelastic stability, frequency lock-in, and limit cycle amplitudes in good agreement with full-order reference solutions. The ROM is used to provide substantial new insight into buffet-induced aeroelastic instabilities involving more than one structural mode.
CommentsUnder Review: AIAA Journal
