An abundant amount of data gathered during wind tunnel testing and health monitoring of structures inspires the use of machine learning methods to replicate the wind forces. This paper presents a data-driven Gaussian Process-Nonlinear Finite Impulse Response (GP-NFIR) model of the nonlinear self-excited forces acting on structures. Constructed in a nondimensional form, the model takes the effective wind angle of attack as lagged exogenous input and outputs a probability distribution of the forces. The nonlinear input/output function is modeled by a GP regression. Consequently, the model is nonparametric, thereby circumventing to set up the function's structure a priori. The training input is designed as random harmonic motion consisting of vertical and rotational displacements. Once trained, the model can predict the aerodynamic forces for both prescribed input motion and aeroelastic analysis. The concept is first verified for a flat plate's analytical solution by predicting the self-excited forces and flutter velocity. Finally, the framework is applied to a streamlined and bluff bridge deck based on Computational Fluid Dynamics (CFD) data. The model's ability to predict nonlinear aerodynamic forces, flutter velocity, and post-flutter behavior are highlighted. Applications of the framework are foreseen in the structural analysis during the design and monitoring of slender line-like structures.
翻译:在风隧道测试和结构健康监测期间收集的大量数据促使人们使用机器学习方法复制风力。本文件展示了由数据驱动的高斯进程-非线性精度免疫反应模型(GP-NFIR),该模型是非线性自我刺激力量模型(GP-NFIR),该模型在结构上运行。模型以非维形式构建,将攻击的有效风角度作为延迟的外源输入和输出的概率分布作为力量。非线性输入/输出功能以GP回归为模型。因此,模型是非参数性的,从而绕过功能结构的设置。培训投入设计为由垂直和旋转变异组成的随机调运动运动。模型一旦经过培训,就可以预测气动动力力量,同时进行气动分析。该模型首先通过预测自振动力和光速速度来验证平板的分析解决方案。最后,该框架被应用到基于可比较性氟化结构结构结构结构结构结构结构结构的简化和虚张的架形甲板。 模型在结构变压性变压性结构的预测中,其变压性能力是模型,其变压性变压性变压性变压的预测。 格式的变压后变压性变压式结构图图。