A Neural Network Time Dependent Hydrodynamic Force Model for Forced Two-Degree-Of-Freedom Sinusoidal Motion of a Circular Cylinder in a Free Stream
A Neural Network Time Dependent Hydrodynamic Force Model for Forced Two-Degree-Of-Freedom Sinusoidal Motion of a Circular Cylinder in a Free Stream
复制标题
自由流中圆柱体受迫二自由度正弦运动的神经网络瞬态水动力模型
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
J. Dahl
中科院分区：
文献类型：
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作者：
E. Aktosun;N. Xiros;J. Dahl
建立了一个基于数据驱动的水动力模型，用于模拟在已知会发生涡激振动（VIV）的流动条件下，作用在振荡圆柱体上的动态力。该模型的开发是为了在未来的控制系统中使用，以提高基于VIV的能量收集。动态模型是经验性的，利用力的测量获得了一个大的强制运动实验，跨越一系列的参数值，规定的气缸运动的运动学。该模型包括一个圆柱体的动力学进行强制组合的线和横流运动在自由流。实验在全自动拖曳水池中进行，其中在近10，000次实验中改变了直列运动幅度、横流运动幅度、折合速度以及直列和横流运动之间的相位差等参数。所有实验都是在恒定雷诺数为7620的条件下进行的。一个前馈神经网络的训练使用的力数据库开发一个时间依赖模型的气缸上的力给定的运动学条件。选定的力模型的输出，提供了一个基础，为未来的合成能量收集控制应用。
A data-driven hydrodynamic force model is developed to model the dynamic forces on an oscillating circular cylinder for flow conditions where Vortex-Induced-Vibrations (VIV) are known to occur. The model is developed for use in future control systems to improve VIV-based energy harvesting. The dynamic model is empirical, utilizing force measurements obtained for a large set of forced motion experiments, spanning a range of parametric values that prescribe the kinematics of the cylinder motion. The model includes the dynamics of a circular cylinder undergoing forced combined in-line and cross-flow motion in a free stream. The experiments were conducted in a fully automated towing tank where parameters of in-line amplitude of motion, cross-flow amplitude of motion, reduced velocity, and phase difference between in-line and cross-flow motion were varied over nearly 10,000 experiments. All experiments were carried out at a constant Reynolds number of 7620. A feed forward neural network is trained using the force database to develop a time dependent model of forces on the cylinder for given kinematic conditions. Selected outputs of the force model are presented, providing a basis for future synthesis in energy harvesting control applications.