A Neural Network Time Dependent Hydrodynamic Force Model for Forced Two-Degree-Of-Freedom Sinusoidal Motion of a Circular Cylinder in a Free Stream

Abstract

Abstract 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.