# Prediction of Dynamic Responses of Flow-Induced Vibration Using Deep Learning
## 1 Introduction  
Interest in marine renewable energy has gradually been increasing worldwide, and various marine technologies have been developed for commercialization of the energy generated thereof. With more than 70% of the Earth's surface covered by water, significant amounts of potential energy are stored in storm surges, sea waves, and resources in the form of heat and salts in the ocean. However, most marine renewable energy technologies have not yet been commercialized because of their low efficiencies and high cost of power generation compared to conventional fossil fuels. Recently, marine power generation technologies have been developed specifically for commercialization [1]. The vortex induced vibration for aquatic clean energy (VIVACE) converter, a marine renewable energy technology, converts the hydrokinetic energy in water to electrical energy [2]. The VIVACE converter was invented and patented by the Marine Renewable Energy Laboratory (MRELab) at the University of Michigan and was developed based on the simple idea of maintaining and enhancing vortex-induced vibrations (VIVs) while maximizing flow-induced vibration (FIV), rather than suppressing them. Several lab-scale converters have been built and tested in the low-turbulence free surface water (LTFSW) channel at the MRELab since 2005 [2]. The research team has been investigating methods to improve the power output of the converter by enhancing the FIVs of multiple cylinders over a wide range of flow speeds. For this study, the MRELab provided the experimental data of asingle-cylinder converter to train our deep neural network (DNN) to predict the dynamic responses and power outputs of the converter. FIVs are caused in various structures, such as bridges, buildings, and offshore structures resulting from fluid flows. One of the most common types of FIVs is the VIV, which was first observed by [PERSON] in 1504 [3]. VIV is generally caused by alternate vortex shedding on both sides of the cylinder like Figure 1, which causes periodic lift forces to act on the cylinder. Galloping is another type of FIV that has a lower frequency and a larger amplitude compared to VIV and is caused by the asymmetric motions of the shear layers. Galloping is a more powerful phenomenon than VIV and can occur for a cylinder with noncircular cross sections above a certain critical flow speed. The amplitude of the galloping continues to increase with the flow velocity until the structure is destroyed. In general, as the flow velocity increases, VIVs occur first, followed by galloping. Because of the destructive nature of FIVs in structures, most researchers have focused on its suppression. In 2006, however, the MRELab research team developed the VIVACE converter, where the kinetic energy of the fluid could be converted to electrical energy by enhancing and controlling the FIVs [4; 5; 6]. This study is concerned with predicting the amplitude and frequency of a single cylinder installed in the VIVACE converter by learning the FIV experiment data through a deep learning model. The study was conducted using experimental data provided by MRELab. When a neural network learns the test data and a deep learning model is constructed to predict the dynamic responses of FIVs for different conditions without learning, the results can be confirmed within a relatively short time. In addition, learning the experimental data under various conditions can be an alternative to statistical methods, such as regression analysis [7]. Therefore, in recent research, studies have been actively conducted on the system to predict data measured via experiments or with sensors by learning the deep learning model. Deep learning can be used not only for interpretation of incomplete and noisy input data but also for pattern recognition, classification, generalization, and abstraction while learning of large amounts of data [8].