Advancements in intelligent materials for the past few decades enabled the development of functional morphing structures and robots operating in fluid environments. Fluid-structure interaction (FSI) problems for functional morphing structures and robots were naturally accentuated. In this paper, the recent advancements in shape-morphing robots and structures in fluid flow across different Reynolds number scales are reviewed and summarized, from microrobots with Reynolds numbers much lower than 1 to deformable aircraft in turbulent flows. To improve the design and functionality of the morphing structures and robots, we discuss modeling methods, experiments, and materials for the morphing structures over a vast range of Reynolds numbers. Understanding FSI in designing morphing structures and robots is emphasized. Following up with several critical future questions to address, the potential applications of artificial intelligence and machine learning (AI/ML) techniques in improving the design of shape-morphing structures and robots are discussed. These shape-morphing structures are expected to significantly enhance sustainable solutions for challenges and explore the unknown of deeper oceans and outer space.

This paper experimentally investigates the nonlinear hydrodynamic forces on a semisubmerged floating collar under full-scale Reynolds number (R) and Keulegan-Carpenter number (KC) oscillatory flow alone and combined with a current. The collar was forced to oscillate harmonically in the direction of the current within a wide range of oscillation amplitudes and frequencies. The first five orders of the horizontal hydrodynamic forces are obtained using Fourier transformation techniques. The influence of the KC, the frequency parameter (β), and the reduced velocity (Vr) on the high-order components of the hydrodynamic force were investigated. The results show that, under oscillatory flows, the first five harmonics of the hydrodynamic force coefficients increase with an increase of KC and β. As the reduced velocity (Vr) increases, the first- and second-order components are greatly affected. These first two order components contribute greatly to the overall hydrodynamic force in the combined flow condit...

Vortex-induced vibration (VIV) for long flexible cylinders in deep water involves a large number of physical variables, such as Strouhal number, Reynolds number, mass ratio, damping parameter etc. Among all the variables, it is essential to identify the most important parameters for robust VIV response prediction. In this paper, machine learning techniques were applied to