Hydrodynamic Performance and Motion Prediction Before Twin-Barge Float-Over Installation of Offshore Wind Turbines

In recent years, the twin-barge float-over method has been widely used in offshore installations. This paper conducts numerical simulation and experimental research on the twin-barge float-over installation of offshore wind turbines (TBFOI-OWTs), focusing primarily on seakeeping performance, and also explores the influence of the gap distance on the hydrodynamic behavior of TBFOI-OWTs. Model tests are conducted in the ocean basin at Tsinghua Shenzhen International Graduate School. A physical model with a scale ratio of 1:50 is designed and fabricated, comprising two barges, a truss carriage frame, two small wind turbines, and a spread catenary mooring system. A series of model tests, including free decay tests, regular wave tests, and random wave tests, are carried out to investigate the hydrodynamics of TBFOI-OWTs. The experimental results and the numerical results are in good agreement, thereby validating the accuracy of the numerical simulation method. The motion RAOs of TBFOI-OWTs are small, demonstrating their good seakeeping performance. Compared with the regular wave situation, the surge and sway motions in random waves have greater ranges and amplitudes. This reveals that the mooring analysis cannot depend on regular waves only, and more importantly, that the random nature of realistic waves is less favorable for float-over installations. The responses in random waves are primarily controlled by motions’ natural frequencies and incident wave frequency. It is also revealed that the distance between two barges has a significant influence on the motion RAOs in beam seas. Within a certain range of incident wave periods (10.00 s < <i>T</i> < 15.00 s), increasing the gap distance reduces the sway RAO and roll RAO due to the energy dissipated by the damping pool of the barge gap. For installation safety within an operating window, it is meaningful but challenging to have accurate predictions of the forthcoming motions. For this, this study employs the Whale Optimization Algorithm (WOA) to optimize the Long Short-Term Memory (LSTM) neural network. Both the stepwise iterative model and the direct multi-step model of LSTM achieve a high accuracy of predicted heave motions. This study, to some extent, affirms the feasibility of float-over installation in the offshore wind power industry and provides a useful scheme for short-term predictions of motions.