Deep-water regions often have winds favorable for offshore wind turbines, and floating turbines currently show the greatest potential to exploit such winds. This work established proper scaling laws for model tests, which were then implemented in the construction of a model wind turbine with optimally designed blades. The aerodynamic, hydrodynamic, and elastic characteristics of the proposed new multi-column tension-leg-type floating wind turbine (WindStar TLP system) were explored in the wave tank testing of a 1:50 scale model at the State Key Laboratory of Ocean Engineering at Shanghai Jiao Tong University. Tests were conducted under conditions of still water, white noise waves, irregular waves, and combined wind, wave, and current loads. The results established the natural periods of the motion, damping, motion response amplitude operators, and tendon tensions of the WindStar TLP system under different environmental conditions, and thus could serve as a reference for further research.

This paper explores a highly accurate identification modeling approach for the ship maneuvering motion with full-scale trial. A multi-innovation gradient iterative (MIGI) approach is proposed to optimize the distance metric of locally weighted learning (LWL), and a novel non-parametric modeling technique is developed for a nonlinear ship maneuvering system. This proposed method's advantages are as follows: first, it can avoid the unmodeled dynamics and multicollinearity inherent to the conventional parametric model; second, it eliminates the over-learning or under-learning and obtains the optimal distance metric; and third, the MIGI is not sensitive to the initial parameter value and requires less time during the training phase. These advantages result in a highly accurate mathematical modeling technique that can be conveniently implemented in applications. To verify the characteristics of this mathematical model, two examples are used as the model platforms to study the ship maneuvering.