Floating offshore wind turbines are subjected to platform motions that modify the local velocity experienced by the rotor. This work analyzes how variations in the platform motions affect the aerodynamic power of a floating wind turbine. Idealized wind conditions and rigid wind turbine are considered. The platform motions are prescribed by the user and the coupled motions considered are pitch-surge, pitch-yaw and surge-yaw. The main novelties of the work consist in the fact that multiple motions are prescribed simultaneously, including yaw, and that the prescribed motions present a difference in phase. In absence of wind turbine controller, the pitch-surge coupling shows significant increase in average power production with respect to fixed conditions when either the amplitude or frequency are increased. This gain is maximum when surge and pitch are in phase, and is almost zero in phase opposition. The presence of the controller reverses the behavior and introduces a loss in average power along with increasing amplitudes. Phase shift analysis is particularly interesting in the surge and pitch cases: the controller introduces an upper limit in power, and phase opposition is now desirable. The yaw degree of freedom is shown to be of secondary importance in every condition.

A new method is proposed to estimate a floating wind turbine's annual energy production (AEP) using frequency and time-domain design techniques. The approach demonstrated herein estimates the AEP by performing a convolution between the floating platform response and the response power operators (RPOs) that map the average power produced by the turbine as a function of the amplitude and frequency of the platform motions. One advantage of this approach is that it can be performed early in the conceptual design phase to help discover design space trade-offs between the platform and rotor design. The methodology is applied to the IEA Wind 15 MW WindCrete spar-buoy model using OpenFAST. The RPOs are obtained by prescribing single-DOF platform motions to the turbine with a given amplitude and frequency. This methodology is then validated by comparing the AEP estimation from the RPOs with the AEP estimation from fully-coupled simulations. The results indicate that the method is able to estimate the value of AEP for a realistic sea-state and regular waves. However, further validation is needed as, in the first case, the turbine is moving too little and, in the second case, the contribution of the controller may be dominant.

Traffic Management and Logistic Optimization have been extensively studied as two separate classes of problems, for which numerous methodologies, mathematical models and algorithmic solutions were made available in literature. However, little attention has been devoted to the interactions between the variables involved in these problems and the consequences of the decision making processes carried independently by Traffic Managers and Logistic Players. We believe this to be of considerable importance, since partial or incomplete knowledge on one another's decisions might yield sub-optimality for either or both of them. In this work, we propose an integrated view on both classes of problems, providing mathematical formulations to support the assessment of the impact which the two players may have on each other.