A coupling numerical methodology has been developed using the Navier-Stokes solver for ship/rotor flowfield simulation during the helicopter shipboard launch. The steady rotor model (SRM) based on the momentum source approach and unsteady rotor model (URM) based on the moving overset mesh method are respectively employed to account for rotor operations. Studies of coupling ship/rotor flowfield on two typical ships highlight complex interactions over the flight deck. It is shown that the rotor-shipboard vortex interaction and recirculation zone are more serious for small-sized-deck destroyers, while on the straight-through-deck ship the helicopter operations are sensitive to asymmetric distribution of lateral velocity caused by the island. Qualitative and quantitative comparisons in terms of vorticity and velocity between SRM and URM solvers have been conducted. The results demonstrate that both methods can capture complex interactions well. The velocity difference is within 1 m/s in most flowfield areas, except the rotor-wake dominant region. With the consideration of computational efficiency, the momentum source approach could be used to effectively conduct the study of helicopter shipboard operations. ARTICLE HISTORY

The working condition of the floating platform will be affected by wind and waves in the marine environment. Therefore, it is of great importance to carry out real-time prediction research on the mooring load for ensuring the normal operation of the floating platform. Current researches have focused on the real-time prediction of mooring load using the machine learning method, but most of the studies are about the application and generalization analysis of different models. There are few studies on the influence of data distribution characteristics on prediction accuracy. In view of the above problems, this paper investigates the effect of data skewness on the prediction performance for the deep learning model. The long short-term memory (LSTM) neural network is applied to construct the mooring load prediction model. The numerical simulation datasets of the deep water semi-submersible platform are employed in model training and data analysis. The prediction performance of the model is preliminarily verified based on the simulation results. Meanwhile, the distribution characteristics of mooring load data under different sea states are analyzed and a skewness processing method based on the Box-Cox Transformation (BCT) is proposed. The effect of data skewness on prediction accuracy is further investigated. The comparison results indicate that reducing the mooring load data skewness can effectively improve the prediction accuracy of LSTM model.