Accurate and reliable short-term prediction of ship motions offers improvements in both safety and control quality in ship motion sensitive maritime operat

Accurate wave forecasting with a couple of hours of warning time offers improvements in safety for maritime operation-related activities. Autoregressive (A

X-band marine radar is an effective tool for sea wave remote sensing. Conventional physical-based methods for acquiring wave parameters from radar sea clutter images use three-dimensional Fourier transform and spectral analysis. They are limited by some assumptions, empirical formulas and the calibration process while obtaining the modulation transfer function (MTF) and signal-to-noise ratio (SNR). Therefore, further improvement of wave inversion accuracy by using the physical-based method presents a challenge. Inspired by the capability of convolutional neural networks (CNN) in image characteristic processing, a deep-learning inversion method based on deep CNN is proposed. No intermediate step or parameter is needed in the CNN-based method, therefore fewer errors are introduced. Wave parameter inversion models were constructed based on CNN to inverse the wave’s spectral peak period and significant wave height. In the present paper, the numerically simulated X-band radar image data were used for a numerical investigation of wave parameters. Results of the conventional spectral analysis and CNN-based methods were compared and the CNN-based method had a higher accuracy on the same data set. The influence of training strategy on CNN-based inversion models was studied to analyze the dependence of a deep-learning inversion model on training data. Additionally, the effects of target parameters on the inversion accuracy of CNN-based models was also studied.

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.

A complicated coupling system performs fuel filling operation of a ship on the sea that involves tank sloshing and filling impact. This research elaborated on the sloshing load and flow characteristics of the variable mass tank and revealed its physical process through numerical simulations to provide safety assurance for fuel filling. First, this work clarified the difference between the numerical theory of variable mass tank sloshing and that of ordinary sloshing. On this foundation, a numerical model was developed for a three-dimensional variable mass tank sloshing. In addition, the model was validated by comparing the results of the previous experiments. Second, the sloshing pressure and internal free surface of the tank were monitored by numerical simulations. At the same time, a series of sensitivity analyses were carried out against the sloshing period, filling rate, and sloshing amplitude. Finally, the baffle was added to the sloshing tank to study the influence of internal barrier structure on the sloshing flow and pressure characteristics. The following conclusions were obtained from this work: Fuel filling caused an increase in liquid mass, suppressing sloshing and increasing the tank load. Overall, the sloshing load increased with the filling rate. Moreover, the impact of internal barrier structure on sloshing is discussed in this study, and it was discovered that the sloshing effect could be efficiently suppressed by the barrier structure.