The prediction of fuel consumption and Carbon Intensity Index (CII) of ships is crucial for optimizing decarbonization strategies in the maritime industry. This study proposes a ship fuel consumption prediction model based on the Long Short-Term Memory with Self-Attention Mechanism (SA-LSTM). The model is applied to a container ship of 2400 TEU to predict its hourly fuel consumption, hourly CII, and annual CII rating. Four different feature sets are selected from these data sources and are used as inputs for SA-LSTM and another ten models. The results demonstrate that the SA-LSTM model outperforms the other models in prediction accuracy. Specifically, the Mean Absolute Percentage Error (MAPE) for fuel consumption predictions using the SA-LSTM model is reduced by up to 20% compared to the XGBoost and by up to 12% compared to the LSTM model. Additionally, the SA-LSTM model achieves the highest accuracy in annual CII predictions.

The Carbon Intensity Index (CII) exerts a substantial impact on the operations and valuation of international shipping vessels. Accurately predicting the CII of ships could help ship operators dynamically evaluate the possible CII grate of a ship at the end of the year and choose appropriate methods to improve its CII grade to meet the IMO requirement with minimum cost. This study developed and compared five CII predicting models with multiple data sources. It integrates diverse data sources, including Automatic Identification System (AIS) data, sensor data, meteorological data, and sea state data from 2022, and extracts 21 relevant features for the vessel CII prediction. Five machine learning methods, including Artificial Neural Network (ANN), Support Vector Regression (SVR), Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), and Random Forest (RF), are employed to construct the CII prediction model, which is then applied to a 2400 TEU container ship. Features such as the mean period of total swell, mean period of wind waves, and seawater temperature were considered for inclusion as inputs in the model. The results reveal significant correlations between cumulative carbon emissions intensity and features like cumulative distance, seawater temperature, wave period, and swell period. Among these, the strongest correlations are observed with cumulative distance and seawater temperature, having correlation coefficients of 0.45 and 0.34, respectively. Notably, the ANN model demonstrates the highest accuracy in CII prediction, with an average absolute error of 0.0336, whereas the LASSO model exhibits the highest error of 0.2817. Similarly, the ANN model provides more accurate annual CII ratings for the vessel. Consequently, the ANN model proves to be the most suitable choice for cumulative CII prediction.

A finite element model of a 100 m X-BOW polar exploration cruise ship has been developed. The ship’s frequency response analysis was conducted, with the simulated results closely matching the test data. The maximum discrepancy was 22%, equating to a negligible 0.24 mm/s difference in terms of comfort. This indicates that the simulation meets the standards of engineering precision and validates the model’s accuracy. Utilizing a global modal equivalent mass solution approach, in conjunction with the spatial distribution of local modal mass, a method for calculating the equivalent mass of a single local mode in mixed modes has been devised. This method was applied to determine the equivalent mass of the local vibration region of the 100 m X-BOW ship. Tuned mass dampers (TMDs) were then designed based on this equivalent mass. Analysis reveals that the TMDs achieve a 31 dB vibration absorption effect at a frequency of 13.4 Hz with a mass ratio of 0.05. They also provide a control effect at 10 Hz and 18.8 Hz, corresponding to 3 dB and 2 dB reductions, respectively. The control frequency band is broad, flat, and robust, indicating the effectiveness of the TMDs in mitigating vibrations across a wide range of frequencies.

Luxury cruise ships are high-end passenger ships with facilities on board for the leisure and entertainment of passengers, so the comfort of luxury cruise ships is a matter of great concern. In this paper, a finite element model of a new polar exploration cruise ship is established, and the wet modes of the whole ship are calculated using the virtual mass method and compared with the principal frequencies of the excitation forces to initially verify the rationality of the design of the structural vibration characteristics of the whole ship. The admittance matrix of the vibration velocity to excitation force was calculated by a frequency response analysis, and the vibration velocities at the stern plate and main engine foundations were tested during sailing. Then, the obtained propeller and main engine excitation forces were loaded into the finite element model; the vibration velocities of each compartment were calculated and compared with the compartment vibration velocity test values. The errors were within the engineering allowable range, verifying the accuracy of the excitation forces. The propeller and main engine excitation forces were loaded separately on the finite element model to calculate the vibration velocity of each cabin, and the contribution of the two excitation sources to the vibration velocity of each cabin was analyzed. It was found that the contribution of the excitation source to the cabin response was related to the relative position between the cabin and the excitation source. When the cabin was located in the cabin adjacent to or directly above a certain excitation source, the contribution of the excitation source to the cabin response was greater. When the cabin was farther away from both excitation sources, the contribution of the propeller excitation was greater. This provides a targeted reference for the preliminary vibration assessment and later vibration control of the new polar expedition cruise ship.