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Researchers have achieved notable advancements over the years in exploring ship damage and stability resulting from underwater explosions (UNDEX). However, numerous challenges and open questions remain in this field. In this study, the research progress of UNDEX load is first reviewed, which covers the explosion load during the shock wave and bubble pulsation stages. Subsequently, the research progress of ship damage caused by UNDEX is reviewed from two aspects: contact explosion and noncontact explosion. Finally, the research progress of ship navigation stability caused by UNDEX is reviewed from three aspects: natural factors, ship’s internal factors, and explosion factors. Analysis reveals that most existing research has focused on the damage to displacement ships caused by UNDEX. Meanwhile, less attention has been paid to the damage and stability of non-displacement ships caused by UNDEX, which are worthy of discussion.

In active heave compensation, in order to realize the smooth control of the heave compensation platform, it is necessary to use the ship motion measurement system to accurately obtain the ship displacement signal, invert the ship displacement signal, and then control the expansion and contraction of the electric cylinder so that the compensation platform remains horizontal. The ship displacement measurement system generally adopts the second integral of the acceleration sensor to obtain the ship displacement signal. During the acquisition process of the ship displacement signal, the quadratic integration process of the acceleration, and the communication process of the output control command, there is a processing lag which makes the error accumulate, resulting in a delay in the measurement of the ship motion. In order to collect the ship motion more accurately and control the heave compensation platform more precisely, this paper proposes a ship motion prediction method based on a variable step-variable sampling frequency characteristic LSTM (Long Short-Term Memory) neural network. First, we use the autocorrelation function algorithm to calculate the inherent delay of the lag in the process of signal acquisition by the measurement system. Secondly, the LSTM neural network is used to predict the inherent delay step of the lagging ship displacement signal. During the prediction process, it is found that the difference in the sampling frequency of the displacement signal will lead to a change in the step of the inherent delay—experiment in the laboratory to verify. By controlling the motion platform to simulate the motion of the ship and using the ship motion measurement system and the laser sensor system to measure the displacement signal of the motion platform synchronously, it is verified that the ship motion measurement system does have an inherent delay. Thirdly, on a sailing ship, ship displacement signals are collected by setting multiple sets of ship motion measurement systems. Finally, multiple sets of sampling frequency and multiple steps are set, and the ship motion is predicted based on the variable step-variable sampling frequency LSTM neural network. It is verified that the prediction accuracy is related to the sampling frequency of the signal collector and the prediction step of the LSTM neural network, which improves the prediction accuracy of the model and the timeliness of ship motion acquisition.

While navigating at sea, ships are influenced by various factors, including wind, waves, and currents, which can result in heave motion that significantly impacts operations and potentially leads to accidents. Accurate forecasting of ship heaving is essential to guarantee the safety of maritime navigation. Consequently, this paper proposes a hybrid neural network method that combines Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory Networks (BiLSTMs), and an Attention Mechanism to predict the heaving motion of ships in moderate to complex sea conditions. The data feature extraction ability of CNNs, the temporal analysis capabilities of BiLSTMs, and the dynamic adjustment function of Attention on feature weights were comprehensively utilized to predict a ship’s heave motion. Simulations of a standard container ship’s motion time series under complex sea state conditions were carried out. The model training and validation results indicate that, under sea conditions 4, 5, and 6, the CNN-BiLSTM-Attention method demonstrated significant improvements in MAPE, APE, and RMSE when compared to the traditional LSTM, Attention, and LSTM-Attention methods. The CNN-BiLSTM-Attention method could enhance the accuracy of the prediction. Heave displacement, pitch displacement, pitch velocity, pitch acceleration, and incoming wave height were chosen as key input features. Sensitivity analysis was conducted to optimize the prediction performance of the CNN-BiLSTM-Attention hybrid neural network method, resulting in a significant improvement in MAPE and enhancing the accuracy of ship motion prediction. The research presented in this paper establishes a foundation for future studies on ship motion prediction.

The analysis of the influence of hull deformation on shaft alignment is predominately conducted using the finite element method (FEM), which is time-consuming, labor-intensive, and challenging to use for iterative hull design optimization. In this paper, hull deformation is separated into two parts—global deformation and local deformation, simplified to a single-span beam model and a grillage beam model, respectively—then solved using the matrix displacement method (MDM). Compared to FEM, the proposed method has a small calculation error, proving its correctness, while the calculation time is greatly reduced. The proposed method has been used to calculate the hull deformation of a ship under various conditions and evaluate its influence on shaft alignment. The results indicate that under certain conditions, the bearing reaction forces are constant, whereas the bearing pressure changes as a consequence of the change in shaft-to-bearing angle. The comparison between local rotation and shaft-to-bearing angle reveals that bearings in various positions follow distinct laws. We suggest that the shaft-to-bearing angle be used as an additional parameter in the evaluation of shaft alignment calculations. Moreover, when optimizing bearing pressure, bearings in different positions are affected differently by global and local deformation, and their optimization priorities are distinct.

Deviations between the design and actual shafting occur due to limitations in ship construction accuracy. Consequently, accurately obtaining the relationship between the actual shafting load and displacement relationship based on the design shafting becomes challenging, leading to inaccurate solutions for bearing displacement values and low alignment efficiency. In this research article, to address the issue of incomplete actual shafting data, a transfer learning-based method is proposed for accurate calculation of bearing displacement values. By combining simulated data from the design shafting with measured data generated during the adjustment process of the actual shafting, higher accuracy can be achieved in calculating bearing displacement values. This research utilizes a certain shafting as an example to carry out the application of the bearing displacement value calculation method. The results show that even under the action of shafting deviation, the actual shafting load and displacement relationship model can become more and more accurate with the shafting adjustment process, and the accuracy of bearing displacement values calculation becomes higher and higher. This method contributes to obtaining precise shafting adjustment schemes, thereby enhancing alignment quality and efficiency of ship shafting.

This paper presents the design and the experimental results of a new type of developable added bulbous bow that has been designated as a dihedral bow. This type of bow is based on polyhedral bows that are used in small vessels, whose origin is traced to the 1990s. The bow is designed with a set of developable surfaces that are designed following previous methodology on surface design that considers material properties and can contain boundary curves. Two dihedral bow designs and their towing tank tests are presented in this work. A displacement and a semi-displacement hull were tested in two different loading conditions and for different Froude numbers. An important reduction of the effective power (PE) of the ships with the dihedral bow was observed during the experiments. There is a reduction of about 20% for the displacement hull and about 16% for the semi-displacement. The design methodology for the dihedral bows is presented in this paper together with experimental results on power, sink and trim. Dihedral bows are a good option for efficient small ship design, as well as larger ships.

Yuan, H.; Li, F.; Yan, Q.; Zhang, W., and Hu, J., 2023. Investigating pressure fluctuations on marine vessel rudders: Numerical results from propeller–rudder–hull interactions. Journal of Coastal Research, 39(2), 284–295. Charlotte (North Carolina), ISSN 0749-0208. Severe pressure fluctuations induced by propeller wake enhance rudder vibrations. In the present study, the pressure fluctuations on the rudder are numerically investigated by using k-ω Menter's shear stress transport turbulence model. The pressure fluctuations on the port and starboard sides of the rudder are compared in time and frequency domains. Furthermore, the impact of the advance coefficient on the displacement of propeller wake and pressure fluctuations is also discussed. The numerical results indicate that the propeller wake displacement is oriented upward and downward at the port and starboard sides, respectively. The most intense pressure fluctuation occurs at the propeller blade passage frequency. As a result of vortex displacement, the suction-side fluctuations are weaker than those on the pressure side at frequencies of 100–300 Hz over an advance coefficient range of J = 0.75–0.925. The displacement of the propeller wake weakens when the advance coefficient decreases.

The estimation of a ship’s displacement volume, ∇, from remote sensing data is of considerable practical value for maritime surveillance and vessel characterization. This paper introduces a practical framework for the inverse estimation of displacement volume from Kelvin ship waves, building upon a prior study through two key extensions. First, the wave amplitude function is recovered using Fourier series expansions combined with the stationary phase method. The displacement volume is then estimated via a two-step procedure: an initial estimate is obtained by identifying a hull with similar amplitude characteristics from a database, followed by a refinement that incorporates discrepancies between the target and candidate wave amplitude functions. In the case studied, the proposed approach achieves a prediction error of 4.02%, demonstrating its potential for non-invasive extraction of hull information from remote sensing data.

In addressing the challenges of trajectory prediction in multi-ship interaction scenarios and aiming to improve the accuracy of multi-ship trajectory prediction, this paper proposes a multi-ship trajectory prediction model, GL-STGCNN. The GL-STGCNN model employs a ship interaction adjacency matrix extraction module to obtain a more reasonable ship interaction adjacency matrix. Additionally, after obtaining the distribution of predicted trajectories using the model, a model predictive control trajectory correction method is introduced to enhance the accuracy and reasonability of the predicted trajectories. Through quantitative analysis of different datasets, it was observed that GL-STGCNN outperforms previous prediction models with a 31.8% improvement in the average displacement error metric and a 16.8% improvement in the final displacement error metric. Furthermore, trajectory correction through model predictive control shows a performance boost of 44.5% based on the initial predicted trajectory distribution. While GL-STGCNN excels in multi-ship interaction trajectory prediction by reasonably modeling ship interaction adjacency matrices and employing trajectory correction, its performance may vary in different datasets and ship motion patterns. Future work could focus on adapting the model’s ship interaction adjacency matrix modeling to diverse environmental scenarios for enhanced performance.

It is of the utmost importance to accurately estimate different ships’ weights during their design stages. Additionally, lightship displacement (LD) data are not always easily accessible to shipping stakeholders, while other ships’ dimensions are within hand’s reach (for example, through data from the online Automatic Identification System (AIS)). Therefore, determining lightship displacement might be a difficult task, and it is traditionally performed with the help of mathematical equations developed by shipbuilders. Distinct from the traditional approach, this study offers the possibility of employing machine learning methods to estimate lightship displacement weight as accurately as possible. This paper estimates oil tankers’ lightship displacement using two ships’ dimensions, length overall, and breadth. The dimensions of oil tanker ships were collected from the INTERTANKO Chartering Questionnaire Q88, available online, and, because of similar block coefficients, all tanker sizes were used for estimation. Furthermore, multiple linear regression and extreme gradient boosting (XGBoost) machine learning methods were utilised to estimate lightship displacement. Results show that XGBoost and multiple linear regression machine learning methods provide similar results, and both could be powerful tools for estimating the lightship displacement of all types of ships.