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Ship motion (SHM) forecasting value is an important parameter for ship navigation and operation. However, due to the coupling effect of wind, wave, and current, its time series has strong nonlinear characteristics, so it is a great challenge to obtain accurate forecasting results. Therefore, considering the strong nonlinear of SHM time series, firstly, this paper decomposes the original time series into multiple intrinsic mode functions (IMF) using empirical mode decomposition (EMD) technology and then establishes a hybrid deep learning network for each IMF based on convolutional neural network (CNN) and gated recurrent unit (GRU) according to the characteristics of SHM time series. On this basis, the EMD-CNN-GRU (ECG) hybrid forecasting model of SHM is constructed by integrating a component forecasting model. Secondly, considering the difficulty of hyper-parameters selection of ECG model, this paper improves the butterfly optimization algorithm (BOA) based on quantum theory, designs the quantum coding rules of butterfly spatial position, establishes the optimization process of butterfly algorithm based on quantum coding, and then proposes the quantum butterfly optimization algorithm (QBOA). Finally, a hybrid forecasting approach integrating ECG and QBOA is proposed, namely ECG & QBOA. To evaluate the feasibility and performance of the proposed approach. A prediction experiment was carried out with the SHM data of a real ship. The results indicate that, compared with the other comparison models selected in this paper, ECG-based models have significant higher forecasting accuracy (with MAPE values of 10.86% and 12.69% in two experiments, respectively, and with significant accuracy improvement of at least 10% than other compared models), and the QBOA has obtained more appropriate hyper-parameters combination of ECG model.

Lake surface fluxes provide important information about the lake's thermal environment. To capture their spatial variations, a ship serves as an excellent platform for applying the eddy correlation (EC) method. Although ship‐based EC measurements have been conducted over the ocean, this has not been the case over lake surfaces. Ship‐based measurements in a lake differ from those over the ocean in terms of the freedom to select the ship, route, and operation, as well as the wave regime, creating measurement conditions that have not been addressed in ocean studies. Thus, 10‐day EC flux measurements on the highly maneuverable yet stable research ship NIES'94 were conducted in Lake Kasumigaura (surface area of 172 km2), which facilitated extensive data analysis on the ship's motion and fluxes under various conditions. The results indicated that the ship's motion differs greatly depending on the ship's shape and dimensions, and that a larger fluctuation in roll and pitch angles propagates into a larger error of the vertical wind velocity measurements. The motion correction was found necessary for momentum fluxes, while it is preferable but may not be essential under favorable conditions for scalar fluxes. Comparisons between the fluxes obtained from the EC method and those from the bulk method showed that the ship's speed and direction and wave height have minimal impact on the agreement, reflecting the use of a stable ship and lower wave height in our study, leading to small ship motion in Lake Kasumigaura compared to the ocean.

A ship motion time series (SMTS) exhibits obvious periodicity under the effects of periodic wave and strong nonlinearity owing to wind, ocean currents, and the load of ship itself, which make accurate forecasting difficult. To improve forecasting accuracy, this investigation divides the SMTS into a periodic term and a nonlinear term and forecasts each term separately. First, the periodogram estimation method (PEM) is implemented to forecast the periodic term. Then, owing to the strong nonlinearity of SMTS, the LSSVR model is used to forecast the nonlinear residual term that is generated by the PEM. On account of parameters that determine the predictive accuracy of the LSSVR model, the chaotic cloud particle swarm optimization (CCPSO) algorithm is introduced to optimize the parameters of the LSSVR model. Finally, combining the PEM, LSSVR model, and CCPSO algorithm, a hybrid forecasting method for SMTS, PEM&LSSVR-CCPSO, is developed. Subsequently, SMTS data for two ships that are sailing on the ocean are used as a numerical example, and thus, the forecasting performance of the presented method is evaluated. The results of the analysis demonstrate that the proposed hybrid SMTS forecasting scheme has better forecasting performance than classical forecasting models that are considered herein.

To forecast the hydrodynamic interference between ships accurately and intuitively so as to study the ship maneuvering considering the effect of ship-ship interaction, it is significant for navigation safety in restricted water. This paper adopted an easy way to fully simulate interference between the ships. By taking advantage of the Computational Fluid Dynamics approach, the governing equations of viscous flow and the equations of motion of the ships are solved. The ship-ship motion induced by the hydrodynamic forces is numerically simulated so as to study the characteristic of the interaction effects between ships. To verify the validity of the present numerical methods, the numerical results about the influences on the hydrodynamic force and moment are illustrated and analyzed. The simulated results are acknowledged by the experienced ship operators.

Parametric rolling is a parametric excitation phenomenon caused by GM variation in waves. There are a lot of studies of the estimation the conditions, the occurrence, and the amplitude of parametric rolling. On the other hand, there are relatively few cases in which theoretical methods for estimating parametric roll amplitudes in irregular waves have been validated in tank tests. The primary objective of this study is to validate theoretical estimation methods for the parametric roll amplitude in irregular waves and improve their accuracy. First, the probability density functions (PDF) of the parametric roll amplitude obtained from the model ship motion experiment in irregular waves are compared with that obtained from theoretical estimation methods. Second, the method to improve the accuracy of estimation of the roll restoring variation in irregular waves is suggested. Third, the method to estimate the distribution of the maximum amplitude of parametric rolling in irregular waves. As a result, the PDFs of the roll amplitude obtained from the experiments differ from the results of theoretical estimation. After that, by correcting GM variation, the results of theoretical estimation are closer to the experimental results. Moreover, by the theoretical estimation method using the moment equation, the qualitative estimation for the PDF of the maximum roll amplitude is succeeded.

This paper proposes a method for the online parameter identification of nonlinear ship motion systems. First, the motion system of a ship is nonlinear, and in the course of sailing, the motion parameters of the ship will change with the change of the motion state of the ship and the sailing environment. To achieve the effect of real-time identification, we adopted an online receding horizon identification method. Second, identification parameters are the essential elements in the navigation control of intelligent merchant ships, and high-precision identification results can achieve better control effects. Therefore, we used an unscented Kalman filter (UKF) that has simpler mathematical structure and higher feedback efficiency than other identification algorithms listed in this paper, such as extended the Kalman filter, Kalman filtering and Ordinary Least Squares, as the identification scheme design algorithm, which is applied to ship motion system identification. Then, to solve the problem of significant identification errors in complex environments, we design a navigation identification framework combining a UKF and rolling wavelet denoising to realize the effect of the online identification of ships. Finally, a Korea Research Institute of Ships and Ocean Engineering (KRISO) Container Ship (KCS) was used for a self-navigation model experiment and data collection. The collected data and identification data were compared and analyzed. By comparing different identification algorithms before and after denoising, it was verified that the UKF algorithm proposed in this paper is superior relative to other traditional algorithms in identifying ship motion systems.

This study focuses on a large-scale cruise ship as the subject of research, with a particular emphasis on conditions not covered in the MSC.1/Circ.1533 guidelines. The investigation explores the impact of specific motion states of the cruise ship, including rolling, heeling, and trimming, on passenger evacuation times. Based on the maritimeEXODUS tool, simulations were conducted to replicate the evacuation process in these unique scenarios. The results of the simulations highlight a significant correlation between the cruise ship’s motion state and evacuation time. Specifically, under inclination conditions, evacuation times were extended, with bow trimming leading to a notable increase in the time. This study underscores the importance of considering the motion state of a cruise ship in evacuation procedures, confirming the validity of the numerical simulation for studying large-scale cruise ship evacuations under inclination and rolling conditions. The findings contribute valuable insights for enhancing safety protocols and optimizing ship arrangements.

Under the influence of variable sea conditions, a ship will have an oscillating motion comprising six degrees of freedom, all of which are connected to each other. Among these degrees of freedom, rolling and pitching motions have a severe impact on a ship’s maritime operations. An accurate and effective ship motion attitude prediction method that makes the prediction in a short period of time is required to guarantee the safety and stability of the ship’s maritime operations. Traditional methods are based on time domain analysis, such as the autoregressive moving average (ARMA) models. However, these models have limitations when it comes to predicting the nonlinear and nonstationary characteristics of real ship motion attitude data. Many intelligent algorithms continue to be applied in nonlinear and nonstationary ship attitude prediction, such as extreme learning machines (ELMs) and the long short-term memory (LSTM) neural network, as well as other deep learning methods, showing promising results. By using the sliding window approach, the time-varying dynamic characteristics of the ship’s motion attitude can be preserved better. The simulation results demonstrate that the proposed model performs well in terms of predicting the nonlinear and nonstationary ship motion attitude.

This study numerically investigates the effect of hydrofoil installation on the motion responses and ride comfort of a 20 m monohull vessel operating at 10 knots in regular waves. Linear seakeeping analysis (Maxsurf Motions) and nonlinear computational fluid dynamics (CFD) simulations (STAR-CCM+) are performed to compute response-amplitude operators (RAOs); for the bare hull, the two methods agree within 5%, confirming methodological reliability. The CFD results show that hydrofoils reduce heave and pitch amplitudes by approximately 16% on average. Motion Sickness Incidence (MSI) analysis indicates negligible seasickness under Gentle Breeze conditions, even during prolonged exposure; under Moderate conditions, no seasickness is predicted within 30 min across all encounter frequencies. Although linear analysis cannot directly estimate MSI for hydrofoil-fitted cases, the observed reductions in RAOs imply improved ride comfort. Overall, these findings demonstrate that hydrofoils can enhance motion stability and passenger comfort in small, low-speed vessels, providing quantitative evidence to support design applications.

Ships experience rolling motion under the action of sea waves and may even face the risk of capsizing. Anti-rolling devices are designed to reduce this motion and enhance vessel safety. This is especially critical for engineering ships operating at sea under zero-speed conditions, where a stable posture is essential for efficient performance. Gyro stabilizers can suppress roll motion at zero speed; however, their high cost typically makes them unsuitable for large civilian vessels. Additionally, most existing anti-rolling devices rely on a certain water speed to function, which results in increased drag. In this study, an anti-rolling system incorporating swing control is proposed. Inspired by the human body’s ability to maintain balance by swinging arms during walking or running, the system generates an anti-rolling moment by oscillating a water tank. This approach operates independently of water speed and does not generate additional drag. The mechanical design of the anti-rolling system is introduced, and a corresponding control system model is derived. The swing-tank mechanism provides phase lead compensation and reduces the system’s sensitivity to wave disturbances. To enhance performance, robust control techniques are applied. Simulation results demonstrate that the proposed anti-rolling system delivers effective roll reduction for ships.