Ship Pitch Research Articles

Effects of adverse sea conditions on the dynamic performance of a cruise ship integrated power system

In adverse sea conditions, changes in the propeller emergence depth can cause propulsion load fluctuations, resulting in significant variations in ship-integrated power systems (IPS). These fluctuations can severely affect the power quality of ships and the operational efficiency of electrical devices. To develop appropriate mitigation measures against the impact of wind and waves on IPS, a comprehensive model that can simulate the response characteristics of the hull and IPS for different heading angles in different sea conditions is necessary. This paper presents an integrated multi-system dynamic model capable of simulating the response characteristics of a ship's hull and its IPS under the wind and waves interference. A novel method is proposed to estimate propeller thrust and torque loss directly from the ship's pitch and roll angles. Utilizing this dynamic model, the responses characteristics of the hull and IPS in varying sea conditions have been investigated. The findings demonstrate that the model accurately captures variations in ship motions, as well as IPS voltage and frequency, under wind and waves interference. Additionally, this study explores the application of a hybrid energy storage system (HESS) to enhance the IPS power quality in the context of Sea State (SS) 7. Results indicate that the HESS has good potential to compensate for propulsion load fluctuations, thereby mitigating the negative impacts on the ship's IPS.

Ship pitch prediction method based on LSTMC and multi-head attention

When ships sail in the sea, ship motion is unstable and stochasticity due to the ever-changing weather and marine conditions. Aiming to address the issue of low prediction accuracy in traditional prediction methods, short term predictions of ship pitch at 1 step, 3 steps, 6 steps, and 12 steps in advance are conducted, and the impact of input-output ratios on the prediction model is studied. The seven types of data of the marine environment and ship motion are taken as model inputs, with pitch as the model output. When predicting 1 step in advance, a prediction model based on LSTM and multi-head attention (LMA) is proposed. This model firstly uses LSTM layer to extract the temporal feature of the input sequence, and then uses the multi-head attention layer to assign larger weights to the key information to enhance prediction accuracy. When predicting 3 steps, 6 steps, and 12 steps in advance, as the input-output ratio increases, the length of the input sequence keeps growing. The model needs to sufficiently capture the local spatial characteristic and long-range dependency between multi-variable inputs, a deep prediction model based on Conv2d and LMA model is proposed. The model firstly uses two-dimensional convolution layer to extract local spatial feature of long sequences, and then through the attention layer, assign larger weights to temporal information and spatial information that are conducive to continuous prediction in order to enhance prediction accuracy. Research on the input-output ratio indicates that, as the number of predicted steps increases, the appropriate ratio continuously decreases, but the length of the input sequence keeps increasing. Finally, four evaluation metrics are used to evaluate the proposed algorithms on real ship data, and the prediction stability and accuracy of the LMA model and the LCMA model are also verified.

Numerical analysis on the seakeeping performances of a full-scale container ship hull using strip theory

The race of taking more cargo on a ship has increased the size of ships as well as other aspects, such as their capacity and structural complexity, which affect the stability of ships. For naval architects and academics, accurately predicting seakeeping performances is difficult. In order to address this, the seakeeping performance of a container ship hall built by Korea Research Institute for Ships and Ocean Engineering (KRISO), i.e., KRISO container ship hall (KCS) is described in this research utilizing a numerical method. Maxsurf software based on strip theory was used to determine the results, where the containership hull was considered and input motions were applied with appropriate boundary conditions. Later, the ship's heading and speed were changed to see the effect of the seakeeping performance of the container ship. The current study is concentrated on systematic comparative research on the investigation of the ship's pitch, heave, and roll movements in irregular waves. It has been found that rolling motion was the highest at 22 kn at the 60° heading angle, potentially affecting ship stability. The significant amplitude analysis indicates that the roll motion is the largest at 60° of wave direction. The pitch motion response and the heave motion are near for each heading angle when the wave frequency exceeds 0.5 and 1 rad/s, respectively. The calculation findings show that seakeeping performance is directly related to ship direction and speed. Furthermore, threatening heading angles during sailing are classified, which could also help in enhancing ship stability.

Estimation of sea state parameters from ship motion responses using attention-based neural networks

On-site estimation of sea state parameters is crucial for ship navigation. Extensive research has been conducted on model-based estimation utilizing ship motion responses. Model-free approaches based on machine learning (ML) have recently gained popularity, and estimation from time-series of ship motion responses using deep learning (DL) methods has given promising results. In this study, we apply the novel, attention-based neural network (AT-NN) for estimating wave height, zero-crossing period, and relative wave direction from raw time-series data of ship pitch, heave, and roll. Despite reduced input data, it has been demonstrated that the proposed approaches by modified state-of-the-art techniques (based on convolutional neural networks (CNN) for regression, multivariate long short-term memory CNN, and sliding puzzle neural network) improved estimation MSE, MAE, and NSE by up to 86%, 66%, and 56%, respectively, compared to the best performing original methods for all sea state parameters. Furthermore, the proposed technique based on AT-NN outperformed all tested methods (original and enhanced), improving estimation MSE by 94%, MAE by 74%, and NSE by 80% when considering all sea state parameters. Finally, we proposed a novel approach for interpreting the uncertainty estimation of neural network outputs based on the Monte-Carlo dropout method to enhance the model’s trustworthiness.

Analysis of the amplitude-frequency characteristics of the on-board pitching obtained during the operational flight

Problems with ensuring the safety of navigation of vessels in waves and with optimizing the parameters of the vessel's movement currently arise, in particular, due to the inability to use new achievements in navigation of related scientific disciplines, such as the theory of ship pitching, oceanography. This is partly due to the lack of methods for obtaining the spectrum of actual waves in the vicinity of a ship that is on an operational voyage and does not have the ability to make stops to record waves, the absence of ship pitching recorders in the widespread practice of navigation and the lack of software for complex processing of the necessary information. At present, the SGUVT has created software and hardware complexes suitable for use by the full-time crew on ships in operation.
 Modern software and hardware complex provides the possibility of automated recording of the ship's pitching and its processing. The pitching record can be processed to obtain an amplitude.

UAV control with active disturbance suppression for the safety landing on an aircraft carrier

Atmospheric disturbances consist of a serious danger to the UAV when landing on an aircraft carrier. The problem of atmospheric disturbances suppression is considered. Two sources of disturbances take into account: wind and aircraft carrier pitching. The corresponding disturbance models are constructed. An algorithm for controlling the attitude and speed of the UAV by using the method of active disturbance suppression (ADRC) is proposed. Flight simulation in the Simulink environment is carried out. The simulation results confirmed the efficiency, accuracy and practicality of the proposed algorithm. Keywords landing on an aircraft carrier, UAV, atmospheric disturbances, ship pitching, ADRC, control with active disturbance suppression

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.

Attitude prediction of ship coupled heave–pitch motions using nonlinear innovation via full-scale test data

—This paper predicts ship seakeeping based on nonlinear innovation. An improved extended Kalman filter (EKF) algorithm is proposed to predict ship pitch attitude based on Markov process model. The stochastic attenuation equation of coupled pitching motion is derived by using the Fokker-Planck equation. The parameters in these equations are then determined using the proposed algorithm. The hyperbolic tangent function is introduced and the nonlinear innovation method is used to process the error. By comparing the numerical simulation and full-scale test data, the effectiveness of the method is verified. The experimental data were obtained by using the “Yukun” ship. The method is more suitable for the case where the system is of small damping. Compared with traditional identification algorithms, the algorithm proposed in this paper has higher prediction accuracy. The real-time prediction results are compared with the full-scale data, which shows that the proposed ship prediction model has significant prediction accuracy and the algorithm is reliable. This parameter identification method can be used to establish ship maneuvering prediction model.

Ship Pitch Prediction Based on Bi-ConvLSTM-CA Model

When a ship is sailing at sea, its pitch angle will be affected by ship motions such as turning angle, relative wind speed, relative wind direction, velocity in surge and velocity in sway of the ship. Due to the randomness of ship motion attitude and the difficulty of capturing the motion rules, traditional machine learning models, statistical learning models and single deep learning models cannot accurately capture the correlation information between multiple variables, which results in poor prediction accuracy. To solve this problem, the bidirectional convolutional long short-term memory neural network (Bi-ConvLSTM) and channel attention (CA) for ship pitch prediction are used to build a Bi-ConvLSTM-CA model in this paper. The Bi-ConvLSTM-CA prediction model can simultaneously extract the time information and spatial information of the ship motion data, and use the channel attention mechanism to process the output of different time steps to obtain the corresponding weight of each channel. Using the weights to do dot product with the output of Bi-ConvLSTM, the resulting attention mechanism output is processed to produce predicted value by the fully connected layer. Compared with other models, the RMSE index of Bi-ConvLSTM-CA model decreased by at least 28.20%; the MAPE index decreased by at least 29.39%; the MAE index decreased by at least 22.68%. The experimental results of real ship data show that the proposed Bi-ConvLSTM-CA model has a significant reduction in mean absolute percentage error (MAPE), mean square error (MSE) and mean absolute error (MAE) compared with other advanced models, which verifies the effectiveness of the Bi-ConvLSTM-CA model in predicting ship pitch angle.

Determination of the ship's pitching parameters during an operational voyage

In this article, it is proposed to use a hardware and software complex created on the basis of microcontroller technology to record and visualize the parameters of the ship's pitching during an operational voyage. The ability to continuously record the parameters of the keel and side pitching of the vessel in various weather conditions and the changing loading of the vessel allows you to quickly monitor the stability parameters and improve the safety of navigation in difficult weather conditions of stormy navigation.
 The track recorded in MS EXCEL with the pitching parameters and the current time allows you to compare the recording time with the effect of various hydrometeorological conditions, taking into account both wind and wave effects on the vessel, including from hydrometeorological objects located at a considerable distance from the current position of the vessel. These data make it possible to carry out a harmonic analysis of pitching, to obtain the frequency spectrum, phase-frequency characteristics, proper pitching period and amplitude-frequency characteristics of the vessel.

A wave foil with passive angle of attack adjustment for wave energy extraction for ships

The application of new energy to augment a ship's propulsion is an effective way to achieve energy saving and emission reduction in ships. Wave foil is a promising device that can extract energy from sea waves to augment a ship's propulsion and reduce the ship's pitch motion in rough seas. This paper proposes a wave foil with a passive angle of attack adjustment, where the changes in angle of attack can promote the extraction of energy from the waves. As the wave foil moves up and down in the water, the angle of attack changes passively in response to the pitching of the ship. Spring damping is used to constrain the change in the angle of attack and in this way, the foil generates the propulsive force for the ships. The model of heave and pitch motion with the wave foil is derived and the simulation is carried out. The analysis shows that the proposed wave foil can extract wave energy to propel the ship and reduce the pitching motion of the ship.

Comparative Analysis of Pitching Prediction Algorithms

Two algorithms are described in the paper; one of them is the Kalman filter, which is based on the use of a pitching mathematical model, and the second uses a neural network in which the model is considered unknown. The results of the algorithms sensitivity analysis to the parameters of the model and its influence on the potential accuracy of prediction are presented. A stationary narrow-band second-order Markov process is used as a model of the ship pitching, which was used to form the input signal of the algorithms. Also, the results of the algorithms simulation in predicting real data are presented.

Удосконалення системи управління гребними електрорушіями при плаванні в умовах хитавиці

У статті розглянуто питання управління судном з електрорушієм в умовах хитавиці. При ході судна в умовах хвилювання дизель-генератор працює в режимах перевантаження, що значно скорочує експлуатаційні характеристики пропульсивного комплексу. Аналіз хитавиці судна, вказує на випадковий характер постійно діючого обурення різної величини і тривалості. При роботі ГЕУ в таких умовах мають місце коливання моменту опору на гребному валу (якщо відсутнє відповідне регулювання збудження ГЕД), моментів опору на валах дизелів, що визначаються електромагнітними моментами генераторів. Квазістаціонарний характер зміни моменту пояснюється таким же характером зміни моменту опору обертанню гребного гвинта. Стабілізацію кутової швидкості можливо досягти зміною упору лопатей азіподу (ГРК), а, отже удосконалити систему управління ГРК.

Nonlinear dynamics of the marine rotor-bearing system during the pitching motion

In order to study the motion law of the rotor-bearing system under ship pitching motion, the dynamic model of the marine rotor-bearing system is established. The dynamic characteristics of the system with different speeds are analysed by numerical method, such as Poincaré map, the frequency spectrum, and the largest Lyapunov exponents. The results show that with the increase of the rotational speed, there are abundant dynamic phenomena, such as single period, quasi-period and quasi-period of the upper and lower branches. Moreover, due to influence of the pitching motion of ship, the rotor tilts in the bearings and its procession occurs obviously, which affects the stability of the operation of rotor-bearing system. The findings of this study will contribute a comprehension of the nonlinear dynamic behaviours of the rotor-bearing system subjected to ship pitching motion.

Comparative analysis of algorithms for ship pitching forecasting

An overview and analysis of various ship motion prediction algorithms based on its description by random or quasideterministic processes are carried out. The testing of real data is conducted to estimate the performance of algorithms.

A Practical Approach to Numerically Predicting a Maneuvering Vessel in Waves Oriented to Maritime Simulator

To improve the behavioral realism of maritime simulator, the mathematical model of ship heave and pitch motion in regular waves is proposed. To avoid the influence of irregular frequency by two-dimensional Frank source and sink distribution method, the multiparameter conformal mapping method was adopted to solve the hydrodynamic problem of ship’s transverse sections, and then the integration of the hydrodynamic coefficients and the wave exciting forces for the whole ship hull was obtained using the Salvesen–Tuck–Faltinsen (STF) strip method. The Abkowitz model was used for the horizontal maneuverability motion equations, and the ship heave and pitch motion mathematical model in regular waves can be built considering the maneuvering factors. To verify the credibility and applicability of the improved model, the Mariner vessel with a Froude number of 0.2 was taken as the simulation case; when Mariner vessel sails in head waves, the maximum relative error between the calculated results and the experimental results is 23.6% for amplitude response operators of ship heave and pitch motion, and the calculated results and the experimental results have the same trend. When turning maneuvers in regular waves are carried out, the time history of ship pitch and pitch motions in regular waves does not show the sinusoidal periodic change any more, but the time history of heave and pitch motion still possesses periodic change to some extent, at which the period is about the time required for the turning motion to finish a circle. When turning maneuvers are carried out for 3000 seconds, the time required for computer operation is about 487.6 s, and the real-time requirements of maritime simulator are satisfied. Thus, the mathematical model of ship heave and pitch motion established in this paper can be effectively applied to the maritime simulator.

Initial Alignment of Shipborne SINS under Ship Motion

Abstract—The paper focuses on improving the accuracy and shortening the time of shipborne SINS initial alignment under the ship yawing, rolling and pitching motions. This is achieved by implementing a two-step SINS alignment algorithm. At the first step, the ship attitude parameters are approximately autonomously estimated by data from gyros and accelerometers. The description of ship dynamics is introduced, and water speed log data are applied. At the second step, the system fine alignment is performed with account for alignment errors after the completion of the first step. Speed and position measurements from external aids are additionally applied during the fine alignment. Kalman filter algorithms are used at the first and second steps. Results from bench and sea tests for SINS on navigation grade FOGs under the ship yawing, rolling and pitching motion are provided.

О начальной выставке корабельной БИНС в условиях качки

The paper focuses on improving the accuracy and shortening the time of shipborne SINS initial alignment under the ship yaw, roll and pitch. This is achieved by implementing a two-step SINS alignment algorithm. At the first step, the ship current attitude parameters are approximately autonomously estimated by data from gyros and accelerometers with account for its dynamics and using water speed log data. At the second step, the system fine alignment is performed with account for alignment errors after the completion of the first step. Speed and position measurements from external aids are additionally applied during the fine alignment. Kalman filter algorithms are used in the first and second steps. Results from bench and sea tests for SINS on navigation grade FOGs under the ship yaw, roll and pitch motion are provided.

Improving deterministic pitch motions estimation using bivariate sequential wave input

As the ship navigates through waves, it will sway continuously with six degrees of freedom, which will adversely affect the offshore operations. Accurate real-time estimation of deterministic ship motions under wave excitation is a key to ship motion prediction and assistance decision-making. In the actual marine environment, ocean waves and ship motions are often nonlinear. Therefore, an effective nonlinear estimation model to accurately estimate the real-time response of the wave-induced ship motions is of great concern. Due to its unique advantages in dealing with nonlinear time series, Long Short-Term Memory network can provide a powerful method for the estimation of nonlinear wave-induced ship motions. Pitch motions as an oscillating motion put forward higher requirements for model input. Based on the Long Short-Term Memory network model using the wave time history information as input to estimate the ship pitch motion, this paper proposes a pitch estimation model received a bivariate sequential wave time series as input. With the use of the nonlinear wave generated by numerical simulation and the corresponding ship motion time history data, the feasibility of the new model is verified and compared with the corresponding single-point sequential wave input model, determined its superiority.

Numerical Prediction of Wave Piecing Catamaran in Regular Waves

The present work is devoted to the prediction of added resistance and the free heave and pitch motion response in regular waves. The numerical results are analyzed in terms of added resistance, ship motions. Both free to heave and pitch are studied to investigate the contribution to added resistance from ship motion at different wavelengths, and the results show that when the wavelength λ < 4Lpp the added resistance induced by ship motion is not negligible. The comparison with experimental results shows that unsteady RANS predicts the ship motion of heave and pitch well in all wavelengths. Ship pitch and heave motion in regular head waves can be estimated accurately by CFD.