Maneuvering Motion Research Articles

Validation of maneuvering simulations for a KCS at different forward speeds using the 4-DOF MMG method

Ships with a high center of gravity and at relatively fast speed, such as container vessels, may experience significant heel during turning at a rudder angle of 35°. Accurate prediction of the maneuvering motion of such ships requires consideration of roll motion in addition to surge, sway, and yaw motions. This study applies the 4-Degrees of Freedom (4-DOF) Maneuvering Modeling Group (MMG) method, developed by one of the authors, as a practical simulation method that includes the roll-coupling effect, to predict the maneuvering of a Kriso Container Ship (KCS). To validate the accuracy of the maneuvering simulations, the results are compared with those obtained from free-running model tests for turning and zig-zag maneuvers. Furthermore, to capture the effect of speed on maneuvering, the free-running tests are conducted by varying the approach speed of the ship model. The test results reveal that the advance in turning and the overshoot angle in zig-zag maneuvers increase with speed, while the tactical diameter decreases. The simulation results obtained using the 4-DOF MMG method are consistent with the free-running test results with practical accuracy. Therefore, the 4-DOF MMG method proves useful in predicting the maneuvering of ships, such as container ships, at fast speeds.

Non-parameterized ship maneuvering model of Deep Neural Networks based on real voyage data-driven

While Deep Neural Network (DNN) models for ship maneuvering model are commonly constructed using experimental model ships or simulation data, this study focuses on training four Non-parameter Ship maneuvering Network models (NSM_Net_models) by using real ship voyage data, then evaluating their performance by comparison. To support the data-driven approach, real ship voyage data is collected from the Voyage Data Recorder (VDR) system of a training ship. Along with this, the input variables of the models are enhanced by incorporating the past movement history of ships. Furthermore, the impact of three different durations of past ship motion history on the models’ performance is compared and analyzed. The results suggest the feasibility and practical application value of the ship maneuvering motion model built by the deep network.

Parameter Prediction of the Non-Linear Nomoto Model for Different Ship Loading Conditions Using Support Vector Regression

Significant changes in the load of cargo ships make it difficult to simulate and control their motion. In this work, a parameter prediction method for a ship maneuvering motion model is developed based on parameter identification and support vector regression (SVR). First, the effects of least-squares (LS) and multi-innovation least-squares (MILS) parameter identification methods for the non-linear Nomoto model are investigated. The MILS method is then used to identify the parameters of the non-linear Nomoto model under various load conditions, and model training datasets are established. On this basis, SVR is used to predict the parameters of the non-linear Nomoto model. The results reveal that the MILS method converges faster than the LS method. The SVR method achieves lower accuracy than the MILS method, but exhibits reasonable prediction accuracy for zigzag motions, and the maneuvering motion model can be predicted as navigation conditions change.

Maneuvering simulations at large drift angles of a ship with a flapped rudder

In this paper, a simulation method is presented for calculating the ship maneuvering motions at large drift angles, including acceleration turning and stopping. For the ship hull force model, a method to use directly the hydrodynamic force data at large drift angle conditions without a mathematical expression is employed. The hydrodynamic forces are obtained using captive model tests. The rudder force is modeled based on existing tank test results. The present models are applied to the maneuvering simulations for an inland container ship equipped with a bow thruster and flap rudder. In addition to the ordinary maneuvering motions such as turning and zig-zag maneuvers, the low-speed maneuvering motions such as acceleration turning by steering, acceleration turning using the bow thruster, and propeller reverse stopping are simulated. The simulation results are compared with the free-running model test results to validate the present method. As a result, we found that the present method can simulate not only ordinary maneuvering motions but also low-speed maneuvering motions with a large drift angle with practical accuracy.

A free surface corrected lumped parameter model for near-surface horizontal maneuvers of underwater vehicles in waves

We provide theory and results obtained from the application of a 6-degree of freedom lumped parameter maneuvering model (LPMM) able to predict the maneuvering motions of underwater vehicles navigating at shallow depth below free surface waves. The parameters of the maneuvering model are identified using a combination of steady and unsteady captive maneuvers, simulated with high fidelity computational fluid dynamic (CFD) methods. This work focuses on correcting the LPMM, which is accurate for deep water motions, to account for free surface effects. A frequency domain strip theory method is used to account for changes in the added mass due to free surface proximity, to calculate memory forces, and to estimate wave excitation forces while a 3D time domain boundary element method is used to predict steady-state wave making forces. The result is a combined maneuvering and seakeeping model for underwater vehicles operating at shallow depths below the free surface. Near-surface horizontal zig-zag motion predictions in both calm water and under waves reveal the importance of including the free surface as vehicle trajectories at shallow depths differ substantially from those at deeper submergences for identical maneuvering inputs.

Online modeling and prediction of maritime autonomous surface ship maneuvering motion under ocean waves

The development of maritime autonomous surface ships (MASS) has attracted much attention at present. As a necessary technology of MASS, ship motion prediction still has some problems, such as low efficiency, poor real-time performance, and easy to be affected by sea conditions. Aiming at the above problems, based on the mathematical model of ship maneuvering motion, this paper uses the least squares support vector machine (LS-SVM) identification algorithm, to establish an offline black-box identification model of MASS motion considering rudder angle and propeller revolution rates, and effectively predicts the motion of ships. On this basis, a sliding window is designed, and an error-based online modeling method of ship motion is proposed to realize adaptive prediction of MASS motion under the effect of ocean waves. A large MASS was selected for modeling and simulation experiments. The results show that compared with the traditional LS-SVM modeling algorithm, the model built by this method has a strong adaptive ability and can maintain high accuracy for a long time, meeting the needs of real-time prediction of MASS motion in practical applications.

Numerical investigation on the effect of interceptor mechanism on maneuvering derivatives

ABSTRACT The effect of the interceptor mechanism on maneuvering motion is still unstudied while its positive effect on the total resistance and dynamic trim angle is well-known. Therefore, in the present study, the effect of the interceptor mechanism on the maneuvering derivatives is investigated numerically. Firstly, the calm water validation is performed for the bare hull and equipped with the interceptor. Then, the horizontal Planar Motion Mechanism simulations are conducted to calculate the maneuvering derivatives via computational fluid dynamics analyses. The results show that for the selected forward velocity the existence of the interceptor increases the amplitudes of the yaw moments and the sway forces. In addition to this, it has a remarkable effect on the sway force phases while the effect of yaw moment phases can be negligible. Since it changes phases and amplitudes, it causes a considerable increase in each linear horizontal maneuvering derivative for the selected case.

Relative Orbital Motion Control of Spacecraft Based on Multi-Objective Optimization

In this paper, we consider the relative motion of a follower spacecraft orbiting the Earth in a near circular orbit with respect to a leader spacecraft. The relative orbital motion-control problem of the follower spacecraft is studied here. In order to allow the follower spacecraft to succeed in its target orbital motion maneuver, we proposed a multi-objective optimization method to solve the relative orbital motion-control problem. Firstly, a Hill–Clohessy–Wiltshire equation was used to describe the continuous relative orbital motion-control system. Then, the system was discretized into a discrete system using numerical methods. Next, a multi-objective optimization model of the relative orbital motion-control problem was formulated. In the model, two objectives, i.e., the orbital motion error and the energy consumption, were minimized simultaneously. Furthermore, the ε-constraint method was used to solve the multi-objective optimization problem and the Pareto front, which demonstrates that the trade-off between the two objectives can be achieved. Finally, numerical experiments were carried out to validate the effectiveness of the proposed multi-objective optimization approach.

Black-box modeling of ship maneuvering motion based on Gaussian process regression with wavelet threshold denoising

A system identification method based on Gaussian progress regression (GPR) combined with wavelet threshold denoising (WT) is proposed for identifying the black-box model of ship maneuvering motion. WT is applied for data preprocessing to filter out the noise in the collected data of ship motion; GPR is used for modeling with the denoised data. Two study objects are considered. One is the KVLCC1 tanker model for verifying the basic identification ability of the proposed method by utilizing the available data of zigzag tests. The other one is an unmanned surface vessel (USV) for further verifying the effectiveness and advantages of the proposed method by using the measured data of zigzag tests and turning test. For the KVLCC1, the results of the proposed method are compared with the experimental data, the results of the basic GPR model and the model identified by BPNN combined with WT. For the USV, the results of the proposed method are further compared with those of the model identified by GPR combined with lowpass filter and the model identified by BPNN combined with WT. It is shown that the proposed method is more suitable for modeling of ship maneuvering motion using limited and noisy data.

A numerical study on the ship-bank interaction at various water depths of a surface ship.

Ship maneuvering in restricted waters is a significant challenge in navigation safety due to the complex flow around the ship. In particular, when a ship travels close to a lateral bank and shallow water, the hydrodynamic interaction forces significantly influence the maneuvering motion of the ship. Maneuverability in restricted water is even more difficult for an autonomous surface ship. Therefore, it is necessary to assess the effects of maneuvering near a bank and in shallow water for an autonomous surface ship. In this study, maneuvering simulations considering the bank effect at various water depths are implemented based on hydrodynamic forces estimated using computational fluid dynamics simulation. First, virtual captive model tests at various water depths and simulations of various lateral distances to the banks are performed to estimate the hydrodynamic forces using computational fluid dynamics simulation. The simulation method is validated by comparing the simulation results of the static drift test in deep water with the measured one in the experimental method. Second, the maneuvering simulations for the turning circle test and zig-zag test at various water depths are conducted using the obtained hydrodynamic coefficients. Then, the maneuvering simulations in deep water are compared with the experiment results, and a good agreement is observed. Finally, the simulation considering the bank effects at various water depths is evaluated and discussed.

A Vulnerability Criterion of Dynamic Instability for Ship Course-Keeping in Following Waves

_ A practical criterion which can be used for assessing the course-keeping capability of a ship in following waves, is proposed and evaluated. Presently, it accounts for regular waves and it accrued from an analytical estimation of the course instability region’s boundary by applying the method of harmonic balance. The calculation is performed with regard to the third-order yaw equation, derived from a classic sway-yaw-rudder model of ship maneuvering motions, with time-dependent coefficients at two places, which is like a Mathieu-type equation extended to third order. The proposed analytical criterion was evaluated thoroughly against simulations with regard to this sway-yaw model and it was found to be adequately accurate. A supplementary quasi-static yaw stability criterion (fitting to ship operation with frequency of encounter, with respect to the waves, close to zero) was also considered in order to determine which one yields more stringent requirements, for various operating conditions. The proposed criterion could be an extra vulnerability check for broaching-to, in the context of the Second Generation Intact Stability Criteria. Introduction The difficulties of steering of ships in following seas have received the attention of the research community since more than 70 years ago (Davidson 1948). A relevant direction of research refers to the avoidance of the broaching-to instability and, in particular, to the development of practical criteria that could ensure sufficient course-keeping capability for a ship encountering steep following waves. Some classic works on this topic, such as those of DuCane and Goodrich (1962), Wahab and Swaan (1964), and Motora et al. (1981), were focused on the quasi-static condition of a ship on the wave which could be practically realized if the ship was advancing with speed equal to the wave celerity (zero frequency of encounter). As implied, these works were essentially focused on the avoidance of a type of broaching-to instability that is preceded by the realization of surf-riding, a phenomenon where the ship is forced to move with the wave, usually riding a downslope.

Research on Maneuverability Prediction of Double Waterjet Propulsion High Speed Planing Craft

A mathematical model for predicting the maneuvering motion of a ship is constructed, using a planing craft with dual waterjet propulsion as the object of study. This model is based on the standard approach of the MMG (Maneuvering Modeling Group) and uses the Runge–Kutta algorithm to solve the differential equations. For the simulation of the turning and Z-shape maneuvering motion, the RANS equation is first solved using the program STAR-CCM + and then the PMM motion of the hull is simulated using the overlapping grid approach to derive the hydrodynamic derivative. The established method for predicting the ship’s maneuverability is feasible, as shown by the calculated results, which agree well with those obtained using data from the sea trials. This method was used to simulate the rudder rotation and Z-shape motion of the planing craft at medium and high speeds to predict the maneuverability index.

Adaptive hybrid-kernel function based Gaussian process regression for nonparametric modeling of ship maneuvering motion

A novel adaptive hybrid-kernel function based Gaussian process regression (AHKGPR) is proposed for nonparametric modeling of ship maneuvering motion. With the aid of Gaussian process regression (GPR) and nonlinear kernel function, the nonparametric ship dynamic model is identified based on the training data collected during free-running tests. The hybrid-kernel function which combines the good properties of radial basis function (RBF) and polynomial function with adaptive gain coefficients is designed to improve the generalization ability and the prediction accuracy of GPR model. The hyperparameters in the kernel function and the gain coefficients are tuned and optimized by genetic algorithm (GA). To verify the effectiveness of AHKGPR, a comparative study between AHKGPR and the widely used RBF kernel function based GPR (RBFGPR) is carried out. Two cases are considered, one for the Mariner class vessel with the dataset generated from simulation and the other for the container ship KCS with the real measured dataset of free-running model tests provided by SIMMAN 2020 Workshop. The results show that AHKGPR has higher prediction accuracy and better generalization ability.

Predicting the Motion of a USV Using Support Vector Regression with Mixed Kernel Function

Predicting the maneuvering motion of an unmanned surface vehicle (USV) plays an important role in intelligent applications. To more precisely predict this empirically, this study proposes a method based on the support vector regression with a mixed kernel function (MK-SVR) combined with the polynomial kernel (PK) function and radial basis function (RBF). A mathematical model of the maneuvering of the USV was established and subjected to a zig-zag test on the DW-uBoat USV platform to obtain the test data. Cross-validation was used to optimize the parameters of SVR and determine suitable weight coefficients in the MK function to ensure the adaptive adjustment of the proposed method. The PK-SVR, RBF-SVR, and MK-SVR methods were used to identify the dynamics of the USV and build the corresponding predictive models. A comparison of the results of the predictions with experimental data confirmed the limitations of the SVR with a single kernel function in terms of forecasting different parameters of motion of the USV while verifying the validity of the MK-SVR based on data collected from a full-scale test. The results show that the MK-SVR method combines the advantages of the local and global kernel functions to offer a better predictive performance and generalization ability than SVR based on the nuclear kernel function. The purpose of this manuscript is to propose a novel method of dynamics identification for USV, which can help us establish a more precise USV dynamic model to design and verify an excellent motion controller.

Identification modeling of ship maneuvering motion based on local Gaussian process regression

A fast and accurate nonparametric modeling method based on local Gaussian process regression (LGPR) is proposed for the identification modeling and prediction of ship maneuvering motion. The training dataset collected from the free-running model tests of ship maneuvering is automatically divided into a number of clusters according to the similarity criterion by clustering analysis using k-means algorithm. Utilizing the data in each cluster, the corresponding local nonparametric model is identified. The computational cost of training and prediction based on LGPR is reduced compared to that based on the classic Gaussian process regression (CGPR) using the whole training dataset. Taking the KVLCC2 tanker and an unmanned surface vehicle (USV) as study objects, the nonparametric models are identified based on the experimental data of zigzag maneuvers of the KVLCC2 model and random maneuver of the USV. Using the identified models, the zigzag maneuvers of the KVLCC2 model and the random maneuver of the USV, which are not involved in the training data, are predicted. The results show that LGPR has higher computational efficiency than CGPR with acceptable prediction accuracy.

Hydrodynamic Derivative Calculation and Turning Performance Simulation of Amphibious Aircraft

To improve the rotation efficiency of amphibious aircraft on the water surface, CFD technology is used to simulate maneuverable motion in this paper. The numerical calculations of direct navigation, oblique towing, no drift angle in rotating arm motion, drift angle in rotating arm motion, direct navigation with rudder, and planar motion are carried out, respectively. The hydrodynamic derivatives such as position derivative, rotation derivative, coupling derivative, rudder derivative, and added mass are obtained. On this basis, the rotational performance of amphibious aircraft is predicted. The results show that when the rudder is fully deflected, the rotation radius is 3159.1 m; when the engine differential and rudder run together, the rotation radius is 1216.8 m; the engine differential can effectively improve the rotation efficiency of amphibious aircraft on the water surface.

Motion characteristics simulations of supercavitating vehicle based on a three-dimensional cavity topology algorithm

The maneuvering motion of a supercavitating vehicle pitching and yawing inevitably occurs when navigating in three-dimensional space. In order to give some insights into design of underwater vehicles, the maneuvering motion in the longitudinal and horizontal planes is investigated in this paper. Considering the definition of multi-body coordinate frames, a three-dimensional cavity topology model is proposed to predict the development of the shape of cavity and interact with the supercavitating vehicle in the simulations. Based on the cavity model, an improved planning model is established to calculate the necessary critical parameters of the planning force. According to the mathematical model above, a numerical algorithm is developed to simulate the maneuvering motion of the supercavitating vehicle. The higher algorithm efficiency is validated by the relative error of 10% order of magnitude and the more stable performance. The real-time scene figures and time-varying curves of the dynamic characteristics are obtained through the developed algorithm to study the influence of fins deflection on the maneuvering motion. The developed algorithm is validated to some extent to be a better alternative than the empirical one and provides a new method for analyses of simulation results of supercavitating vehicles.

Research on Area of Uncertainty of Underwater Moving Target Based on Stochastic Maneuvering Motion Model.

Considering the influence of measurement error on target state estimation, there is an uncertain dispersion region for target position estimate, that is, the area of uncertainty (AOU, area of uncertainty). In underwater target tracking, the state estimation is point estimation without AOU estimation and its accuracy is poor in the early stage because of large measurement errors. Fast tracking with higher accuracy and AOU estimation are of great significance to time-sensitive target tracking. To improve the state estimation accuracy in the early stage, and estimate the AOU, a method of AOU estimation of underwater moving target is presented based on a stochastic maneuvering motion (SMM, stochastic maneuvering motion) model. The stochastic maneuvering motion model is established based on the Langevin equation to reflect the movement characteristics of an underwater moving target. Then, the target state is estimated with a noise adaptive Kalman filter by constructing the measurement equation and state equation according to measurement error characteristic and stochastic maneuvering model. Based on the physical significance of the error covariance matrix from the Kalman filter, the parameters of AOU are deduced. Simulation results of underwater target tracking and AOU estimation are presented to demonstrate the relative performance of the proposed algorithm compared with the adaptive Kalman filter. It is clearly shown from the results that SMM tracking algorithm achieves higher accuracy of state estimation in the initial stage of tracking, and the predicted AOU is consistent with the actual distribution of underwater moving targets while yielding more concentrated distribution, which reveals that estimated AOU can be precisely represented by the confidence ellipses. The presented approach and obtained results may be useful in time-sensitive target threat analysis and weapon strike applications.

Numerical Simulation of the Maneuvering Motion Wake of an Underwater Vehicle in Stratified Fluid

When a vehicle moves underwater, disturbance is generated and a wake remains that destroys the original free surface and produces a new wake. In order to study the mechanism and characteristics of the wave-making wake generated by the maneuvering motion of an underwater vehicle in density-stratified fluid, the k-ε model and the VOF method that is based on the RANS equation were used in this paper to analyze the SUBOFF model in stratified fluid at different drift angles. Numerical simulation of the maneuvering motion was carried out under these angles, and the corresponding changes in flow field caused by this motion were analyzed. The results from the comparison and analysis of the surface wave wakes under different drift angles in stratified fluid show that with the increasing drift angle, the motion wake of the vehicle still exhibits obvious Kelvin wave system characteristics. However, there are significant changes in hydrodynamic performance. The asymmetry of the surrounding flow field will increase with the increase in the drift angle. The pressure of the underwater vehicle is inversely proportional to the velocity of the surrounding flow field, and the amplitude of the peak and trough of the free surface wave is linearly related to the change in the drift angle. The numerical simulation can serve as a reference for the non-acoustic detection of the motion heading of an underwater vehicle and the motion trajectory of anti-reconnaissance underwater vehicles under actual sea conditions.

Transformer-Based Maneuvering Target Tracking.

When tracking maneuvering targets, recurrent neural networks (RNNs), especially long short-term memory (LSTM) networks, are widely applied to sequentially capture the motion states of targets from observations. However, LSTMs can only extract features of trajectories stepwise; thus, their modeling of maneuvering motion lacks globality. Meanwhile, trajectory datasets are often generated within a large, but fixed distance range. Therefore, the uncertainty of the initial position of targets increases the complexity of network training, and the fixed distance range reduces the generalization of the network to trajectories outside the dataset. In this study, we propose a transformer-based network (TBN) that consists of an encoder part (transformer layers) and a decoder part (one-dimensional convolutional layers), to track maneuvering targets. Assisted by the attention mechanism of the transformer network, the TBN can capture the long short-term dependencies of target states from a global perspective. Moreover, we propose a center-max normalization to reduce the complexity of TBN training and improve its generalization. The experimental results show that our proposed methods outperform the LSTM-based tracking network.