Prediction Of Roll Motion Research Articles

An Application of the Improved Autoregressive Method for Two Vessels Roll Motion Prediction

It is of great significance and value to predict the roll of two ships, especially when one is replenishing for the other or in some other conditions. Autoregressive time series analysis method (AR) with Recursive least squares (RLS) theory is the mainstream currently and the effectiveness for the prediction of the motion attitudes have been fully validated. However, there are some differences between the prediction for the motion of two vessels and for that of one. The hydrodynamic interactions between two ships should be taken into consideration and be reflected in the application of the method. In order to solve this issue, this work firstly proposed a double ships autoregressive (DAR) method, which can determine the orders and the parameters of model in real-time with consideration of the interference between two ships and the DAR method was applied to forecast the roll motion between two ships. The simulative results of DAR method show the validity and veracity compared to real value and the advancement compared to the autoregressive (AR) method for single ship motion prediction.

Roll motion analysis of deepwater pipelay crane vessel

For a large floating vessel in waves, radiation damping is not an accurate prediction of the degree of roll unlike other degrees of freedom motion. Therefore, to get the knowledge of roll motion performance of deepwater pipelay crane vessels and to keep the vessel working safety, the paper presents the relationship between a series of dimensionless roll damping coefficients and the roll response amplitude operator (RAO). By using two kinds of empirical data, the roll damping is estimated in the calculation flow. After getting the roll damping coefficient from the model test, a prediction of roll motion in regular waves is evaluated. According to the wave condition in the working region, short term statistics of roll motion are presented under different wave parameters. Moreover, the relationship between the maximal roll response level to peak spectral wave period and the roll damping coefficient is investigated. Results may provide some reference to design and improve this kind of vessel.

ONLINE GREY PREDICTION OF SHIP ROLL MOTION USING VARIABLE RBFN

Ship's roll motion at sea is a complex system featured by nonlinearity, uncertainty, and time-varying dynamics. In this paper, a grey neural prediction scheme is presented for online ship roll motion prediction. The grey data processing approach is employed to alleviate the unfavorable effects of the uncertainty exhibited in measurement data, and grey relational analysis method is also involved to determine the structure of the grey prediction scheme. To represent the time-varying dynamics of nonlinear system, a variable structure radial basis function network (RBFN) is online constructed by learning samples in a sliding data window sequentially. Simulations of ship roll motion prediction are conducted via different approaches to validate the effectiveness of the proposed variable-RBFN-based grey prediction method. Measurement data employed in simulation is obtained during sea trials of the scientific research and training ship Yu Kun. Simulation results demonstrate the efficiency and accuracy of the proposed prediction method.

Online ship roll motion prediction based on grey sequential extreme learning machine

For the online prediction of nonlinear systems with characteristics of time-varying dynamics and uncertainty, a sequential grey prediction approach is proposed based on the online sequential extreme learning machine (OS-ELM). The grey processing of time series alleviates the unfavorable effects of uncertainty in measurement data; the extremely fast learning speed and high generalization accuracy of OS-ELM enable online application of the sequential grey prediction approach. Ship's roll motion at sea is a complex nonlinear process with time-varying dynamics. Its dynamics also involves uncertainty caused by wind, random waves and rudder actions. In this paper, the proposed OS-ELM-based grey prediction approach is implemented for online ship roll prediction. The simulation of prediction is based on measurement data obtained from sea trials of the scientific research and training ship Yu Kun. Simulation results of ship roll prediction demonstrate the effectiveness and efficiency of the proposed grey neural prediction approach in dealing with time-varying nonlinear system with uncertainty.

On-line prediction of ship roll motion during maneuvering using sequential learning RBF neuralnetworks

The on-line prediction of ship roll motion during maneuvering plays an important role in navigation safety and ship control applications. This paper presents an on-line prediction model of ship roll motion via a variable structure radial basis function neural network (RBFNN), whose structure and parameters are tuned in real time based on a sliding data window observer. The RBFNN is sequentially constructed by adding the new sample in the hidden layer and pruning the obsolete hidden units at each epoch, with the connecting parameters adjusted simultaneously. Gaussian functions with multi-scale kernel width are adopted to provide more flexible representations of model input terms and to achieve better generalization capability. Simulation study of ship roll motion prediction is conducted with measurement data of turning circle test and zigzag test in full-scale sea trial. Results demonstrate that the proposed neural network predictive model can on-line predict the roll angle with high accuracy. The predictive model is also featured with its compact network structure and fast computationalspeed.

Approximate analytical response curve for a parametrically excited highly nonlinear 1-DOF system with an application to ship roll motion prediction

The paper presents a model for a parametrically excited nonlinear 1-DOF damped mass-spring system. An approximate analytical technique is used to obtain the general polynomial whose roots give the response of the system in frequency domain when the parametric excitation acts in the first parametric resonance region. All the details needed for the implementation of the method are given. The model is suitable for the prediction of the parametrically excited roll motion of ships in longitudinal regular waves, thus some examples of application are shown to analyse the behaviour of two different hulls. The proposed approximate frequency domain analysis is shown to be able to disclose some nonlinear features of the ship response still not well known in the field of Naval Architecture.

Prediction of stabilization of ship in random wave

In this paper we show the method of energy in part with which we can get the model of random wave, and predict the roll motion of unstabilized ship and stabilized ship using the wave model. The control parameters of fin stabilizer are determined according to the performance index. The simulation of the system is also made in this paper. The comparison of the simulation with real ship indicates that the method can be used in the prediction of roll motion of a stabilized ship in random wave.

A RANS-based technique for simulation of the flow near a rolling cylinder at the free surface

Accurate prediction of roll motion is very important in ship dynamics, particularly when the safety of the ship is in question. In this paper "COMET," a Reynolds averaged Navier–Stokes (RANS) solver, is used to simulate the flow in the vicinity of a rectangular cylinder rolling at the free surface and to demonstrate the potential of RANS-based techniques in complex flows with free surfaces. The method adopted is based on an unstructured collocated finite-volume technique, which uses a variation of the SIMPLE scheme for pressure correction. An advanced interface capturing technique known as high resolution interface capturing (HRIC) is used for tracking the free surface with reduced numerical diffusion. The results derived are compared with available numerical and experimental results, showing good agreement on added moment of inertia and damping coefficients, whilst accurately predicting other main features of the flow. However, further validation for other geometries and extension to 3-D hull forms are essential before applications to practical problems are realised.

A theoretical and experimental investigation of indirectly excited roll motion in ships

The phenomenon of indirectly exciting the roll motion of a vessel due to nonlinear couplings of the heave, pitch and roll modes is investigated theoretically and analytically. Two nonlinear mechanisms that cause large-amplitude rolling motions in a head or following sea are investigated. The first mechanism is internal or autoparametric resonance and the second is parametric resonance. The energy put into the pitch and heave modes by the wave excitations may be transferred into the roll mode by means of nonlinear coupling among these modes; thus, the roll can be indirectly excited. As a result, a ship in a head or following sea can spontaneously develop severe rolling motion. In the analytical approach, the method of multiple scales is used to determine a system of nonlinear first-order equations governing the modulation of the amplitudes and phases of the system. The fixed-point solutions of these equations are determined and their bifurcations are investigated. Hopf bifurcations are found in the case of two-to-one internal resonance. Numerical simulations are used to investigate the bifurcations of the ensuing limit cycles and how they produce chaos. Experiments are conducted with tanker and destroyer models. They demonstrate some of the nonlinear effects, such as the jump phenomenon, the subcritical instability, and the coexistence of multiple solutions. The experimental results are in good qualitative agreement with the results predicted theoretically.