Search

[Objectives]In recent years, ships are becoming larger, faster and more intelligent, so the ship dynamic positioning technology is particularly important. In order to establish a kinematic mathematical model in the dynamic positioning system,we need to determine the values of the parameters in the model.[Methods]Firstly,a dredger is used as the research object to establish the mathematical model of the ship movement,and the surge motion model and the sway and yaw motion model are extracted. The unknown parameters in the model are identified based on the system identification theory and the Feedback Particle Filter(FPF)algorithm,including the thrust coefficients of two main thrusters and one side thruster. Then,the parameters to be identified are obtained by the simulation experiment.[Results] Finally,by comparing with the Extended Kalman Filter(EKF)algorithm,it is shown that the FPF algorithm is better at identifying the parameters and the reliability of FPF algorithm is verified.[Conclusions]This method has good application prospect in ship dynamic positioning system.

Aiming at the problem in which a vessel's dynamic positioning system can control it at an expected position,a novel linear active disturbance rejection controller is designed to solve the problem of poor dynamic performance due to the omission of a tracking differentiator. Based on the advantages of linear and nonlinear tracking differentiators,an improved tracking differentiator is designed which can track the differential signal and degrade the effects of noise; it constitutes a novel Linear Active Disturbance Rejection Controller(LADRC). The simulation results show that the novel LADRC based on the improved tracking differentiator has strong robustness,high control accuracy and good dynamic performance compared with the traditional LADRC.

Trailing suction hopper dredger is a kind of hydraulic dredger, it has the characteristics of self-propelled, selfloading, self-dredging, self-unloading, it is the main force in dredging and blowing works, it is widely used in the world, it can be said that where there is a big dredging project where there is a trailing suction hopper dredger's figure. The loading optimization process of trailing suction hopper dredger contains a lot of dredging parameters related to soil type, and the soil type under different working conditions is not very clear. In this study, we present a hybrid optimization technique based on simulated annealing and multi-population genetic algorithm to enhance the loading efficiency of a trailing suction hopper dredger and to examine the variation of dredged soil parameters. The soil parameters of the spoil hopper deposition model were estimated using this hybrid optimization algorithm. The experimental results show that the soil parameters are successfully estimated and verified by our measured construction data of a trailing suction hopper dredger. In addition, our proposed method has the highest accuracy of soil parameter estimation, the fastest algorithm convergence, and excellent robustness compared to the other three intelligent optimization methods. In addition, our method successfully avoids the phenomenon of premature convergence that usually occurs in traditional genetic algorithms, and the parameters show strong adaptability to different vessels under the same dredging area. [ABSTRACT FROM AUTHOR]

Affected by complex fault blocks, sedimentary environment and reservoir physical properties, Jidong Oilfield generally develops small-scale sand bodies, accounting for 24.2% of the produced reserves of medium and low permeability reservoirs. Such sand bodies rely on elastic energy to develop and are difficult to use effectively. In this paper, through the three-dimensional hydraulic physical model experiment and the analysis of the oil layer-dry layer-mudstone mixed sand body, the reasons that affect the model parameters are obtained. The results show that: the length of the high-permeability reservoir area, the length of the intermediate-permeability dry layer area, the rate of change of permeability parameters, the viscosity of the construction fluid, and the construction displacement have an impact on the effect of fracturing enhanced injection energy storage.

The dredging output of suction dredger mainly comes from the suction density of the rake head. Accurate prediction of suction density is of great significance to improve the dredging output of suction dredger. In order to overcome the shortcomings of low accuracy and poor real-time performance of the current inhalation density prediction methods, a bat algorithm is proposed to optimize the inhalation density prediction method of extreme learning machine. The bat algorithms for optimizing extreme learning machines prediction model is constructed based on the measured construction data of “Xinhaifeng” Yangtze Estuary, and compared with other prediction models. Finally, the bat algorithms for optimizing extreme learning machines model is used to build the output simulator of inhalation density. Compared with the actual construction, the selection of control parameters is analyzed when the output of inhalation density is the best. Experients show that bat algorithms for optimizing extreme learning machines prediction has high accuracy and good stability, and can provide scientific and effective reference for yield prediction and construction guidance.