Motivated by the application of water-entry problems in the air-drop deployment of a spherical oceanographic measuring device, the free-fall water entry of a sphere was numerically investigated by using the transient Reynolds-averaged Navier–Stokes (RANS) method. A convergence study was carried out, which accounts for the mesh density and time-step independence. The present model was validated by the comparison of non-dimensional impact force with previous experimental and numerical results. Effects of parameters, such as impact velocity, radius, and mass of the sphere on the impact force and the acceleration of the sphere, are discussed. It is found that the peak value of the non-dimensional impact force is independent of the impact velocity and the radius of the sphere, while it depends on the mass of the sphere. By fitting the relationship between the peak value of the non-dimensional impact force and the non-dimensional mass, simplified formulas for the prediction of peak values of the impact force and the acceleration were achieved, which will be useful in the design of the spherical oceanographic measuring device.

Due to the complex fluid interference between outrigger and main hull, it is difficult to predict the trimaran load fluctuation accurately in different marine environments. Therefore, a time-domain Rankine method is developed to improve the accuracy...

A complete application research of Fiber Bragg Grating sensing technology in ship structure stress monitoring and evaluation system is studied in this paper. The working principles of Fiber Bragg Grating strain sensor in monitoring system are briefly introduced. And a kind of new differential FBG strain sensor for ship structure stress monitoring is applied and verified by a series of calibration and force measurement test systematically. Through finite element calculation and test towards ship structure, some defects of monitoring sensor used in bending deformation are found. Then, a three-dimensional strain sensor arrangement for stress monitoring of hull structure is designed and developed. The satisfactory test results are obtained by this sensor arrangement. Finally, the accuracy and reliability of hull structure monitoring system is improved further through this research results.

To prevent signal faults from causing misjudgements of structural health and even catastrophic accidents in ship monitoring systems, a fault diagnosis and restoration method is proposed by using machine learning. The method integrates advanced technologies including wavelet transform, IPSO-BP neural network, CNN, event-triggered sampling based on predictive zero-crossing instant, and adaptive segmented least squares algorithm, focusing on efficient identification and rectification of the typical signal faults. Based on the monitoring data of a model test, an impact analysis on the fault diagnosis and restoration method is carried out, covering aspects like noise resistance, fault severity, fault occurrence time, and ship monitoring position. The result indicates that the proposed method has a total diagnostic rate of no less than 98 % at different monitoring positions, and its noise-resistance ability is superior to that of the LSTM and Random Forest algorithms. Moreover, it delivers outstanding restoration effects. Compared to traditional mean segmentation, it reduces RMSE by 73.86 % for bias faults, 75.49 % for drift faults, and 19.55 % for impulse faults. This method can effectively enhance the stability of ship structural health monitoring systems, providing critical technical support for intelligent ship navigation safety.