Shaojuan Su

To meet the propulsion requirements of a novel shallow-water seismic survey ship, a Ka+19A Z-drive ducted propeller was designed based on ship model resistance tests using the propeller chart method. Numerical simulations of open water tests were conducted using Reynolds-Averaged Navier-Stokes (RANS) and transient Rigid Body Motion (RBM) methods, obtaining the open water performance curves. Compared to the original propeller test values, the results verified that the designed propeller could provide effective thrust for the shallow-water seismic survey ship, ensuring its smooth navigation. By varying water depth conditions, the effects of different water depths on the hydrodynamic performance of the Z-drive ducted propeller were investigated, and the influence of shallow-water effects on vortex structures and propeller wake velocity was explored. The results revealed that as the water depth H decreased from deep water to 5D, the influence on open water performance was minimal. However, as H decreased further from 5D to D, significant shallow-water effects emerged, characterized by increases in both the torque coefficient KQ and total thrust coefficient KT. Notably, the increase in KT was more pronounced than that of KQ, which resulted in an improvement in open water efficiency η0. Additionally, the vortex structures and wake velocity demonstrated that the bottom boundary exerted a minimal influence when H decreased from deep water to 2D, but its effect became pronounced when H=D. This study provided guidance for numerical simulation of ducted propellers in shallow-water environments and for propeller design and selection for shallow-water ships.

In this study, a comprehensive digital twin monitoring system for ship equipment was designed and implemented, including the system architecture, key technologies, and applications. Through data-driven models and operational monitoring system analysis, our PSO-SVM-based time series prediction method demonstrated excellent predictive capabilities for catamaran equipment, achieving efficient fault warnings using a threshold method. The digital twin model and virtual scenarios constructed here provide a visualisation and simulation platform for equipment status monitoring, enhanced fault diagnosis and support for maintenance decisions. The system integrates real-time monitoring, fault warning, and data analysis, and testing results show good stability and accuracy. In addition, the system optimises the user experience through multi-round feedback testing, and ensures data security and privacy protection through multi-layer encryption, identity verification, and role-based access control. A case study indicates that the proposed system effectively monitors equipment status and provides fault warnings, and has broad application prospects and practical value. Future work will focus on optimising the functionality and improving the applicability and security of the system.

The rise of digital twin technology has provided innovative methods for monitoring and optimizing ship cooling pumps. This paper proposes a digital twin-based framework for the status monitoring and visualization of ship cooling pumps. By establishing highly realistic physical and mathematical models and integrating actual operational data, a comprehensive virtual environment was created to simulate the operational status of ship cooling pumps. Using the random forest algorithm for data training and testing, the results showed that the root mean square error for the training set was 0.0037873, and for the test set, it was 0.008929, indicating high accuracy in predicting the status of cooling pumps. This system enables real-time monitoring, problem diagnosis, performance optimization, and decision support for cooling pumps. This study aims to leverage digital twin technology to design and apply a visualization monitoring system to enhance the intelligence of ship operation and maintenance.

When a ship sails in shallow water, it will show different hydrodynamic performance from that in deep water due to the limitations of water depth. The shallow water effect may lead to hull sinkage and trim, increasing the risk of bottoming or collision. In this study, a new design scheme of a shallow-water seismic survey vessel is proposed to solve the problems of traditional seismic survey vessels in shallow-water marine resources exploration and safety. The RANS (the Reynolds-Averaged Navier–Stokes) method combined with the Overset Mesh and DFBI (Dynamic Fluid Body Interaction) method is used for numerical simulation to analyze the influence of ship type, water depth, and speed on ship sinkage and trim, as well as the influence of the shallow-water ship’s attitude on resistance. The results show that with the decrease in water depth and the increase in speed, the pressure distribution around the hull becomes uneven, which leads to the aggravation of the sinkage and trim of the hull. In response to this problem, the shallow-water seismic survey vessel significantly improved the sinkage and trim of the hull in shallow water to ensure its safe navigation. The research also shows that navigation resistance can be effectively reduced by appropriately adjusting the ship’s attitude. Therefore, this study provides a reference for the development of shallow-water ships in the future.

Centrifugal pumps are susceptible to various faults, particularly under challenging conditions such as high pressure. Swift and accurate fault diagnosis is crucial for enhancing the reliability and safety of mechanical equipment. However, monitoring data under fault conditions in centrifugal pumps are limited. This study employed an experimental approach to gather original monitoring data (vibration signal data) across various fault types. We introduce a multi–scale sensing Convolutional Neural Network (MS–1D–CNN) model for diagnosing faults in centrifugal pumps. The network structure is further optimized by examining the impact of various hyperparameters on its performance. Subsequently, the model’s efficacy in diagnosing centrifugal pump faults has been comprehensively validated using experimental data. The results demonstrate that, under both single and multiple operating conditions, the model not only reduces reliance on manual intervention but also improves the accuracy of fault diagnosis.