Enterprise AI Analysis
Digital Twin System for Attitude and Stress of Machinery Equipment
During actual operation, ships may experience tilting or large-angle rolling due to the effects of wind and waves. The changes in attitude and key stress during navigation have consistently garnered significant attention. In response to the complex motions encountered during ship navigation, this paper focuses on a model ship as the research subject and constructs a digital twin system for marine vessel attitude. By analyzing the mechanisms, establishing a digital twin framework, conducting CFD simulations, and constructing a dataset, this study achieves mutual mapping between the physical ship and the virtual space, thereby building a digital twin system for the model ship's attitude. This enables real-time visualization of the model ship's attitude and stress data within the digital twin space.
Executive Impact & Strategic Value
Leveraging digital twin technology transforms maritime operations, offering unprecedented real-time insights and proactive management capabilities for vessel integrity and safety.
Operational Safety Improvement
Continuous Data Flow
Digital Twin Fidelity
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Comprehensive Digital Twin for Maritime Vessels
This research presents a robust framework for developing a digital twin system focused on monitoring the attitude and stress of machinery equipment in maritime vessels. By integrating physical models, sensor data, and operational history, it establishes a high-fidelity virtual representation of the ship. This approach addresses critical challenges in real-time monitoring and feedback for safe vessel operation, leveraging multi-disciplinary simulation processes to enhance maritime competitiveness.
Enterprise Process Flow: Digital Twin Construction
Physical Entity Data Acquisition
→
Digital Model Definition & Reconstruction
→
Twin Model Data Fusion & Mapping
→
Real-time Analysis & Visualization
Real-time
Attitude & Stress Data Monitoring and Visualization
| Feature | Simulation Data | Measured Data |
|---|---|---|
| Source | Computational Fluid Dynamics (CFD), preset conditions | Onboard Sensors (Gyroscopes, GPS, Pressure) |
| Purpose | Initial predictions, 'what-if' scenarios, design optimization | Real-time correction, calibration, operational monitoring |
| Accuracy & Fidelity | Theoretical/Ideal, relies on model assumptions | Real-world, high-confidence representation of actual state |
| Application | Preliminary analysis, behavior under controlled conditions | Live operational feedback, hazard warning, decision support |
Case Study: Model Ship Digital Twin Implementation
Scenario: Monitoring a model ship's attitude and travel route in real-time under actual operating conditions to assess hydrodynamic performance and detect potential hazards.
Approach: A digital twin system was constructed for a model ship, integrating both measured data from onboard sensors (gyroscopes, GPS) and simulation data (CFD analysis of wave resistance). The physical model ship was placed in Xinhai Lake, Dalian Maritime University, for real-world driving simulations.
Outcome: The system successfully enabled real-time visualization of ship posture changes, comprehensive hydrodynamic performance evaluation, and immediate hazard warning during operation. This validated the digital twin model's capability for accurate, live monitoring.
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Your AI Implementation Roadmap
A typical journey to integrate digital twin technology for real-time monitoring and predictive maintenance.
01. Feasibility Study & Requirements Gathering
Assess existing infrastructure, define project scope, identify key data points, and establish performance metrics for the digital twin system.
02. Digital Model & Sensor Integration
Develop high-fidelity 3D models of machinery, integrate sensors for real-time data acquisition (attitude, stress), and establish communication protocols.
03. Data Pipeline & Twin Development
Design and implement data ingestion pipelines, develop the digital twin logic for real-time mapping, and set up simulation and analytical models.
04. Validation, Deployment & Training
Thoroughly test the digital twin against physical systems, deploy the solution, and provide comprehensive training to operators and maintenance teams.
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