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Course Outline
Overview of Digital Twin Technology
- Foundational concepts and historical development of digital twin frameworks
- Strategic applications within manufacturing, energy sectors, and supply chain logistics for government operations
- Architectural components and lifecycle management of digital twins
System Modeling and Simulation Methodologies
- Implementation of dynamic system models using Simulink
- Comparison of physics-based and data-driven modeling approaches
- Utilization of Unity for advanced system visualization
Real-Time Data Integration Frameworks
- Implementation of MQTT and OPC-UA protocols for secure connectivity
- Management of streaming data via Node-RED
- Ingestion of sensor and machine telemetry into the twin environment
Artificial Intelligence and Machine Learning Integration
- Deployment of AI models to enhance predictive analytics and operational optimization for government initiatives
- Application of TensorFlow or PyTorch frameworks with live data feeds
- Model training utilizing simulation-generated outputs
Visualization and Operational Dashboards
- Design principles for user interfaces supporting twin monitoring
- Selection between 3D and 2D visualization technologies
- Development of custom dashboards delivering real-time operational insights
Case Study: Development of a Digital Twin Prototype
- Comprehensive design of a manufacturing asset twin
- Configuration of data integration and machine learning components
- Deployment and validation within a simulated environment for government testing standards
Maintenance and Scalability of Digital Twin Systems
- Lifecycle governance and version control updates
- Ensuring interoperability through adherence to industry standards
- Expansion strategies for multi-asset or enterprise-wide processes for government scale
Summary and Strategic Next Steps
Requirements
- Knowledge of system modeling or industrial operations
- Practical experience with Python or analogous programming languages
- Familiarity with data integration principles
Audience
- Digital transformation executives
- Plant IT staff
- Data architects
21 Hours