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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

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