Digital Twin System Using CFD and AI/ML

This diagram illustrates the complete lifecycle of a digital twin system, showing how CFD (Computational Fluid Dynamics) and AI/ML play crucial roles at different stages.

Key Stages

1. Design:
   • CFD plays a critical role at this stage
   • Establishes the foundation through geometric modeling, physical property definition, and boundary condition setup
   • Accurate physical simulation at this stage forms the basis for future predictions
2. Build:
   • Implementation stage for the designed model
   • Integration of both CFD models and AI/ML models
3. Operate:
   • AI/ML plays a critical role at this stage
   • System performance prediction and optimization based on real-time data
   • Continuous model improvement by learning from operational data

Technology Integration Process

• CFD Track:
  • Provides accurate physical modeling during the design phase
  • Defines geometry, physics, and boundary conditions to establish the basic structure
  • Verifies model accuracy through validation processes
  • Updates the model according to changes during operation
• AI/ML Track:
  • Configures learning data and defines metrics
  • Sets up data lists and resolution
  • Provides predictive models using real-time data during the operation phase
  • Continuously improves prediction accuracy by learning from operational data

Cyclical Improvement System

The key to this system is that physical modeling (CFD) at the design stage and data-driven prediction (AI/ML) at the operation stage work complementarily to form a continuous improvement cycle. Real data collected during operation is used to update the AI/ML models, which in turn contributes to improving the accuracy of the CFD models.

With Claude