PROJECT

Digital Thread for Design℠ Applications

Digital Thread for Design℠ Applications

Digital Thread for Design℠ (or DT4D℠ for short) is GE's internal capability for rapidly creating A.I. surrogate models from engineering workflows, while socializing & threading the data associated with the models.   

This project is focused on the application of the DT4D℠ Capability to a wide array of DMS cases with relevance to GE, including:

  • Combustor Operability
  • Turbomachinery Aerodynamics
  • Investment Casting
  • Metal 3D Printing
  • System Thermal Management
  • Motor Electro-Magnetics
  • Aerodynamic / Flow Control Systems

In a recently published Forbes article on DT4D℠referring to the use of surrogate modeling and the scale up of digital thread for product design, Prof. Karthik Duraisamy from the University of Michigan said the following:

"Surrogate modeling has been done on a smaller scale for some time [..] but the scale of what GE appears to have achieved is impressive. [..] GE is among the industry leaders, if not the leader, in this area.”

Project Impact

The extreme speed advantage of the A.I. surrogate models enable several classes of broadly applicable engineering outcomes that otherwise could not be achieved.  These outcomes include:

• INCREASED ENGINEERING PRODUCTIVITY
• IMPROVED DESIGN QUALITY & ROBUSTNESS (Example #1)
• FASTER SIMULATION DATA MATCHING
• LOWER OVERALL COST OF SOLUTIONS
• PREVIOUSLY-IMPROBABLE TECHNOLOGIES (Example #2)


Additionally, the concurrent DT4D℠ development and application to real-world problems helps to harden, strengthen, broaden, and expedite the ecosystem as a product.
 

Diesel Piston Bowl Geometry Optimization


 

Turbine Airfoil Temperature Predictions

 

  • Our Expertise

    Capabilities utilized for Digital Thread for Design℠ Applications project

  • High Performance Computing

    World-Class Modeling and Simulation Based Engineering Services

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  • Digital Thread for Design

    Revolutionizing product engineering by harnessing workflow automation, data management and machine learning to expedite and differentiate engineering outcomes

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  • Advanced Modeling & Simulation

    Developing advanced simulation techniques and models to enable design by numerical analysis

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  • Machine Learning

    Developing and scaling machine learning solutions for industrial applications to allow for continuous learning, adapting and improving in dynamic operating environments

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