Digital Twin AeroEngine

Aims & Scope

This session explores cutting-edge digital twin methodologies for aero-engine systems, emphasizing theoretical advancements, practical applications, and future directions. It highlights strategies for improving reliability, predictive maintenance, lifecycle management, and noise reduction through digital twin technologies.                                

 

The session will focus on the following points

• Digital twins for aero-engine health monitoring
• AI-driven reliability modeling and uncertainty quantification
• Data-model integration for real-time reliability prediction
• Lifecycle management and prognostics
• Physics-informed AI methods
• Aeroacoustic modeling and noise prediction
• Pipe flow noise analysis and mitigation
• Case studies and industrial applications

Session Chairs

Presentations

  • Professor
    University of Chinese Academy of Sciences
    Title: To be confirmed
    Abstract

    To be confirmed

  • Professor
    Northwestern Polytechnical University
    Title: To be confirmed
    Abstract

    To be confirmed

  • Associate Professor
    Tsinghua University
    Title: Digital Transformation in Aeroengine Operation and Maintenance
    Abstract This speech will focus on the digital transformation trends in the field of aeroengine operation and maintenance, and deeply explore how digital technologies are reshaping the full-life-cycle management mode of engines.
  • Professor
    University of Science and Technology Beijing
    Title: Bayesian inversion for parameter calibration in presence of mixed uncertainty
    Abstract

    To be confirmed

  • Assistant Professor
    Shanghai Jiaotong University
    Title: A Digital-Twin approach for evaluation of the manufactured imperfection on the fatigue life
    Abstract

    To be confirmed

  • Research Assistant Professor
    Beihang University
    Title: Data-driven modeling method for Aero-engine vibration under complex loads and variable conditions
    Abstract

    To be confirmed