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Email

fang.y5@unimelb.edu.au

Credentials


Position
Research Fellow,data-Driven Turbulence Modelling
Department of Mechanical Engineering
ORCID

0000-0002-7010-4740

Dr Yuan Fang

Research Fellow,data-Driven Turbulence Modelling
Department of Mechanical Engineering

13 Scholarly works
0 Projects

HIGHLIGHTS

  • 2026

    Journal article

    Enhancing One-Equation Turbulence Models for Delta Wings by Gene Expression Programming
    DOI: 10.2514/1.j065906
  • 2026

    Journal article

    Accelerating CFD-driven training of transition and turbulence models for turbine flows by one-shot and real-time transformer integration
    DOI: 10.1016/j.compfluid.2025.106927
  • 2026

    Journal article

    The Impact of Transition and Turbulence Modeling on the SPLEEN High-Speed Low-Pressure Turbine Cascade
    DOI: 10.1115/1.4069487
  • 2025

    Journal article

    Symbolic turbulence model development for complex-geometry flows exploiting language model-based transfer learning
    DOI: 10.1063/5.0278635
  • 2025

    Journal article

    A novel data-driven method for augmenting turbulence modeling for unsteady cavitating flows
    DOI: 10.1016/j.ijheatfluidflow.2025.109847
  • 2025

    Journal article

    Constraining genetic symbolic regression via semantic backpropagation
    DOI: 10.1007/s10710-025-09510-z
  • 2025

    Journal article

    A Reformulation of the Laminar Kinetic Energy Model to Enable Multi-mode Transition Predictions
    DOI: 10.1007/s10494-024-00590-y
Yuan Fang

RECENT SCHOLARLY WORKS

  • 2025

    Conference Proceedings

    THE IMPACT OF TRANSITION AND TURBULENCE MODELLING ON THE SPLEEN HIGH-SPEED LOW-PRESSURE TURBINE CASCADE
    DOI: 10.1115/GT2025-153288
  • 2024

    Journal article

    A Data-Driven Approach for Generalizing the Laminar Kinetic Energy Model for Separation and Bypass Transition in Low- and High-Pressure Turbines
    DOI: 10.1115/1.4065124
  • 2024

    Journal article

    Strategies for Enhancing One-Equation Turbulence Model Predictions Using Gene-Expression Programming
    DOI: 10.3390/fluids9080191

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