AI-Driven Turbine Duct Optimization Promises Emission Cuts and Fuel Savings for Future Aircraft Engines

November 28, 2025
AI-Driven Turbine Duct Optimization Promises Emission Cuts and Fuel Savings for Future Aircraft Engines
  • In the ARIADNE project, TU Graz combines long-running flow data with AI and machine learning to speed up design optimization of the ducts and enhance overall engine efficiency.

  • Reduced order modelling emerged as the most effective method, delivering massive speedups by focusing on the most influential features, with acceptable accuracy for design exploration.

  • These reduced order models identify data similarities and rely on only the most relevant features, enabling rapid trend prediction and easier identification of optimization opportunities.

  • Surrogate models struggle outside the validated data range due to interpolation limits, while Physics-Informed Neural Networks (PINNs) still require further development before practical use.

  • The current model handles two-dimensional duct simulations, with plans to extend to three dimensions by using TU Graz’s turbine-duct database and sharing it publicly to foster collaboration.

  • Next steps include extending to three-dimensional simulations and continuing industry collaborations to validate and refine the AI-driven optimization framework.

  • Machine learning revealed dependencies and trends that traditional methods miss, signaling a shift toward more efficient duct and engine design.

  • The team built a large database of flow data and simulations from collaborations with engine manufacturers and tested three AI approaches, with reduced order modeling delivering the strongest performance.

  • Three AI approaches explored—reduced order modeling, surrogate modeling, and PINNs—with reduced order modeling delivering the fastest speedups for analyses across varying geometry parameters.

  • The work is published in the Turbomachinery section of ASME GT2025, with a DOI provided for reference.

  • Aviation emissions remain a driver for AI-enabled optimization, aligning with Flightpath 2050’s goal to decarbonize air travel as efficiency gains in engines are pursued.

  • Researchers at Graz University of Technology are advancing the EU’s Flightpath 2050 by using AI-assisted analysis to optimize intermediate turbine ducts, aiming to cut emissions and fuel use in aircraft engines.

Summary based on 3 sources


Get a daily email with more AI stories

More Stories