With its “Flightpath 2050” strategy, the European Commission has outlined a framework for the aviation industry that aims to reduce emissions and fuel and energy consumption, requiring more efficient aircraft engines.
The ARIADNE project has created the basis for achieving the desired efficiency gains more quickly. The researchers have combined years of flow data on intermediate turbine ducts with AI and machine learning.
This developed a model that tests the impact of changes to a wide range of geometry parameters on efficiency much more quickly and efficiently.
Intermediate turbine ducts offer optimisation potential
“Intermediate turbine ducts are an essential component of aircraft engines,” explained project manager Wolfgang Sanz from the Institute of Thermal Turbomachinery and Machine Dynamics at TU Graz.
“They guide the flow between the high-pressure and low-pressure turbines, which run at different speeds. However, these intermediate ducts are quite heavy, which is why they need to be as short, small, and light as possible while still achieving high efficiency. There is still a lot of potential for optimisation here.”
Based on its own research in collaboration with renowned aircraft engine manufacturers, the institute has built up an extensive database of measurement data and flow simulations.
To utilise this reservoir of information to optimise components and entire engines, the team collaborated with Franz Wotawa’s research group at the Institute of Software Engineering and Artificial Intelligence at TU Graz, as well as with two corporate partners to pursue three different AI-supported approaches.
Reduced order models have the most potential to produce efficient aircraft engines
Reduced order models proved to be the most successful, as they search for similarities in the data and use only the most significant common features for simulation.
This leads to an enormous acceleration of the calculations, which run several orders of magnitude faster than a complete flow simulation. Although these models can entail some loss in accuracy, they allow for predicting trends and identifying optimisation potential by linking them to the simulation.
Another advantage of the independently developed model was its ability to quickly recognise changes in the performance of efficient aircraft engines when a parameter, such as the length of the transition duct, changes.
In contrast, surrogate models have certain limitations, as they primarily rely on interpolating existing data. Outside the validated flow data range, the results were inaccurate because the database was too small.
PINNs (Physics-Informed Neural Networks), which aim to integrate physical differential equations into neural networks, were also investigated in the project.
However, further development is still required before they can be used in practice to test more efficient aircraft models.
Next steps: Developing 3D simulations
The research team is already planning the next steps, as the reduced order model has so far only modelled the intermediate turbine ducts in two dimensions.
The extensive database on turbine ducts and the reduced order model developed in the project will be made available online to other research groups, allowing them to work on a three-dimensional simulation model of an aircraft engine.
For Sanz, however, working with machine learning has already opened up new approaches.
“From the results of the machine learning approaches, we were able to recognise dependencies and trends that we would never have thought of otherwise,” he concluded.